#!/usr/bin/perl
# $Id$

=head1 NAME

BioPerlTutorial 1.2.2 - a tutorial for bioperl

=head1 AUTHOR

  Written by Peter Schattner <schattner@alum.mit.edu>

  Copyright Peter Schattner

  Contributions, additions and corrections have been made
  to this document by the following individuals:

  Jason Stajich
  Heikki Lehvaslaiho
  Brian Osborne
  Hilmar Lapp
  Chris Dagdigian
  Elia Stupka
  Ewan Birney

=head1 DESCRIPTION

   This tutorial includes "snippets" of code and text from various
   Bioperl documents including module documentation, example scripts
   and "t" test scripts. You may distribute this tutorial under the
   same terms as perl itself.

   This document is written in Perl POD (plain old documentation)
   format.  You can run this file through your favorite pod translator
   (pod2html, pod2man, pod2text, etc.) if you would like a more
   convenient formatting.

  Table of Contents

  I. Introduction
  I.1 Overview
  I.2 Quick getting started scripts
  I.3 Software requirements
    I.3.1 For minimal bioperl installation
    I.3.2 For complete installation
  I.4 Installation procedures
  I.5 Additional comments for non-unix users
  I.6 Places to look for additional documentation

  II. Brief overview to bioperl's objects
  II.1 Sequence objects:
         (Seq, PrimarySeq, LocatableSeq, RelSegment, LiveSeq, 
          LargeSeq, RichSeq, SeqWithQuality, SeqI)
  II.2  Location objects (Simple, Split, Fuzzy)
  II.3  Interface objects and implementation objects

  III. Using bioperl
  III.1 Accessing sequence data from local and remote databases
     III.1.1 Accessing remote databases (Bio::DB::GenBank, etc)
     III.1.2 Indexing and accessing local databases 
             (Bio::Index::*,  bpindex.pl, bpfetch.pl)
  III.2 Transforming formats of database/ file records
     III.2.1 Transforming sequence files (SeqIO)
     III.2.2 Transforming alignment files (AlignIO)
  III.3 Manipulating sequences
    III.3.1 Manipulating sequence data with Seq methods (Seq)
    III.3.2 Obtaining basic sequence statistics- MW, residue & 
            codon frequencies (SeqStats)
    III.3.3 Identifying restriction enzyme sites (RestrictionEnzyme)
    III.3.4 Identifying amino acid cleavage sites (Sigcleave)
    III.3.5 Miscellaneous sequence utilities: OddCodes, SeqPattern
    III.3.6 Converting coordinate systems (Coordinate::Pair, RelSegment)
  III.4 Searching for "similar" sequences
     III.4.1 Running BLAST remotely (using RemoteBlast.pm)
     III.4.2 Parsing BLAST and FASTA reports with Search and SearchIO
     III.4.3 Parsing BLAST reports with BPlite, BPpsilite, and BPbl2seq
     III.4.4 Parsing HMM reports (HMMER::Results, SearchIO)
     III.4.5 Running BLAST locally  (StandAloneBlast)
  III.5 Manipulating sequence alignments (SimpleAlign)
  III.6 Searching for genes and other structures on genomic DNA
           (Genscan, Sim4, ESTScan, MZEF, Grail, Genemark, EPCR)
  III.7 Developing machine readable sequence annotations
     III.7.1 Representing sequence annotations 
           (Annotation::Collection, SeqFeature, RichSeq)
     III.7.2 Representing and large and/or changing sequences 
           (LiveSeq,LargeSeq)
     III.7.3 Representing related sequences - mutations, polymorphisms etc 
           (Allele, SeqDiff)
     III.7.4 Incorpotating quality data in sequence annotation (SeqWithQuality)
     III.7.5 Sequence XML representations - generation and parsing (SeqIO::game)
     III.7.6 Representing Sequence Features using GFF (Bio:Tools:GFF )
  III.8 Manipulating clusters of sequences (Cluster, ClusterIO)
  III.9 Representing non-sequence data in Bioperl: structures, trees, 
             maps, graphics and bibliographic text
     III.9.1 Using 3D structure objects and reading PDB files (StructureI,
             Structure::IO)
     III.9.2 Tree objects and phylogenetic trees (Tree::Tree, TreeIO, PAML.pm )
     III.9.3 Map objects for manipulating genetic maps (Map::MapI, MapIO)
     III.9.4 Bibliographic objects for querying bibliographic databases (Biblio)
     III.9.5 Graphics objects for representing sequence objects as 
             images (Graphics)
  III.10 Bioperl alphabets
     III.10.1 Extended DNA / RNA alphabet
     III.10.2 Amino Acid alphabet

  IV.  Auxilliary Bioperl Libraries (Bioperl-run, Bioperl-db, etc.)
     IV.1 Using the Bioperl Auxilliary Libraries
     IV.2 Running programs (Bioperl-run and Bioperl-ext)
         IV.2.1 Sequence manipulation using the Bioperl EMBOSS and PISE interfaces
         IV.2.2 Aligning 2 sequences with Blast using  bl2seq and AlignIO
         IV.2.3 Aligning multiple sequences (Clustalw.pm, TCoffee.pm)
         IV.2.4 Aligning 2 sequences with Smith-Waterman (pSW)
     IV.3 Bioperl-db
     IV.4 Other Bioperl auxilliary libraries

  V.  Appendices
     V.1 Finding out which methods are used by which Bioperl Objects
     V.2 Tutorial Demo Scripts

=head1 I. Introduction

=head2  I.1 Overview

Bioperl is a collection of perl modules that facilitate the
development of perl scripts for bioinformatics applications.  As
such, it does not include ready to use programs in the sense that many
commercial packages and free web-based interfaces (eg Entrez, SRS) do.
On the other hand, bioperl does provide reusable perl modules that
facilitate writing perl scripts for sequence manipulation, accessing
of databases using a range of data formats and execution and parsing
of the results of various molecular biology programs including Blast,
clustalw, TCoffee, genscan, ESTscan and HMMER.  Consequently, bioperl
enables developing scripts that can analyze large quantities of
sequence data in ways that are typically difficult or impossible with
web based systems.

In order to take advantage of bioperl, the user needs a basic
understanding of the perl programming language including an
understanding of how to use perl references, modules, objects and
methods. If these concepts are unfamiliar the user is referred to any
of the various introductory / intermediate books on perl. We've liked
S. Holzmer's Perl Core Language, Coriolis Technology Press, for
example.  This tutorial is not intended to teach the fundamentals of
perl to those with little or no experience in the perl language.  On
the other hand, advanced knowledge of perl - such as how to write a
perl object - is not required for successfully using bioperl.

Bioperl is open source software that is still under active
development.  The advantages of open source software are well known.
They include the ability to freely examine and modify source code and
exemption from software licensing fees.  However, since open source
software is typically developed by a large number of volunteer
programmers, the resulting code is often not as clearly organized and
its user interface not as standardized as in a mature commercial
product.  In addition, in any project under active development,
documentation may not keep up with the development of new features.
Consequently the learning curve for actively developed, open source
source software is sometimes steep.

This tutorial is intended to ease the learning curve for new users of
bioperl.  To that end the tutorial includes:

=over 4

=item *

Descriptions of what bioinformatics tasks can be handled with bioperl

=item *

Directions on where to find the methods to accomplish these tasks
within the bioperl package

=item *

Recommendations on where to go for additional information.

=item *

A runnable script, bptutorial.pl, which demonstrates many of the
capabilities of Bioperl. Runnable example code can also be found in
the scripts/ and examples/ directories. Summary descriptions of all
of these scripts can be found in the file bioscripts.pod.  In
addition, the POD documentation for many Bioperl modules should contain
runnable code in the SYNOPSIS section which is meant to illustrate the
use of a module and its methods.

=back

Running the bptutorial.pl script while going through this tutorial - or
better yet, stepping through it with an interactive debugger - is a
good way of learning bioperl.  The tutorial script is also a good
place from which to cut-and-paste code for your scripts (rather than
using the code snippets in this tutorial). Most of the scripts in the
tutorial script should work on your machine - and if they don't it would
probably be a good idea to find out why, before getting too involved
with bioperl! (Some of the demos require optional modules from the
bioperl auxilliary libraries and/or external programs.  These demos
should be skipped if the demos are run and the required auxilliary
programs are not found.)

=head2 I.2 Quick getting started scripts

For newcomers and people who want to quickly evaluate whether this package
is worth using in the first place, we have a very simple module which allows
easy access to a small number of Bioperl's functionality in an easy to use 
manner. The Bio::Perl module provides some simple access functions,
for example, this script will retrieve a swissprot sequence and write
it out in fasta format

  use Bio::Perl;

  # this script will only work with an internet connection
  # on the computer it is run on
  $seq_object = get_sequence('swissprot',"ROA1_HUMAN");

  write_sequence(">roa1.fasta",'fasta',$seq_object);

Another example is the ability to blast a sequence using the facilities
as NCBI. (Please be careful not to abuse the compute that NCBI provides
and so use this only for individual searches. If you want to do a large
number of BLAST searches, please download the blast package locally).

  use Bio::Perl;

  # this script will only work with an internet connection
  # on the computer it is run on

  $seq_object = get_sequence('swissprot',"ROA1_HUMAN");

  # uses the default database - nr in this case
  $blast_result = blast_sequence($seq);

  write_blast(">roa1.blast",$blast_report);

Bio::Perl has a number of other "easy to use" functions, including

  get_sequence        - gets a sequence from standard, internet accessible
                        databases
  read_sequence       - reads a sequence from a file
  read_all_sequences  - reads all sequences from a file 
  new_sequence        - makes a bioperl sequence just from a string
  write_sequence      - writes a single or an array of sequence to a file
  translate           - provides a translation of a sequence
  translate_as_string - provides a translation of a sequence, returning back 
                        just the sequence as a string
  blast_sequence      - BLASTs a sequence against standard databases at  
                        NCBI
  write_blast         - writes a blast report out to a file

Using the Bio::Perl.pm module, it is possible to manipulate sequence
data in Bioperl without explicitly creating Seq or SeqIO objects 
described later in this tutorial. However, only limited
data manipulation is supported in this mode.

Look at the documentation in L<Bio::Perl> by going 'perldoc Bio::Perl' to
learn more about these functions. In all these cases, Bio::Perl
accesses a subset of the underlying Bioperl functions (for example,
translation in Bioperl can handle many different translation tables
and provides different options for stop codon processing) - in most
cases, most users will migrate to using the underlying bioperl objects
as their sophistication level increases, but Bio::Perl provides an
easy on-ramp for newcomers and lazy programmers. Also see examples/bioperl.pl
for more examples of usage of this module.

=head2 I.3 Software requirements

What's required to run bioperl.

=head2 I.3.1 Minimal bioperl installation (Bioperl "core" installation)

For a "minimal" installation of bioperl, you will need to have perl
itself installed as well as the bioperl "core modules".  Bioperl has
been tested primarily using perl 5.005, 5.6, and 5.8.
The minimal bioperl installation should still work under perl 5.004.
However, as increasing numbers of bioperl objects are using modules
from CPAN (see below), problems have been observed for bioperl running
under perl 5.004.  So if you are having trouble running bioperl under
perl 5.004, you should probably upgrade your version of perl.

In addition to a current version of perl, the new user of bioperl is
encouraged to have access to, and familiarity with, an interactive
perl debugger.  Bioperl is a large collection of complex interacting
software objects.  Stepping through a script with an interactive
debugger is a very helpful way of seeing what is happening in such a
complex software system - especially when the software is not behaving
in the way that you expect.  The free graphical debugger ptkdb
(available as Devel::ptkdb from CPAN) is highly recommended.  The
standard perl distribution also contains a powerful interactive
debugger - though with a more cumbersome command-line interface (use
it like "perl -d <script>"). The Perl tool Data::Dumper used with the 
syntax:

  use Data::Dumper;
  print Dumper($seqobj);

can also be helpful for obtaining debugging information on perl objects.

=head2 I.3.2 Complete installation

Some of the capabilities of bioperl require software beyond that of
the minimal installation.  This additional software includes perl
modules from CPAN, package-libraries from bioperl's auxilliary
code-repositories, a bioperl xs-extension, and several standard
compiled bioinformatics programs.

B<Perl - extensions>

For a complete listing of external Perl modules required by bioperl
please see the INSTALL file in the Bioperl package.

B<Bioperl auxilliary repositories>

Some features of bioperl that require modules from bioperl's
auxilliary code repositories. See section IV and references therein
for further installation instructions for these modules.

B<Bioperl C extensions & external bioinformatics programs>

Bioperl also uses several C programs for sequence alignment and local
blast searching. To use these features of bioperl you will need an
ANSI C or Gnu C compiler as well as the actual program available from
sources such as:

for Smith-Waterman alignments- bioperl-ext-0.6 from
http://bioperl.org/Core/external.shtml

for clustalw alignments-
ftp://ftp.ebi.ac.uk/pub/software/unix/clustalw/
ftp://ftp-igbmc.u-strasbg.fr/pub/ClustalW/

for tcoffee alignments-
http://igs-server.cnrs-mrs.fr/~cnotred/Projects_home_page/t_coffee_home_page.html

for local blast searching- ftp://ftp.ncbi.nlm.nih.gov/blast/server/current_release/

for EMBOSS applications - http://www.hgmp.mrc.ac.uk/Software/EMBOSS/download.html

=for html <A NAME ="i.3"></A>

=head2  I.4 Installation

The actual installation of the various system components is
accomplished in the standard manner:

=over 6

=item *

Locate the package on the network

=item *

Download

=item *

Decompress (with gunzip or a similiar utility)

=item *

Remove the file archive (eg with tar -xvf)

=item *

Create a "makefile" with "perl Makefile.PL" for perl modules or a
supplied "install" or "configure" program for non-perl programs

=item *

Run "make", "make test" and "make install". This procedure must be
repeated for every CPAN module, bioperl-extension and external
module to be installed. A helper module CPAN.pm is available from
CPAN which automates the process for installing the perl modules.

The CPAN module can also be used to install all of the modules
listed above in a single step as a "bundle" of modules,
Bundle::BioPerl, eg

  $>perl -MCPAN -e shell
  cpan>install Bundle::BioPerl
  <installation details....>
  cpan>install B/BI/BIRNEY/bioperl-1.0.1.tar.gz 
  <installation details....>
  cpan>quit

Be advised that version numbers change regularly, so the number used
above may not apply. A disadvantage of the "bundle" approach is that
if there's a problem installing any individual module it may be a bit
more difficult to isolate.

See the package's INSTALL file for more details.

=back

For the external programs (clustal, Tcoffee, ncbi-blast), there is an
extra step:

=over 1

=item *

Set the relevant environmental variable (CLUSTALDIR, TCOFFEEDIR or
BLASTDIR) to the directory holding the executable in your startup
file - eg in .bashrc or .tcshrc. For running local blasts, it is also
necessary that the name of local-blast database directory is known
to bioperl.  This will typically happen automatically, but in case
of difficulty, refer to the documentation in
L<Bio::Tools::Run::StandAloneBlast>.

=back

The only likely complication (at least on unix systems) that may occur
is if you are unable to obtain system level writing privileges.  For
instructions on modifying the installation in this case and for more
details on the overall installation procedure, see the INSTALL file in
the bioperl distribution as well as the README files in the external
programs you want to use (eg bioperl-ext, clustalw, TCoffee,
NCBI-blast).

=head2 I.5 Additional comments for non-unix users

Bioperl has mainly been developed and tested under various unix
environments (including Linux and MacOS X).  In addition, this tutorial
has been written largely from a Unix perspective.

Mac users may find Steve Cannon's installation notes and suggestions
for Bioperl on OS X at
http://www.tc.umn.edu/~cann0010/Bioperl_OSX_install.html helpful. Also
Todd Richmond has written of his experiences with BioPerl on MacOS 9
at http://bioperl.org/Core/mac-bioperl.html.

The bioperl core has also been tested and should work under most
versions of Microsoft Windows.  For many windows users the perl and
bioperl distributions from Active State, at http://www.activestate.com
has been quite helpful.  Other windows users have had success running
bioperl under Cygwin (http://www.cygwin.com). See the package's 
INSTALL.WIN file for more details.

Many bioperl features require the use of CPAN modules, compiled
extensions or external programs.  These features probably will
not work under some or all of these other operating systems.  If a
script attempts to access these features from a non-unix OS, bioperl
is designed to simply report that the desired capability is not
available.  However, since the testing of bioperl in these
environments has been limited, the script may well crash in a less
"graceful" manner.

=head2 I.6 Places to look for additional documentation

This tutorial does not intend to be a comprehensive description of all
the objects and methods available in bioperl.  For that the reader is
directed to the documentation included with each of the modules. A
very useful interface for finding one's way within all the module
documentation can be found at http://doc.bioperl.org/bioperl-live/.
This interface lists all bioperl modules and descriptions of all
of their methods. In addition, beginner questions can often be
answered by looking at the FAQ, INSTALL and README files in the
top-level directory of the bioperl distribution.

One potential problem in locating the correct documentation is that
multiple methods in different modules may all share the same name.
Moreover, because of perl's complex method of inheritance
it is not often clear which of the identically named methods is being
called by a given object. One way to resolve this question is by using
the software described in Appendix L<"V.1">.

For those who prefer more visual descriptions,
http://bioperl.org/Core/Latest/modules.html also offers links to
PDF files which contain schematics that describe how many of the bioperl
objects related to one another.

In addition, a bioperl online course is available on the web at
http://www.pasteur.fr/recherche/unites/sis/formation/bioperl. The
user is also referred to numerous bioperl scripts in the scripts/
and examples/ directories (see bioscripts.pod for a description of 
these scripts).


=head1 II. Brief introduction to bioperl's objects

The purpose of this tutorial is to get you using bioperl to solve
real-life bioinformatics problems as quickly as possible.  The aim is
not to explain the structure of bioperl objects or perl
object-oriented programming in general.  Indeed, the relationships
among the bioperl objects is not simple; however, understanding them
in detail is fortunately not necessary for successfully using the
package.

Nevertheless, a little familiarity with the bioperl object "bestiary"
can be very helpful even to the casual user of bioperl. For example
there are (at least) eight different "sequence objects" - Seq,
PrimarySeq, LocatableSeq, RelSegment, LiveSeq, LargeSeq, SeqI, and
SeqWithQuality.  Understanding the relationships among these objects -
and why there are so many of them - will help you select the
appropriate one to use in your script.

=for html <A NAME ="ii.1"></A>

=head2 II.1 Sequence objects 

Seq is the central sequence object in bioperl.  When in doubt this is
probably the object that you want to use to describe a DNA, RNA or
protein sequence in bioperl.  Most common sequence manipulations can
be performed with Seq.  These capabilities are described in sections
L<"III.3.1"> and L<"III.7.1">, or in L<Bio::Seq>.

Seq objects can be created explicitly (see section L<"III.2.1"> for an
example).  However usually Seq objects will be created for you
automatically when you read in a file containing sequence data using
the SeqIO object.  This procedure is described in section L<"III.2.1">.
In addition to storing its identification labels and the sequence itself,
a Seq object can store multiple annotations and associated "sequence
features", such as those contained in most Genbank and EMBL sequence files.
This capability can be very useful - especially in development of
automated genome annotation systems, see section L<"III.7.1">.

On the other hand, if you need a script capable of simultaneously
handling hundreds or thousands sequences at a time, then the
overhead of adding annotations to each sequence can be significant.
For such applications, you will want to use the PrimarySeq
object. PrimarySeq is basically a "stripped down" version of Seq.
It contains just the sequence data itself and a few identifying labels
(id, accession number, alphabet = dna, rna, or protein), and no features.
For applications with hundreds or thousands or sequences, using PrimarySeq
objects can significantly speed up program execution and decrease the
amount of RAM the program requires. See L<Bio::PrimarySeq> for more
details.

RichSeq objects store additional annotations beyond those used by
standard Seq objects.  If you are using sources with very rich
sequence annotation, you may want to consider using these objects
which are described in section L<"III.7.1">. SeqWithQuality objects are
used to manipulate sequences with quality data, like those produced by
phred.  These objects are described in section L<"III.7.4">,
L<Bio::Seq::RichSeqI>, and in L<Bio::Seq::SeqWithQuality>.

What is called a LocatableSeq object for historical reasons
might be more appropriately called an "AlignedSeq" object.  It is a Seq
object which is part of a multiple sequence alignment.  It has "start" and
"end" positions indicating from where in a larger sequence it may have
been extracted.  It also may have "gap" symbols corresponding to the
alignment to which it belongs.  It is used by the alignment
object SimpleAlign and other modules that use SimpleAlign objects
(eg AlignIO.pm, pSW.pm).  In general you don't have to
worry about creating LocatableSeq objects because they will be made for
you automatically when you create an alignment (using pSW, Clustalw,
Tcoffee or bl2seq) or when you input an alignment data file using AlignIO.
However if you need to input a sequence alignment by hand (eg to build
a SimpleAlign object), you will need to input the sequences as
LocatableSeqs. Other sources of information include L<Bio::LocatableSeq>,
L<Bio::SimpleAlign>, L<Bio::AlignIO>, and L<Bio::Tools::pSW>.

The RelSegment object is also a type of bioperl Seq object. RelSegment
objects are useful when you want to be able to manipulate the origin
of the genomic coordinate system.  This situation may occur when
looking at a sub-sequence (e.g. an exon) which is located on a longer
underlying underlying sequence such as a chromosome or a contig. Such
manipulations may be important, for example when designing a graphical
genome browser. If your code may need such a capability, look at the
documentation L<Bio::DB::GFF::RelSegment> which describes this feature
in detail.

A LargeSeq object is a special type of Seq object used for
handling very long sequences (eg E<gt> 100 MB).  If you need to
manipulate such long sequences see section L<"III.7.2"> which describes
LargeSeq objects, or L<Bio::Seq::LargeSeq>.

A LiveSeq object is another specialized object for storing
sequence data. LiveSeq addresses the problem of features whose location
on a sequence changes over time.  This can happen, for example, when
sequence feature objects are used to store gene locations on newly
sequenced genomes - locations which can change as higher quality
sequencing data becomes available.  Although a LiveSeq object is not
implemented in the same way as a Seq object, LiveSeq does implement
the SeqI interface (see below). Consequently, most methods available
for Seq objects will work fine with LiveSeq objects. Section L<"III.7.2">
and L<Bio::LiveSeq> contain further discussion of LiveSeq objects.

SeqI objects are Seq "interface objects" (see section L<"II.4"> and
L<Bio::SeqI>). They are used to ensure bioperl's compatibility with
other software packages. SeqI and other interface objects are not
likely to be relevant to the casual bioperl user.

Having described these other types of sequence objects, the
"bottom line" still is that if you store your sequence data in Seq
objects (which is where they'll be if you read them in with
SeqIO), you will usually do just fine. ***

=for html <A NAME ="ii.2"></A>

=head2 II.2 Location objects

A Location object is designed to be associated with a Sequence
Feature object to indicate where on a larger structure (eg a chromosome or
contig) the feature can be found.  The reason why this simple concept has
evolved into a collection of rather complicated objects is that:

1) Some objects have multiple locations or sub-locations (e.g. a gene's exons
may have multiple start and stop locations)
2) In unfinished genomes, the precise locations of features is not known with
certainty.

Bioperl's various Location objects address these complications.  In addition
there are "CoordinatePolicy" objects that allow the user to specify how to
measure the "length" of a feature if its precise start and end coordinates are
not know. In most cases, you will not need to worry about these complications
if you are using bioperl to handle simple features with well-defined start
and stop locations.  However, if you are using bioperl to annotate partially
or unfinished genomes or to read annotations of such genomes with bioperl,
understanding the various Location objects will be important.  See the
documentation of the various modules in the Bio::Locations directory or
L<Bio::Location::CoordinatePolicyI> for more information.

=for html <A NAME ="ii.4"></A>

=head2 II.4 Interface objects and implementation objects

One goal of the design of Bioperl is to separate interface and
implementation objects.  An interface is solely the definition of what
methods one can call on an object, without any knowledge of how it is
implemented. An implementation is an actual, working implementation of
an object. In languages like Java, interface definition is part of the
language. In Perl, you have to roll your own.

In bioperl, the interface objects usually have names like
Bio::MyObjectI, with the trailing I indicating it is an interface
object. The interface objects mainly provide documentation on what the
interface is, and how to use it, without any implementations (though
there are some exceptions).  Although interface objects are not of
much direct utility to the casual bioperl user, being aware of their
existence is useful since they are the basis to understanding how
bioperl programs can communicate with other bioinformatics projects
and computer languages such as Ensembl and biopython and biojava.

For more discussion of design and development issues please see the
biodesign.pod file.

=head1 III. Using bioperl


Bioperl provides software modules for many of the typical tasks of
bioinformatics programming.  These include:

=over 7

=item * Accessing sequence data from local and remote databases

=item * Transforming formats of database/ file records

=item * Manipulating individual sequences

=item * Searching for "similar" sequences

=item * Creating and manipulating sequence alignments

=item * Searching for genes and other structures on genomic DNA

=item * Developing machine readable sequence annotations

=back

The following sections describe how bioperl can help perform all of
these tasks.

=head2 III.1 Accessing sequence data from local and remote databases

Much of bioperl is focused on sequence manipulation.  However, before
bioperl can manipulate sequences, it needs to have access to sequence
data.  Now one can directly enter data sequence data into a bioperl
Seq object, eg:

  $seq = Bio::Seq->new('-seq'              =>'actgtggcgtcaact',
                       '-desc'             =>'Sample Bio::Seq object',
                       '-display_id'       =>'something',
                       '-accession_number' =>'accnum',
                       '-alphabet'         =>'dna' );

However, in most cases, it is preferable to access sequence data from
some online data file or database. Note that in common with
conventional bioinformatics usage we will sometimes call a "database"
what might be more appropriately referred to as an "indexed flat
file".

Bioperl supports accessing remote databases as well as developing 
indices for setting up local databases.  There are two general approaches 
to accomplishing this.  If you know what kind of database the sequences 
is stored in (i.e. flat file, local relational database or a database 
accessed remotely over the internet), you can write a script that specifically 
accesses data from that kind of database.  This approach is described 
in sections III.1.1 and III.1.2 for access from remote databases and 
local indexed flat files respectively. To explicitly access sequence 
data from a local relational database requires installing and setting 
up the modules in the bioperl-db library which is outside the scope 
of this tutorial.

The other approach is to use the recently developed OBDA (Open
Bioinformatics Data Access) Registry system.  Using OBDA, it is
possible to import sequence data from a database without your needing
to know whether the required database is flat-file or relational or
even whether it is local or accessible only over the net.
Descriptions of how to set up the necessary registry configuration
file and access sequence data with the registry in described in
BIODATABASE_ACCESS in the doc/howto subdirectory and won't be repeated
here. The OBDA approach is still under development as of this 
writing (March 2003) so if you run into difficulties you can always 
use the older database specific access methods described in the next
two subsections.

=head2   III.1.1 Accessing remote databases (Bio::DB::GenBank, etc)

Accessing sequence data from the principal molecular biology databases
is straightforward in bioperl.  Data can be accessed by means of the
sequence's accession number or id.  Batch mode access is also
supported to facilitate the efficient retrieval of multiple sequences.
For retrieving data from genbank, for example, the code could be as
follows:

  $gb = new Bio::DB::GenBank();
  # this returns a Seq object :
  $seq1 = $gb->get_Seq_by_id('MUSIGHBA1');
  # this returns a Seq object :
  $seq2 = $gb->get_Seq_by_acc('AF303112');
  # this returns a SeqIO object :
  $seqio = $gb->get_Stream_by_id(["J00522","AF303112","2981014"]);

See section L<"III.2.1"> for information on using this SeqIO object.

Bioperl currently supports sequence data retrieval from the genbank,
genpept, RefSeq, swissprot, and EMBL databases. See L<Bio::DB::GenBank>,
L<Bio::DB::GenPept>, L<Bio::DB::SwissProt>, L<Bio::DB::RefSeq> and
L<Bio::DB::EMBL> for more information. A user can also specify a different
database mirror for a database - this is especially relevent for the SwissProt
resource where there are many ExPaSy mirrors.  There are also configuration
options for specifying local proxy servers for those behind firewalls.

The retrieval of NCBI RefSeqs sequences is supported through a special
module called Bio::DB::RefSeq which actually queries an EBI server.
Please see L<Bio::DB::RefSeq> before using it as there are some caveats
with RefSeq retrieval. RefSeq ids in Genbank begin with "NT_", "NC_",
"NG_", "NM_", "NP_", "XM_", "XR_", or "XP_" (for more information see
http://www.ncbi.nlm.nih.gov/LocusLink/refseq.html). Bio::DB::GenBank
can be used to retrieve entries corresponding to these ids but bear in
mind that these are not Genbank entries, strictly speaking. See
L<Bio::DB::GenBank> for special details on retrieving entries beginning
with "NT_", these are specially formatted "CONTIG" entries.

Bioperl also supports retrieval from a remote Ace database. This
capability requires the presence of the external AcePerl module. You
need to download and install the aceperl module from
http://stein.cshl.org/AcePerl/.

An additional module is available for accessing remote databases, BioFetch,
which queries the dbfetch script at EBI. The available databases are EMBL,
GenBank, or SWALL, and the entries can be retrieved in different formats 
as objects or streams (SeqIO objects), or as "tempfiles". See
L<Bio::DB::BioFetch> for the details.


=head2  III.1.2 Indexing and accessing local databases (Bio::Index::*, bpindex.pl, bpfetch.pl, Bio::DB::*)

Alternately, bioperl permits indexing local sequence data files by
means of the Bio::Index or Bio::DB::Fasta objects.  The following sequence
data formats are supported by Bio::Index: genbank, swissprot, pfam, embl and
fasta.  Once the set of sequences have been indexed using Bio::Index,
individual sequences can be accessed using syntax very similar to that
described above for accessing remote databases.  For example, if one wants to
set up an indexed flat-file database of fasta files, and later wants then to
retrieve one file, one could write scripts like:

  # script 1: create the index
  use Bio::Index::Fasta; # using fasta file format
  use strict; # some users have reported that this is necessary

  my $Index_File_Name = shift;
  my $inx = Bio::Index::Fasta->new(
      -filename => $Index_File_Name,
      -write_flag => 1);
  $inx->make_index(@ARGV);

  # script 2: retrieve some files
  use Bio::Index::Fasta;
  use strict; # some users have reported that this is necessary

  my $Index_File_Name = shift;
  my $inx = Bio::Index::Fasta->new($Index_File_Name);
  foreach  my $id (@ARGV) {
      my $seq = $inx->fetch($id);  # Returns Bio::Seq object
      # do something with the sequence
  }

To facilitate the creation and use of more complex or flexible
indexing systems, the bioperl distribution includes two sample scripts
in the scripts/index directory, bpindex.PLS and bpfetch.PLS.  These scripts
can be used as templates to develop customized local data-file indexing
systems.

Bioperl also supplies Bio::DB::Fasta as a means to index and query Fasta
format files. It's similar in spirit to Bio::Index::Fasta but offers more
methods, e.g.

  use Bio::DB::Fasta;
  use strict;

  my $db = Bio::DB::Fasta->new($file);  # one file or many files
  my $seqstring = $db->seq($id);        # get a sequence as string
  my $seqobj = $db->get_Seq_by_id($id); # get a PrimarySeq obj
  my $desc = $db->header($id);          # get the header, or description line

See L<Bio::DB::Fasta> for more information on this fully-featured module.

Both modules also offer the user the ability to designate a specific string
within the fasta header as the desired id, such as the gi number within the
string "gi|4556644|gb|X45555". Consider the following fasta-formatted 
sequence, "test.fa":

  >gi|523232|emb|AAC12345|sp|D12567 titin fragment
  MHRHHRTGYSAAYGPLKJHGYVHFIMCVVVSWWASDVVTYIPLLLNNSSAGWKRWWWIIFGGE
  GHGHHRTYSALWWPPLKJHGSKHFILCVKVSWLAKKERTYIPKKILLMMGGWWAAWWWI

By default Bio::Index::Fasta and Bio::DB::Fasta will use the first "word" they 
encounter in the fasta header as the retrieval key, in this case 
"gi|523232|emb|AAC12345|sp|D12567". What would be more useful as a key would be a 
single id.  The code below will index the "test.fa" file and create an index file 
called "test.fa.idx".

  $ENV{BIOPERL_INDEX_TYPE} = "SDBM_File";
  # look for the index in the current directory
  $ENV{BIOPERL_INDEX} = ".";

  my $file_name = "test.fa";
  my $inx = Bio::Index::Fasta->new( -filename   => $file_name . ".idx",
     				    -write_flag => 1 );
  # pass a reference to the critical function to the Bio::Index object
  $inx->id_parser(\&get_id);
  # make the index
  $inx->make_index($file_name);

  # here is where the retrieval key is specified
  sub get_id {
     my $header = shift;
     $header =~ /^>.*\bsp\|([A-Z]\d{5}\b)/;
     $1;
  }

Here is how you would retrieve the sequence, as a Bio::Seq object:

  my $seq = $inx->fetch("D12567");
  print $seq->seq;

What if you wanted to retrieve a sequence using either a Swissprot id
or a gi number and the fasta header was actually a concatenation of headers
with multiple gi's and Swissprots?

  >gi|523232|emb|AAC12345|sp|D12567|gi|7744242|sp|V11223 titin fragment

Modify the function that's passed to the id_parser method:

  sub get_id {
     my $header = shift;
     my (@sps) = $header =~ /^>.*\bsp\|([A-Z]\d{5})\b/g;
     my (@gis) = $header =~ /gi\|(\d+)\b/g;
     return (@sps,@gis);
  }

The Bio::DB::Fasta module uses the same principle, but the syntax is 
slightly different, for example:

  my $db = Bio::DB::Fasta->new('test.fa', -makeid=>\&make_my_id);
  my $seqobj = $db->get_Seq_by_id($id);

  sub make_my_id {
     my $description_line = shift;
     $description_line =~ /gi\|(\d+)\|emb\|(\w+)/;
     ($1,$2);
  }

The core bioperl installation does not support accessing sequences
and data stored in relational databases. However, this capability is
available with the auxilliary bioperl-db library. See section L<"IV.3"> for
more information.

=head2 III.2 Transforming formats of database/ file records

=for html <A NAME ="iii.2.1"></A>

=head2   III.2.1 Transforming sequence files (SeqIO)

A common - and tedious - bioinformatics task is that of converting
sequence data among the many widely used data formats.  Bioperl's
SeqIO object, however, makes this chore a breeze.  SeqIO can
read a stream of sequences - located in a single or in multiple files -
in a number of formats: Fasta, EMBL, GenBank, Swissprot, PIR, GCG, SCF,
phd/phred, Ace, fastq, exp, chado, or raw (plain sequence). SeqIO can 
also parse tracefiles in alf, ztr, abi, ctf, and ctr format Once the 
sequence data has been read in with SeqIO, it is available to bioperl 
in the form of Seq objects.  Moreover, the Seq objects can then be 
written to another file (again using SeqIO) in any of the supported 
data formats making data converters simple to implement, for example:

  use Bio::SeqIO;
  $in  = Bio::SeqIO->new('-file' => "inputfilename",
                         '-format' => 'Fasta');
  $out = Bio::SeqIO->new('-file' => ">outputfilename",
                         '-format' => 'EMBL');
  while ( my $seq = $in->next_seq() ) {$out->write_seq($seq); }

In addition, perl "tied filehandle" syntax is available to SeqIO,
allowing you to use the standard E<lt>E<gt> and print operations to read
and write sequence objects, eg:

  $in  = Bio::SeqIO->newFh('-file' => "inputfilename" ,
                           '-format' => 'Fasta');
  $out = Bio::SeqIO->newFh('-format' => 'EMBL');
  print $out $_ while <$in>;

If the "-format" argument isn't used then Bioperl will guess the format
based on the file's suffix in a case-insensitive manner. Here are the
current interpretations:

   Format   Suffixes

   fasta    fasta|fast|seq|fa|fsa|nt|aa
   genbank  gb|gbank|genbank
   scf      scf
   pir      pir
   embl     embl|ebl|emb|dat
   raw      txt
   gcg      gcg
   ace      ace
   bsml     bsm|bsml
   swiss    swiss|sp
   phd      phd|phred
   fastq    fastq
   qual
   chado
   abi*
   alf*
   ctf*
   exp*
   scf*
   ztr*
   pln*

* These formats require the bioperl-ext package

For more information see L<Bio::SeqIO> or the SeqIO HOWTO at 
http://bioperl.org/HOWTOs/html/SeqIO.html.

=for html <A NAME ="iii.2.2"></A>

=head2 III.2.2 Transforming alignment files (AlignIO)

Data files storing multiple sequence alignments also appear in varied
formats.  AlignIO is the bioperl object for data conversion of
alignment files. AlignIO is patterned on the SeqIO object and shares
most of SeqIO's features.  AlignIO currently supports input in the
following formats:

   fasta
   mase (Seaview)
   stockholm
   prodom
   selex (HMMER))
   bl2seq
   clustalw (.aln)
   msf (GCG)
   water*
   phylip (interleaved)
   stockholm
   nexus
   mega
   meme
   psi (PSI-BLAST)

*used by EMBOSS, see L<"IV.2.1">

AlignIO supports output in these formats: fasta, mase, selex, clustalw, msf/gcg,
and phylip (interleaved).  One significant difference between AlignIO and SeqIO
is that AlignIO handles IO for only a single alignment at a time but
SeqIO.pm handles IO for multiple sequences in a single stream. Syntax
for AlignIO is almost identical to that of SeqIO:

  use Bio::AlignIO;
  $in  = Bio::AlignIO->new('-file' => "inputfilename" ,
                           '-format' => 'fasta');
  $out = Bio::AlignIO->new('-file' => ">outputfilename",
                           '-format' => 'pfam');
  while ( my $aln = $in->next_aln() ) { $out->write_aln($aln); }

The only difference is that here, the returned object reference, $aln,
is to a SimpleAlign object rather than a Seq object.

AlignIO also supports the tied filehandle syntax described above for
SeqIO.  See L<Bio::AlignIO> and section L<"III.5"> for more
information.

=head2 III.3 Manipulating sequences

Bioperl contains many modules with functions for sequence analysis. And
if you cannot find the function you want in bioperl you may be able to
find it in EMBOSS or PISE , which are accessible through the bioperl-run
auxilliary library (see L<"IV.2.1">). 

=for html <A NAME ="iii.3.1"></A>

=head2 III.3.1  Manipulating sequence data with Seq methods

OK, so we know how to retrieve sequences and access them as Seq
objects.  Let's see how we can use the Seq objects to manipulate our
sequence data and retrieve information.  Seq provides multiple
methods for performing many common (and some not-so-common) tasks of
sequence manipulation and data retrieval.  Here are some of the most
useful:

The following methods return strings

  $seqobj->display_id();       # the human read-able id of the sequence
  $seqobj->seq();              # string of sequence
  $seqobj->subseq(5,10);       # part of the sequence as a string
  $seqobj->accession_number(); # when there, the accession number
  $seqobj->alphabet();         # one of 'dna','rna','protein'
  $seqobj->primary_id();       # a unique id for this sequence irregardless
                               # of its display_id or accession number
  $seqobj->desc();             # a description of the sequence

It is worth mentioning that some of these values correspond to specific
fields of given formats. For example, the display_id method returns
the LOCUS name of a Genbank entry, the (\S+) following the E<gt> character
in a Fasta file, the ID from a SwissProt file, and so on. The desc()
method will return the DEFINITION line of a Genbank file, the line
following the display_id in a Fasta file, and the DE field in a SwissProt
file.

The following methods return an array of Bio::SeqFeature objects:

   $seqobj->get_SeqFeatures;      # The 'top level' sequence features
   $seqobj->get_all_SeqFeatures;  # All sequence features, including sub-
                                  # seq features

For a comment annotation, you can use:

   use Bio::Annotation::Comment;
   $seq->annotation->add_Annotation('comment',
      Bio::Annotation::Comment->new(-text => 'some description');

For a reference annotation, you can use:

   use Bio::Annotation::Reference;
   $seq->annotation->add_Annotation('reference',
      Bio::Annotation::Reference->new(-authors  => 'author1,author2',
                                      -title    => 'title line',
                                      -location => 'location line',
                                      -medline  => 998122 );

Sequence features will be discussed further in section L<"III.7"> on
machine-readable sequence annotation. A general description of the
object can be found in L<Bio::SeqFeature::Generic>, and a description
of related, top-level "annotation" is found in L<Bio::Annotation::Collection>.

Additional sample code for obtaining sequence features can be found in
the script gb2features.pl in the subdirectory examples/DB.

The following methods returns new sequence objects, but do not transfer
features across:

  $seqobj->trunc(5,10);  # truncation from 5 to 10 as new object
  $seqobj->revcom;       # reverse complements sequence
  $seqobj->translate;    # translation of the sequence

Note that some methods return strings, some return arrays and some
return references to objects.  See L<Bio::Seq> for more information.

Many of these methods are self-explanatory. However, bioperl's flexible
translation methods warrant further comment. Translation in bioinformatics
can mean two slightly different things:

=over 2

=item 1 Translating a nucleotide sequence from start to end.

=item 2 Taking into account the constraints of real coding regions in mRNAs.

=back

The bioperl implementation of sequence-translation does the first of
these tasks easily. Any sequence object which is not of alphabet 'protein'
can be translated by simply calling the method which returns a protein
sequence object:

  $translation1 = $my_seq_object->translate;

However, the translate method can also be passed several optional
parameters to modify its behavior. For example, the first two
arguments to "translate" can be used to modify the characters used to
represent stop (default '*') and unknown amino acid ('X'). (These are
normally best left untouched.)  The third argument determines the
frame of the translation. The default frame is "0".  To get
translations in the other two forward frames, we would write:

  $translation2 = $my_seq_object->translate(undef,undef,1);
  $translation3 = $my_seq_object->translate(undef,undef,2);

The fourth argument to "translate" makes it possible to use
alternative genetic codes. There are currently 16 codon tables
defined, including tables for 'Vertebrate Mitochondrial', 'Bacterial',
'Alternative Yeast Nuclear' and 'Ciliate, Dasycladacean and Hexamita
Nuclear' translation. These tables are located in the object
Bio::Tools::CodonTable which is used by the translate method. For
example, for mitochondrial translation:

  $human_mitochondrial_translation =
      $my_seq_object->translate(undef,undef,undef, 2);

If we want to translate full coding regions (CDS) the way major
nucleotide databanks EMBL, GenBank and DDBJ do it, the translate
method has to perform more tricks. Specifically, 'translate' needs to
confirm that the sequence has appropriate start and terminator codons
at the beginning and the end of the sequence and that there are no
terminator codons present within the sequence.  In addition, if the
genetic code being used has an atypical (non-ATG) start codon, the
translate method needs to convert the initial amino acid to
methionine.  These checks and conversions are triggered by setting the
fifth argument of the translate method to evaluate to "true".

If argument 5 is set to true and the criteria for a proper CDS are
not met, the method, by default, issues a warning. By setting the
sixth argument to evaluate to "true", one can instead instruct
the program to die if an improper CDS is found, e.g.

  $protein_object =
      $cds->translate(undef,undef,undef,undef,1,'die_if_errors');

See L<Bio::Tools::CodonTable> for related details.

=head2 III.3.2 Obtaining basic sequence statistics- molecular weights,
residue & codon frequencies(SeqStats, SeqWord)

In addition to the methods directly available in the Seq object,
bioperl provides various "helper" objects to determine additional
information about a sequence.  For example, SeqStats object provides
methods for obtaining the molecular weight of the sequence as well the
number of occurrences of each of the component residues (bases for a
nucleic acid or amino acids for a protein.)  For nucleic acids,
SeqStats also returns counts of the number of codons used.  For
example:

  use SeqStats;
  $seq_stats  =  Bio::Tools::SeqStats->new($seqobj);
  $weight = $seq_stats->get_mol_wt();
  $monomer_ref = $seq_stats->count_monomers();
  $codon_ref = $seq_stats->count_codons();  # for nucleic acid sequence

Note: sometimes sequences will contain "ambiguous" codes.  For this
reason, get_mol_wt() returns a reference to a two element array
containing a greatest lower bound and a least upper bound of the
molecular weight.

The SeqWords object is similar to SeqStats and provides
methods for calculating frequencies of "words" (eg tetramers or hexamers)
within the sequence. See L<Bio::Tools::SeqStats> and L<Bio::Tools::SeqWords>
for more information.

=head2 III.3.3 Identifying restriction enzyme sites (RestrictionEnzyme)

Another common sequence manipulation task for nucleic acid sequences
is locating restriction enzyme cutting sites.  Bioperl provides the
RestrictionEnzyme object for this purpose. Bioperl's
standard RestrictionEnzyme object comes with data for more than 150
different restriction enzymes. A list of the available enzymes can be
accessed using the available_list() method. For example to select all
available enzymes with cutting patterns that are six bases long one
would write:

  $re = new Bio::Tools::RestrictionEnzyme('-name'=>'EcoRI');
  @sixcutters = $re->available_list(6);

Once an appropriate enzyme has been selected, the sites for that
enzyme on a given nucleic acid sequence can be obtained using the
cut_seq() method.  The syntax for performing this task is:

  $re1 = new Bio::Tools::RestrictionEnzyme(-name=>'EcoRI');
  # $seqobj is the Seq object for the dna to be cut
  @fragments =  $re1->cut_seq($seqobj);

Adding an enzyme not in the default list is easily accomplished:

  $re2 = new Bio::Tools::RestrictionEnzyme('-NAME' =>'EcoRV--GAT^ATC',
                                           '-MAKE' =>'custom');

Once the custom enzyme object has been created, cut_seq() can be
called in the usual manner. See L<Bio::Tools::RestrictionEnzyme> for
details.


=head2    III.3.4 Identifying amino acid cleavage sites (Sigcleave)

For amino acid sequences we may be interested to know whether the
amino acid sequence contains a cleavable signal sequence for
directing the transport of the protein within the cell.  SigCleave is
a program (originally part of the EGCG molecular biology package) to
predict signal sequences, and to identify the cleavage site based on
the von Heijne algorithm.

The "threshold" setting controls the score reporting.  If no value for
threshold is passed in by the user, the code defaults to a reporting
value of 3.5.  SigCleave will only return score/position
pairs which meet the threshold limit.

There are 2 accessor methods for this object. "signals" will return a
perl hash containing the sigcleave scores keyed by amino acid
position. "pretty_print" returns a formatted string similar to the
output of the original sigcleave utility.

The syntax for using Sigcleave is as follows:

  # create a Seq object, for example:
  $seqobj = Bio::Seq->new(-seq => "AALLHHHHHHGGGGPPRTTTTTVVVVVVVVVVVVVVV");

  use Bio::Tools::Sigcleave;
  $sigcleave_object = new Bio::Tools::Sigcleave
      ( -seq       => $seqobj,
        -threshold => 3.5,
        -desc      => 'test sigcleave protein seq',
        -type      => 'AMINO'
      );
  %raw_results      = $sigcleave_object->signals;
  $formatted_output = $sigcleave_object->pretty_print;

Note that the "type" in the Sigcleave object is "amino"
whereas in a Seq object it would be called "protein". Please see
L<Bio::Tools::Sigcleave> for details.


=head2 III.3.5 Miscellaneous sequence utilities: OddCodes, SeqPattern

OddCodes:

For some purposes it's useful to have a listing of an amino acid
sequence showing where the hydrophobic amino acids are located or
where the positively charged ones are.  Bioperl provides this
capability via the module Bio::Tools::OddCodes.

For example, to quickly see where the charged amino acids are located
along the sequence we perform:

  use Bio::Tools::OddCodes;
  $oddcode_obj = Bio::Tools::OddCodes->new($amino_obj);
  $output = $oddcode_obj->charge();

The sequence will be transformed into a three-letter sequence (A,C,N)
for negative (acidic), positive (basic), and neutral amino acids.  For
example the ACDEFGH would become NNAANNC.

For a more complete chemical description of the sequence one can call
the chemical() method which turns sequence into one with an 8-letter
chemical alphabet { A (acidic), L (aliphatic), M (amide), R
(aromatic), C (basic), H (hydroxyl), I (imino), S (sulfur) }:

  $output = $oddcode_obj->chemical();

In this case the sample sequence ACDEFGH would become LSAARAC.

OddCodes also offers translation into alphabets showing alternate
characteristics of the amino acid sequence such as hydrophobicity,
"functionality" or grouping using Dayhoff's definitions.  See the
documentation in L<Bio::Tools::OddCodes> for further details.

SeqPattern:

The SeqPattern object is used to manipulate sequences
that include perl "regular expressions".  A key motivation for
SeqPattern is to have a way of generating a reverse complement of a
nucleic acid sequence pattern that includes ambiguous bases and/or
regular expressions.  This capability leads to significant performance
gains when pattern matching on both the sense and anti-sense strands
of a query sequence are required. Typical syntax for using SeqPattern
is shown below.  For more information, there are several interesting
examples in the script seq_pattern.pl in the examples/tools directory.

  use Bio::Tools::SeqPattern;
  $pattern     = '(CCCCT)N{1,200}(agggg)N{1,200}(agggg)';
  $pattern_obj = new Bio::Tools::SeqPattern('-SEQ'  => $pattern,
                                            '-TYPE' => 'dna');
  $pattern_obj2 = $pattern_obj->revcom();
  $pattern_obj->revcom(1); # returns expanded rev complement pattern.

More detail can be found in L<Bio::Tools::SeqPattern>.


=for html <A NAME ="iii.3.6"></A>

=head2 III.3.6 Converting coordinate systems (Coordinate::Pair, RelSegment)

Coordinate system conversion is a common requirement, for example, when 
one wants to look at the relative positions of sequence features to one 
another and convert those relative positions to "absolute" coordinates 
along a chromosome or contig.  Although coordinate conversion sounds pretty 
trivial it can get fairly tricky when one includes the possibilities of switching 
to coordinates on negative (ie Crick) strands and/or having a coordinate 
system "terminate" because you have reached the end of a clone or contig.  
Bioperl has two different approaches to coordinate-system conversion (based 
on the modules Bio::Coordinate::Pair and Bio::DB::GFF::RelSegment, respectively).

The Coordinate::Pair approach is somewhat more "low level".  With it, you 
define an input coordinate system and an output coordinate system, where 
in each case a coordinate system is a triple of a start position, end position 
and strand.  The "end position" is especially important when dealing with 
unfinished assemblies where the coordinate system ends when one reaches 
the end of the sequence of a clone or contig.  Once one has defined the 
two coordinate systems, one defines a "Coordinate::Pair" to map between 
them.  Then one can map positions between the coordinates systems with 
code such as this:

  $input_coordinates = Bio::Location::Simple->new  
  (-seq_id => 'propeptide', -start => 1000, -end => 2000, -strand=>1 );
  $output_coordinates = Bio::Location::Simple->new  
  (-seq_id => 'peptide', -start => 1100, -end => 2100, -strand=>1 );
  $pair = Bio::Coordinate::Pair->new
  (-in => $input_coordinates ,  -out => $output_coordinates   );
  $pos = Bio::Location::Simple->new (-start => 500, -end => 500 );
  $res = $pair->map($pos);
  $converted_pos  = $res->gap->start;

See the documentation for Bio::Coordinate::Pair and Bio::Coordinate::GeneMapper 
for more details.

The Bio::DB::GFF::RelSegment approach is designed more for handling coordinate 
transformations of sequence features rather than for transforming arbitrary 
coordinate systems.  With Bio::DB::GFF::RelSegment you define a coordinate 
system relative to a specific feature (called the "refseq").  You also have 
access to the "absolute" coordinate system (typically of the entire chromosome.)  
You can determine the position of a feature relative to some other feature 
simply by redefining the relevant reference feature (ie the "refseq") with 
code like this:

  $db = Bio::DB::GFF->new(-dsn => 'dbi:mysql:elegans',
                        -adaptor =>'dbi:mysqlopt');

  $seg = $db->segment('ZK909');
  $relative_start = $seg->start;  # $relative_start = 1;

  # Now retrieve the start position of ZK909 relative to feature ZK337
  $seg->refseq('ZK337');
  $relative_start = $seg->start;  

  # Now retrieve the start position of ZK909 relative to the entire chromosome
  $absolute_start =  $seg->abs_start;

This approach is convenient because you don't have to keep track of 
coordinates directly; you just keep track of the name of a feature 
which in turn marks the coordinate-system origin.  However, this 
approach does require that you have stored all the sequence features 
in GFF format.   Moreover, Bio::DB::GFF::RelSegment has been principally 
developed and tested for applications where all the sequence features are 
stored in a Bioperl-db relational database. However,  if one wants to use 
the Bio:DB::GFF machinery (including its coordinate transformation 
capabilities) without building a local relational database, this is 
possible by defining the 'database' as having an adaptor called 'memory'; 
e.g. 

  $db = Bio::DB::GFF->new( '-adaptor' => 'memory' );

For more details on coordinate transformations and other GFF-related 
capabilities in Bioperl see  Bio::DB::GFF::RelSegment.pm, Bio::DB::GFF.pm 
and the test file t/BioDBGFF.t.

=head2 III.4 Searching for "similar" sequences

One of the basic tasks in molecular biology is identifying sequences
that are, in some way, similar to a sequence of interest.  The Blast
programs, originally developed at the NCBI, are widely used for
identifying such sequences.  The bioperl and bioperl-run packages
offer a number of modules to facilitate running Blast as well as to
parse the often voluminous reports produced by Blast.

=head2   III.4.1 Running BLAST (using RemoteBlast.pm)

Bioperl supports remote execution of blasts at NCBI by means of the
RemoteBlast object.

A skeleton script to run a remote blast might look as follows:

  $remote_blast = Bio::Tools::Run::RemoteBlast->new(
  	   '-prog' => 'blastp','-data' => 'ecoli','-expect' => '1e-10' );
  $r = $remote_blast->submit_blast("t/data/ecolitst.fa");
  while (@rids = $remote_blast->each_rid ) {
      foreach $rid ( @rids ) {$rc = $remote_blast->retrieve_blast($rid);}}

You may want to change some parameter of the remote job and this example
shows how to change the matrix:

$Bio::Tools::Run::RemoteBlast::HEADER{'MATRIX_NAME'} = 'BLOSUM25';

For a description of the many CGI parameters see:

http://www.ncbi.nlm.nih.gov/BLAST/Doc/urlapi.html

Note that the script has to be broken into two parts. The actual Blast
submission and the subsequent retrieval of the results. At times when the
NCBI Blast is being heavily used, the interval between when a Blast
submission is made and when the results are available can be substantial.

The object $rc would contain the blast report that could then be parsed with
Bio::Tools::BPlite or Bio::SearchIO. The default object is BPlite
in version 1.0 or earlier and it's SearchIO after version 1.0. The
object type can be changed using the -readmethod parameter but bear
in mind that the favored Blast parser is Bio::SearchIO, others won't be
supported in later versions.

Note that to make this script actually useful, one should add details
such as checking return codes from the Blast to see if it succeeded and
a "sleep" loop to wait between consecutive requests to the NCBI server.
See example 25 in the demonstration script in the appendix to see some
working code you could use, or L<Bio::Tools::Run::RemoteBlast> for
details.

It should also be noted that the syntax for creating a remote blast factory
is slightly different from that used in creating StandAloneBlast, Clustalw,
and T-Coffee factories.  Specifically RemoteBlast requires parameters to
be passed with a leading hyphen, as in '-prog' =E<gt> 'blastp', while the
other programs do not pass parameters with a leading hyphen.

=for html <A NAME ="iii.4.2"></A>

=head2    III.4.2 Parsing BLAST and FASTA reports with Search and SearchIO

No matter how Blast searches are run (locally or remotely, with or
without a perl interface), they return large quantities of data that
are tedious to sift through.  Bioperl offers several different objects
- Search.pm/SearchIO.pm, and BPlite.pm (along with its minor
modifications, BPpsilite and BPbl2seq) for parsing Blast reports.
Search and SearchIO which are the principal Bioperl interfaces for
Blast and FAST report parsing are described in this section.  The
older BPlite is described in section L<"III.4.3">. We recommend you
use SearchIO, it's certain to be supported in future releases.

The Search and SearchIO modules provide a uniform interface for
parsing sequence-similarity-search reports generated by BLAST (in
standard and BLAST XML formats), PSI-BLAST, RPS-BLAST and FASTA. The
SearchIO modules also provide a parser for HMMER reports and in
the future, it is envisioned that the Search/SearchIO syntax will be
extended to provide a uniform interface to an even wider range of report
parsers including parsers for Genscan.

Parsing sequence-similarity reports with Search and SearchIO is
straightforward.  Initially a SearchIO object specifies a file
containing the report(s). The method next_result reads the next report
into a Search object in just the same way that the next_seq method of
SeqIO reads in the next sequence in a file into a Seq object.

Once a report (i.e. a Search object) has been read in and is available
to the script, the report's overall attributes (e.g. the query) can be
determined and its individual "hits" can be accessed with the
next_hit method.  Individual high-scoring-pairs for each hit
can then be accessed with the next_hsp method. Except for
the additional syntax required to enable the reading of multiple
reports in a single file, the remainder of the Search/SearchIO parsing
syntax is very similar to that of the BPlite object it is intended to replace.
Sample code to read a BLAST report might look like this:

  # Get the report
  $searchio = new Bio::SearchIO (-format => 'blast',
				 -file   => $blast_report);
  $result = $searchio->next_result;

  # Get info about the entire report
  $result->database_name;
  $algorithm_type =  $result->algorithm;

  # get info about the first hit
  $hit = $result->next_hit;
  $hit_name = $hit->name ;

  # get info about the first hsp of the first hit
  $hsp = $hit->next_hsp;
  $hsp_start = $hsp->query->start;

For more details there is an excellent description of how to use
SearchIO at http://www.bioperl.org/HOWTOs/html/SearchIO.html
or in the docs/ subdirectory of the distribution. Additional
documentation can be found in: L<Bio::SearchIO::blast>,
L<Bio::SearchIO::psiblast>, L<Bio::SearchIO::blastxml>,
L<Bio::SearchIO::fasta>, and L<Bio::SearchIO>. There is also sample
code in the Bio/examples/searchio directory which illustrates how to
use SearchIO.

=for html <A NAME ="iii.4.3"></A>

=head2 III.4.3 Parsing BLAST reports with BPlite, BPpsilite, and BPbl2seq

Bioperl's older BLAST report parsers - BPlite, BPpsilite, BPbl2seq and
Blast.pm - are expected to be phased out over a period of time. Since a
considerable amount of legacy Bioperl scripts has been written which
heavily use these objects, they are likely to remain within Bioperl
for some time.

Much of the user interface of BPlite is very similar to that of Search.
However accessing the next hit or HSP uses methods called next_Sbjct and
next_HSP, respectively - in contrast to Search's next_hit and next_hsp.


BPlite

The syntax for using BPlite is as follows where the method
for retrieving hits is now called "nextSbjct" (for "subject"), while the
method for retrieving high-scoring-pairs is called "nextHSP":

  use Bio::Tools::BPlite;
  $report = new Bio::Tools::BPlite(-fh=>\*STDIN);
  $report->query;
  while(my $sbjct = $report->nextSbjct) {
       $sbjct->name;
       while (my $hsp = $sbjct->nextHSP) { $hsp->score; }
  }

A complete description of the module can be found in L<Bio::Tools::BPlite>.

BPpsilite

BPpsilite and BPbl2seq are objects for parsing (multiple iteration)
PSIBLAST reports and Blast bl2seq reports, respectively.  They are
both minor variations on the BPlite object. See L<Bio::Tools::BPbl2seq>
and L<Bio::Tools::BPpsilite> for details.

The syntax for parsing a multiple iteration PSIBLAST report is as
shown below.  The only significant additions to BPlite are methods to
determine the number of iterated blasts and to access the results from
each iteration.  The results from each iteration are parsed in the
same manner as a (complete) BPlite object.

  use Bio::Tools::BPpsilite;
  $report = new Bio::Tools::BPpsilite(-fh=>\*STDIN);
  $total_iterations = $report->number_of_iterations;
  $last_iteration = $report->round($total_iterations)
  while(my $sbjct =  $last_iteration ->nextSbjct) {
       $sbjct->name;
       while (my $hsp = $sbjct->nextHSP) {$hsp->score; }
  }

See L<Bio::Tools::BPpsilite> for details.

BPbl2seq

BLAST bl2seq is a program for comparing and aligning two sequences
using BLAST.  Although the report format is similar to that of a
conventional BLAST, there are a few differences.  Consequently, the
standard bioperl parser BPlite ia unable to read bl2seq
reports directly. From the user's perspective, one difference
between bl2seq and other blast reports is that the bl2seq report does
not print out the name of the first of the two aligned sequences.
Consequently, BPbl2seq has no way of identifying the name of one of
the initial sequence unless it is explicitly passed to constructor as
a second argument as in:

  use Bio::Tools::BPbl2seq;
  $report = Bio::Tools::BPbl2seq->new(-file => "t/data/dblseq.out",
                                      -queryname => "ALEU_HORVU");
  $hsp = $report->next_feature;
  $answer=$hsp->score;

In addition, since there will only be (at most) one "subject" (hit) in a
bl2seq report one should use the method $report-E<gt>next_feature,
rather than $report-E<gt>nextSbjct-E<gt>nextHSP to obtain the next high
scoring pair. See L<Bio::Tools::BPbl2seq> for more details.

Blast.pm

The Bio::Tools::Blast parser has been removed from Bioperl as of version
1.1. Consequently, the BPlite parser (described in the
section L<"III.4.3">) or the Search/SearchIO parsers (section L<"III.4.2">)
should be used for BLAST parsing within bioperl (SearchIO is the preferred
approach and will be formally supported in subsequent releases).

=for html <A NAME ="iii.4.4"></A>

=head2 III.4.4 Parsing HMM reports (HMMER::Results, SearchIO)

Blast is not the only sequence-similarity-searching program supported
by bioperl. HMMER is a Hidden Markov Model (HMM) program that
(among other capabilities) enables sequence similarity searching, from
http://hmmer.wustl.edu. Bioperl does not currently provide a perl interface
for running HMMER.  However, bioperl does provide 2 HMMER report parsers,
the recommended SearchIO HMMER parser and an older parser called HMMER::Results.

SearchIO can parse reports generated both by the HMMER program
hmmsearch - which searches a sequence database for sequences similar
to those generated by a given HMM - and the program hmmpfam - which
searches a HMM database for HMMs which match domains of a given
sequence.  Sample usage for parsing a hmmsearch report might be:

  use Bio::SearchIO;

  $in = new Bio::SearchIO(-format => 'hmmer',-file => '123.hmmsearch');
  while ( $res = $in->next_result ){
    # get a Bio::Search::Result::HMMERResult object
    print $res->query_name, " for HMM ", $res->hmm_name, "\n";
    while ( $hit = $res->next_hit ){
      print $hit->name, "\n";
      while ( $hsp = $hit->next_hsp ){
        print "length is ", $hsp->length, "\n";
      }
    }
  }

Purists may insist that the term "hsp" is not applicable to hmmsearch or
hmmpfam results and they may be correct - this is an unintended
consequence of using the flexible and extensible SearchIO approach. See
L<Bio::Search::Result::HMMERResult> for more information.

For documentation on the older HMMER parser, look at L<Bio::Tools::HMMER::Results>.

=for html <A NAME ="iii.4.5"></A>

=head2 III.4.5 Running BLAST locally  (StandAloneBlast)

There are several reasons why one might want to run the Blast programs
locally - speed, data security, immunity to network problems, being
able to run large batch runs, wanting to use custom or proprietary
databases, etc.  The NCBI provides a downloadable version of blast in
a stand-alone format, and running blast locally without any use of
perl or bioperl is completely straightforward.  However, there are
situations where having a perl interface for running the blast
programs locally is convenient.

The module Bio::Tools::Run::StandAloneBlast offers the ability to wrap
local calls to blast from within perl.  All of the currently available
options of NCBI Blast (eg PSIBLAST, PHIBLAST, bl2seq) are available
from within the bioperl StandAloneBlast interface.  Of course, to use
StandAloneBlast, one needs to have installed locally ncbi-blast as
well as one or more blast-readable databases.

Basic usage of the StandAloneBlast.pm module is simple.  Initially, a
local blast "factory object" is created.

  @params = ('program'  => 'blastn',
             'database' => 'ecoli.nt');
  $factory = Bio::Tools::Run::StandAloneBlast->new(@params);

Any parameters not explicitly set will remain as the BLAST defaults.
Once the factory has been created and the appropriate parameters set,
one can call one of the supported blast executables.  The input
sequence(s) to these executables may be fasta file(s), a Seq
object or an array of Seq objects, eg

  $input = Bio::Seq->new(-id  =>"test query",
  			 -seq =>"ACTAAGTGGGGG");
  $blast_report = $factory->blastall($input);

The returned blast report will be in the form of a bioperl
parsed-blast object.  The report object may be either a BPlite,
BPpsilite, BPbl2seq or Blast object depending on the type of blast
search.  The "raw" blast report is also available.

The syntax for running PHIBLAST, PSIBLAST and bl2seq searches via
StandAloneBlast is also straightforward.  See
L<Bio::Tools::Run::StandAloneBlast> documentation for details. In
addition, the script standaloneblast.pl in the examples/tools directory
contains descriptions of various possible applications of the
StandAloneBlast object. This script shows how the blast report object
can access a blast parser directly, eg

  while (my $sbjct = $blast_report->next_hit){
     while (my $hsp = $sbjct->next_hsp){
        print $hsp->score . " " . $hsp->subject->seqname . "\n";
     }
  }

See the sections L<"III.4.2"> and L<"III.4.3"> on for more details on
parsing BLAST reports. Also see the script standaloneblast.pl in the
examples/tools/ subdirectory for more detailed working examples of code
using this module.

=for html <A NAME ="iii.5"></A>

=head2 III.5 Manipulating sequence alignments

Once one has identified a set of similar sequences, one often needs to
create an alignment of those sequences. Bioperl offers several perl
objects to facilitate sequence alignment: pSW, Clustalw.pm, TCoffee.pm
and the bl2seq option of StandAloneBlast. As of release 1.2 of
bioperl, using these modules (except bl2seq) requires a bioperl
auxilliary library (bioperl-ext for pSW, bioperl-run for the others)
and are therefore described in section IV. Here we describe only the
module within the bioperl core package for manipulating previously
created alignments, namely the SimpleAlign module.

The script aligntutorial.pl in the examples/align/ subdirectory is
another good source of information of ways to create and manipulate
sequence alignments within bioperl.

SimpleAlign objects are produced by bioperl-run alignment creation objects
(eg Clustalw.pm, BLAST's bl2seq, and pSW) or they can be read in from
files of multiple-sequence alignments in various formats using AlignIO.

Some of the manipulations possible with SimpleAlign include:

=over 4

=item *

slice(): Obtaining an alignment "slice", that is, a subalignment inclusive of
specified start and end columns.  Sequences with no residues in the slice are
excluded from the new alignment and a warning is printed.

=item *

column_from_residue_number(): Finding column in an alignment where a specified
residue of a specified sequence is located.

=item *

consensus_string(): Making a consensus string. This method includes an
optional threshold parameter, so that positions in the alignment with lower
percent-identity than the threshold are marked by "?"'s in the consensus

=item *

percentage_identity(): A fast method for calculating the average percentage
identity of the alignment

=item *

consensus_iupac(): Making a consensus using IUPAC ambiguity codes from
DNA and RNA.


=back

Skeleton code for using some of these features is shown below.  More detailed,
working code is in bptutorial.pl example 13 and in align_on_codons.pl in the
examples/align directory. Additional documentation on methods can be found in
L<Bio::SimpleAlign> and L<Bio::LocatableSeq>.

  use Bio::SimpleAlign;
  $aln = Bio::SimpleAlign->new('t/data/testaln.dna');
  $threshold_percent = 60;
  $consensus_with_threshold = $aln->consensus_string($threshold_percent);
  $iupac_consensus = $aln->consensus_iupac();   # dna/rna alignments only
  $percent_ident = $aln->percentage_identity;
  $seqname = '1433_LYCES';
  $pos = $aln->column_from_residue_number($seqname, 14);

=head2 III.6 Searching for genes and other structures on genomic DNA
(Genscan, Sim4, Grail, Genemark, ESTScan, MZEF, EPCR)

Automated searching for putative genes, coding sequences,
sequence-tagged-sites (STS's) and other functional
units in genomic and expressed sequence tag (EST) data has
become very important as the available quantity of sequence data has
rapidly increased.  Many feature searching programs currently exist.
Each produces reports containing predictions that must be read
manually or parsed by automated report readers.

Parsers for six widely used gene prediction programs - Genscan, Sim4,
Genemark, Grail, ESTScan and MZEF - are available in bioperl. The
interfaces for these parsers are all similar.  We illustrate the usage
for Genscan and Sim4 here.  The syntax is relatively self-explanatory;
see L<Bio::Tools::Genscan>, L<Bio::Tools::Genemark>,
L<Bio::Tools::Grail>, L<Bio::Tools::ESTScan>, L<Bio::Tools::MZEF>, and
L<Bio::Tools::Sim4::Results> for further details.

  use Bio::Tools::Genscan;
  $genscan = Bio::Tools::Genscan->new(-file => 'result.genscan');
  # $gene is an instance of Bio::Tools::Prediction::Gene
  # $gene->exons() returns an array of Bio::Tools::Prediction::Exon objects
  while($gene = $genscan->next_prediction())
      { @exon_arr = $gene->exons(); }
  $genscan->close();

See L<Bio::Tools::Prediction::Gene> and L<Bio::Tools::Prediction::Exon>
for more details.

  use Bio::Tools::Sim4::Results;
  $sim4 = new Bio::Tools::Sim4::Results(-file => 't/data/sim4.rev',
                                        -estisfirst => 0);
  # $exonset is-a Bio::SeqFeature::Generic with Bio::Tools::Sim4::Exons
  # as sub features
  $exonset = $sim4->next_exonset;
  @exons = $exonset->sub_SeqFeature();
  # $exon is-a Bio::SeqFeature::FeaturePair
  $exon = 1;
  $exonstart = $exons[$exon]->start();
  $estname = $exons[$exon]->est_hit()->seqname();
  $sim4->close();

See L<Bio::SeqFeature::Generic> and L<Bio::Tools::Sim4::Exons> for more
information.

A parser for the ePCR program is also available. The ePCR program identifies
potential PCR-based sequence tagged sites (STSs)
For more details see the documentation in L<Bio::Tools::EPCR>.
A sample skeleton script for parsing an ePCR report and using the data to
annotate a genomic sequence might look like this:

  use Bio::Tools::EPCR;
  use Bio::SeqIO;
  $parser = new Bio::Tools::EPCR(-file => 'seq1.epcr');
  $seqio = new Bio::SeqIO(-format => 'fasta', -file => 'seq1.fa');
  $seq = $seqio->next_seq;
  while( $feat = $parser->next_feature ) {
        # add EPCR annotation to a sequence
        $seq->add_SeqFeature($feat);}

=for html <A NAME ="iii.7"></A>

=head2 III.7 Developing machine readable sequence annotations

Historically, annotations for sequence data have been entered and read
manually in flat-file or relational databases with relatively little
concern for machine readability.  More recent projects - such as EBI's
Ensembl project and the efforts to develop an XML molecular biology
data specification - have begun to address this limitation.  Because
of its strengths in text processing and regular-expression handling,
perl is a natural choice for the computer language to be used for this
task.  And bioperl offers numerous tools to facilitate this process -
several of which are described in the following sub-sections.

=for html <A NAME ="iii.7.1"></A>

=head2 III.7.1 Representing sequence annotations (Annotation::Collection,SeqFeature)

In Bioperl, most sequence annotations are stored in sequence-feature
(SeqFeature) objects.  A SeqFeature onject generally has (at least) a
description (e.g. exon, promoter) and a location specified in as its
start postion and length an a reference to the "parent" sequence of
which it is a feature. In addition, A Seq object can also have an
Annotation object (used to store database links, literature references
and comments) associated with it.  Creating a new SeqFeature and
Annotation and associating it with a Seq is accomplished with syntax
like:

  $feat = new Bio::SeqFeature::Generic('-start'   => 40,
  				       '-end'     => 80,
  				       '-strand'  => 1,
  				       '-primary' => 'exon',
  				       '-source'  => 'internal' );
  $seqobj->add_SeqFeature($feat); # Add the SeqFeature to the parent
  $annotations = $seqobj->annotation(new Bio::Annotation::Collection);
  $annotations->add_Annotation('disease', $object);

Once the features and annotations have been associated with the Seq,
they can be with retrieved, eg:

  @topfeatures = $seqobj->top_SeqFeatures(); # just top level, or
  @allfeatures = $seqobj->all_SeqFeatures(); # descend into sub features
  $disease_annotation = $annotations->get_Annotations('disease'); 

The individual components of a SeqFeature can also be set or retrieved
with methods including:

  # attributes which return numbers
  $feat->start          # start position
  $feat->end            # end position

  $feat->strand         # 1 means forward, -1 reverse, 0 not relevant

  # attributes which return strings
  $feat->primary_tag    # the main 'name' of the sequence feature,
                        # eg, 'exon'
  $feat->source_tag     # where the feature comes from, eg'BLAST'

  # attributes which return Bio::PrimarySeq objects
  $feat->seq            # the sequence between start,end
  $feat->entire_seq     # the entire sequence

  # other useful methods include
  $feat->overlap($other)  # do SeqFeature $feat and SeqFeature $other overlap?
  $feat->contains($other) # is $other completely within $feat?
  $feat->equals($other)   # do $feat and $other completely agree?
  $feat->sub_SeqFeatures  # create/access an array of subsequence features

See L<Bio::Annotation::Collection> and L<Bio::SeqFeature::Generic> as starting
points for further exploration, and see the examples/tools/gff2ps.pl
and examples/tools/gb_to_gff.pl scripts.

It is worth mentioning that one can also retrieve the start and end
positions of a feature using a Bio::LocationI object:

  $location = $feat->location # $location is a Bio::LocationI object
  $location->start;           # start position
  $location->end;             # end position

This is useful because one needs a Bio::Location::SplitLocationI object
in order to retrieve the split coordinates inside the Genbank or EMBL join()
statements (e.g. "CDS    join(51..142,273..495,1346..1474)"):

  if ( $feat->location->isa('Bio::Location::SplitLocationI') &&
	       $feat->primary_tag eq 'CDS' )  {
    foreach $loc ( $feat->location->sub_Location ) {
      print $loc->start . ".." . $loc->end . "\n";
    }
  }

See L<Bio::LocationI> and L<Bio::Location::SplitLocationI> for more
information.

If more detailed annotation is required than is currently available in Seq
objects the RichSeq object may be used. It is applicable in particular to
database sequences (EMBL, GenBank and Swissprot) with detailed annotations.
Sample usage might be:

    @secondary   = $richseq->get_secondary_accessions;
    $division    = $richseq->division;
    @dates       = $richseq->get_dates;
    $seq_version = $richseq->seq_version;

See L<Bio::Seq::RichSeqI> for more details.


=for html <A NAME ="iii.7.2"></A>

=head2 III.7.2 Representing and large and/or changing sequences (LiveSeq,LargeSeq)

Very large sequences and/or data files with sequences that are frequently being
updated present special problems to automated sequence-annotation storage and
retrieval projects.  Bioperl's LargeSeq and LiveSeq objects are designed to
address these two situations.

LargeSeq

A LargeSeq object is a SeqI compliant object that stores a sequence as
a series of files in a temporary directory (see sect L<"II.1"> or
L<Bio::SeqI> for a definition of SeqI objects). The aim is to enable
storing very large sequences (eg, E<gt> 100MBases) without running out
of memory and, at the same time, preserving the familiar bioperl Seq
object interface. As a result, from the users perspective, using a
LargeSeq object is almost identical to using a Seq object. The
principal difference is in the format used in the SeqIO calls. Another
difference is that the user must remember to only read in small chunks
of the sequence at one time.  These differences are illustrated in the
following code:

  $seqio = new Bio::SeqIO('-format'=>'largefasta',
  			  '-file'  =>'t/data/genomic-seq.fasta');
  $pseq = $seqio->next_seq();
  $plength = $pseq->length();
  $last_4 = $pseq->subseq($plength-3,$plength);  # this is OK

  # On the other hand, the next statement would
  # probably cause the machine to run out of memory
  # $lots_of_data = $pseq->seq();  # NOT OK for a large LargeSeq object

LiveSeq

The LiveSeq object addresses the need for a sequence object capable of
handling sequence data that may be changing over time.  In such a
sequence, the precise locations of features along the sequence may
change.  LiveSeq deals with this issue by re-implementing the sequence
object internally as a "double linked chain." Each element of the
chain is connected to other two elements (the PREVious and the NEXT
one). There is no absolute position (like in an array), hence if
positions are important, they need to be computed (methods are
provided). Otherwise it's easy to keep track of the elements with
their "LABELs". There is one LABEL (think of it as a pointer) to each
ELEMENT. The labels won't change after insertions or deletions of the
chain. So it's always possible to retrieve an element even if the
chain has been modified by successive insertions or deletions.

Although the implementation of the LiveSeq object is novel, its
bioperl user interface is unchanged since LiveSeq implements a
PrimarySeqI interface (recall PrimarySeq is the subset of Seq without
annotations or SeqFeatures - see section L<"II.1"> or L<Bio::PrimarySeq>).
Consequently syntax for using LiveSeq objects is familiar although a
modified version of SeqIO called Bio::LiveSeq::IO::Bioperl needs to be
used to actually load the data, eg:

  $loader=Bio::LiveSeq::IO::BioPerl->load('-db'=>"EMBL",
                                          '-file'=>"t/data/factor7.embl");
  $gene=$loader->gene2liveseq('-gene_name' => "factor7");
  $id = $gene->get_DNA->display_id ;
  $maxstart = $gene->maxtranscript->start;

See L<Bio::LiveSeq::IO::BioPerl> for more details.

=head2 III.7.3 Representing related sequences - mutations,
polymorphisms etc (Allele, SeqDiff)

A Mutation object allows for a basic description of a sequence change
in the DNA sequence of a gene. The Mutator object takes in mutations,
applies them to a LiveSeq gene and returns a set of Bio::Variation
objects describing the net effect of the mutation on the gene at the DNA,
RNA and protein level.

The objects in Bio::Variation and Bio::LiveSeq directory were
originally designed for the "Computational Mutation Expression
Toolkit" project at European Bioinformatics Institute (EBI). The
result of using them to mutate a gene is a holder object, 'SeqDiff',
that can be printed out or queried for specific information. For
example, to find out if restriction enzyme changes caused by a
mutation are exactly the same in DNA and RNA sequences, we can write:

  use Bio::LiveSeq::IO::BioPerl;
  use Bio::LiveSeq::Mutator;
  use Bio::LiveSeq::Mutation;

  $loader = Bio::LiveSeq::IO::BioPerl->load('-file' => "$filename");
  $gene = $loader->gene2liveseq('-gene_name' => $gene_name);
  $mutation = new Bio::LiveSeq::Mutation ('-seq' =>'G',
  					  '-pos' => 100 );
  $mutate = Bio::LiveSeq::Mutator->new('-gene'      => $gene,
  				       '-numbering' => "coding"  );
  $mutate->add_Mutation($mutation);
  $seqdiff = $mutate->change_gene();
  $DNA_re_changes = $seqdiff->DNAMutation->restriction_changes;
  $RNA_re_changes = $seqdiff->RNAChange->restriction_changes;
  $DNA_re_changes eq $RNA_re_changes or print "Different!\n";

For a complete working script, see the change_gene.pl script
in the examples/liveseq directory. For more details on the use of these objects
see L<Bio::LiveSeq::Mutator> and L<Bio::LiveSeq::Mutation> as well as
the original documentation for the "Computational Mutation Expression
Toolkit" project at http://www.ebi.ac.uk/mutations/toolkit/.

=for html <A NAME ="iii.7.4"></A>

=head2 III.7.4 Incorporating quality data in sequence annotation (SeqWithQuality)

SeqWithQuality objects are used to describe sequences with very
specific annotations - that is, data quality annotations.  Data quality
information is important for documenting the reliability of base
"calls" in newly sequenced or otherwise questionable sequence
data. The quality data is contained within a Bio::Seq::PrimaryQual object.
Syntax for using SeqWithQuality objects is as follows:

  # first, make a PrimarySeq object
  $seqobj = Bio::PrimarySeq->new
	  ( -seq => 'atcgatcg',            -id  => 'GeneFragment-12',
	    -accession_number => 'X78121', -alphabet => 'dna');
  # now make a PrimaryQual object
  $qualobj = Bio::Seq::PrimaryQual->new
	 ( -qual => '10 20 30 40 50 50 20 10', -id  => 'GeneFragment-12',
	   -accession_number => 'X78121',      -alphabet => 'dna');
  # now make the SeqWithQuality object
  $swqobj = Bio::Seq::SeqQithQuality->new
	  ( -seq  => $seqobj, -qual => $qualobj);
  # Now we access the sequence with quality object
  $swqobj->id(); # the id of the SeqWithQuality object may not match the
                 # id of the sequence or of the quality
  $swqobj->seq(); # the sequence of the SeqWithQuality object
  $swqobj->qual(); # the quality of the SeqWithQuality object

A SeqWithQuality object is created automatically when phred output, a *phd
file, is read by SeqIO, eg

  $seqio = Bio::SeqIO->new(-file=>"my.phd",-format=>"phd");
  # or just 'Bio::SeqIO->new(-file=>"my.phd")'
  $seqWithQualObj = $seqio->next_seq;

See L<Bio::Seq::SeqWithQuality> for a detailed description of the methods,
L<Bio::Seq::PrimaryQual>, and L<Bio::SeqIO::phd>.


=head2 III.7.5 Sequence XML representations - generation and parsing (SeqIO::game, SeqIO::bsml)

The previous subsections have described tools for automated sequence
annotation by the creation of an "object layer" on top of a
traditional database structure.  XML takes a somewhat different
approach.  In XML, the data structure is unmodified, but machine
readability is facilitated by using a data-record syntax with special
flags and controlled vocabulary.

In order to transfer data with XML in biology, one needs an agreed
upon "vocabulary" of biological terms. Several of these have been
proposed and bioperl has at least some support for three: "game", BSML
and AGAVE. 

Once a vocabulary is agreed upon, it becomes possible to convert
sequence XML sequence features can be turned into bioperl Seq
annotations and SeqFeature objects.  Conversely Seq object features
and annotations can be converted to XML so that they become available
to any other systems.  Typical usage with "game" or "BSML" are shown
below. No special syntax is required by the user. Note that some Seq
annotation will be lost when using XML in this manner since generally
XML does not support all the annotation information available in Seq
objects.

  $str = Bio::SeqIO->new('-file'=> 't/data/test.game',
  			 '-format' => 'game');
  $seq = $str->next_primary_seq();
  $id = $seq->id;
  @feats = $seq->all_SeqFeatures();
  $first_primary_tag = $feats[0]->primary_tag;

  $str = Bio::SeqIO->new('-file'=> 'bsmlfile.xml',
  			 '-format' => 'bsml');
  $seq = $str->next_primary_seq();
  $id = $seq->id;
  @feats = $seq->all_SeqFeatures();
  $first_primary_tag = $feats[0]->primary_tag;

=head2  III.7.6 Representing Sequences using GFF (Bio:DB:GFF )

Another format for transmitting machine-readable sequence-feature data
is the Genome Feature Format (GFF).  This file type is well suited to
sequence annotation because it allows the ability to describe entries
in terms of parent-child relationships (see
http://www.sanger.ac.uk/software/GFF for details). Bioperl includes a
parser for converting between GFF files and SeqFeature objects.
Typical syntax looks like:

  $gffio = Bio::Tools::GFF->new(-fh => \*STDIN, -gff_version => 2);
  $feature;
    # loop over the input stream
  while($feature = $gffio->next_feature()) {
    # do something with feature
  }
  $gffio->close();

Further information can be found at L<Bio::Tools::GFF>. Also see the
examples/tools/gff2ps.pl, examples/tools/gb_to_gff.pl, and the
scripts in scripts/Bio-DB-GFF. Note: this module shouldn't be confused 
with the module Bio::DB::GFF which is for implementing relational 
databases when using bioperl-db.

=head2  III.8 Manipulating clusters of sequences (Cluster, ClusterIO)

Sequence alignments are not the only examples in which one might want
to manipulate a group of sequences together.  Such groups of related 
sequences are generally referred to as clusters.  Examples include
Unigene clusters and gene clusters resulting from clustering
algorithms being applied to microarray data.

The bioperl Cluster and ClusterIO modules are available for handling
sequence clusters.  Currently, cluster input/output modules are
available only for Unigene clusters.  To read in a Unigene cluster (in
the NCBI XML format) and then extract individual sequences for the
cluster for manipulation might look like this:

  $stream  = Bio::ClusterIO->new('-file' => "Hs.data",  '-format' => "unigene");
  # note: we quote -format to keep older perl's from complaining.
  while ( my $in = $stream->next_cluster() ) {
    print $in->unigene_id() . "\n";
	while ( my $sequence = $in->next_seq() ) {
	  print $sequence->accession_number() . "\n";
	}
   }

See L<Bio::Cluster::UniGene> for more details.

=head2  III.9 Representing non-sequence data in Bioperl: structures, trees and maps

Though bioperl has its roots in describing and searching nucleotide and protein
sequences it has also branched out into related fields of study,
such as protein structure, phylogenetic trees and genetic maps.


=head2 III.9.1 Using 3D structure objects and reading PDB files
(StructureI, Structure::IO)

A StructureIO object can be created from one or more 3D structures
represented in Protein Data Bank, or pdb, format (see
http://www.rcsb.org/pdb for details).

StructureIO objects allow access to a variety of related Bio:Structure
objects. An Entry object consist of one or more Model objects, which
in turn consist of one or more Chain objects. A Chain is composed of
Residue objects, which in turn consist of Atom objects. There's a
wealth of methods, here are just a few:

  $structio = Bio::Structure::IO->new( -file => "1XYZ.pdb");
  $struc = $structio->next_structure; # returns an Entry object
  $pseq = $struc->seqres;    # returns a PrimarySeq object, thus
  $pseq->subseq(1,20);              # returns a sequence string
  @atoms = $struc->get_atoms($res); # Atom objects, given a Residue
  @xyz = $atom->xyz;                # the 3D coordinates of the atom

These lines show how one has access to a number of related objects and methods.
For examples of typical usage of these modules, see the scripts in the
examples/structure subdirectory. Also see L<Bio::Structure::IO>, 
L<Bio::Structure::Entry>, L<Bio::Structure::Model>,
L<Bio::Structure::Chain>, L<Bio::Structure::Residue>, and
L<Bio::Structure::Atom> for more information.

=head2  III.9.2 Tree objects and phylogenetic trees (Tree::Tree,
TreeIO, PAML)

Bioperl Tree objects can store data for all kinds of computer trees
and are intended especially for phylogenetic trees.  Nodes and
branches of trees can be individually manipulated.  The TreeIO object
is used for stream I/O of tree objects.  Currently only phylip/newick
tree format is supported.  Sample code might be:

  $treeio = new Bio::TreeIO( -format => 'newick', -file   => $treefile);
  $tree = $treeio->next_tree;   # get the tree
  @nodes = $tree->get_nodes;    # get all the nodes
  $tree->get_root_node()->each_Descendent();  # get descendents of root node

See L<Bio::TreeIO> and L<Bio::Tree::Tree> for details.

Using the Bio::Tools::Phylo::PAML module one can also parse the
results of the PAML tree-building programs codeml, baseml, basemlg,
codemlsites and yn00. See L<Bio::Tools::Phylo::PAML> for details.

=head2 III.9.3 Map objects for manipulating genetic maps (Map::MapI,
MapIO)

Bioperl map objects can be used to describe any type of biological map
data including genetic maps, STS maps etc.  Map I/O is performed with
the MapIO object which works in a similar manner to the SeqIO,
SearchIO and similar I/O objects described previously. In principle,
Map I/O with various map data formats can be performed.  However
currently only "mapmaker" format is supported.  Manipulation of
genetic map data with Bioperl Map objects might look like this:

  $mapio = new Bio::MapIO( '-format' => 'mapmaker', '-file' => $mapfile);
  $map = $mapio->next_map;  # get a map
  $maptype =  $map->type ;
  foreach  $marker ( $map->each_element ) {
    $marker_name =  $marker->name ;  # get the name of each map marker
  }

See L<Bio::MapIO> and L<Bio::Map::SimpleMap> for more information.

=head2 III.9.4 Bibliographic objects for querying bibliographic databases (Biblio)

Bio::Biblio objects are used to query bibliographic databases, such as MEDLINE.
The associated modules are built to work with OpenBQS-compatible databases
(see http://industry.ebi.ac.uk/openBQS). A Bio::Biblio object can execute a query
like:

  my $collection = $biblio->find ('brazma', 'authors');
  while ( $collection->has_next ) {
      print $collection->get_next;
  }

See L<Bio::Biblio> or the scripts/biblio/biblio.PLS script for details.


=for html <A NAME ="iii.9.5"></A>

=head2 III.9.5 Graphics objects for representing sequence objects as images (Graphics)

A user may want to represent Seq objects and their SeqFeatures graphically. The
Bio::Graphics::* modules use Perl's GD.pm module to create a PNG or GIF image
given the SeqFeatures (Section L<"III.7.1">) contained within a Seq object.

These modules contain numerous methods to dictate the sizes, colors,
labels, and line formats within the image. For information see the
excellent Graphics-HOWTO at www.bioperl.org/HOWTOs/ or in the docs/howto 
subdirectory. Additional documentation can be found at L<Bio::Graphics>, 
L<Bio::Graphics::Panel>, and in the examples/graphics/render_sequence.pl 
script.

=head2 III.10 Bioperl alphabets

Bioperl modules use the standard extended single-letter genetic
alphabets to represent nucleotide and amino acid sequences.

In addition to the standard alphabet, the following symbols
are also acceptable in a biosequence:

 ?  (a missing nucleotide or amino acid)
 -  (gap in sequence)


=head2 III.10.1 Extended DNA / RNA alphabet

 (includes symbols for nucleotide ambiguity)
 ------------------------------------------
 Symbol       Meaning      Nucleic Acid
 ------------------------------------------
  A            A           Adenine
  C            C           Cytosine
  G            G           Guanine
  T            T           Thymine
  U            U           Uracil
  M          A or C
  R          A or G
  W          A or T
  S          C or G
  Y          C or T
  K          G or T
  V        A or C or G
  H        A or C or T
  D        A or G or T
  B        C or G or T
  X      G or A or T or C
  N      G or A or T or C


 IUPAC-IUB SYMBOLS FOR NUCLEOTIDE NOMENCLATURE:
   Cornish-Bowden (1985) Nucl. Acids Res. 13: 3021-3030.


=head2 III.10.2 Amino Acid alphabet

 ------------------------------------------
 Symbol   Meaning
 ------------------------------------------
 A        Alanine
 B        Aspartic Acid, Asparagine
 C        Cystine
 D        Aspartic Acid
 E        Glutamic Acid
 F        Phenylalanine
 G        Glycine
 H        Histidine
 I        Isoleucine
 K        Lysine
 L        Leucine
 M        Methionine
 N        Asparagine
 P        Proline
 Q        Glutamine
 R        Arginine
 S        Serine
 T        Threonine
 V        Valine
 W        Tryptophan
 X        Unknown
 Y        Tyrosine
 Z        Glutamic Acid, Glutamine
 *        Terminator

   IUPAC-IUP AMINO ACID SYMBOLS:
   Biochem J. 1984 Apr 15; 219(2): 345-373
   Eur J Biochem. 1993 Apr 1; 213(1): 2

=for html <A NAME ="IV."></A>

=head1 IV.  Auxilliary Bioperl Libraries (Bioperl-run, Bioperl-db, etc.)

=for html <A NAME ="iv.1"></A>

=head2 IV.1 Using the Bioperl Auxilliary Libraries

Beyond the bioperl "core" distribution which you get with the
"minimal" installation, bioperl contains numerous other modules in
so-called auxilliary libraries.  These auxilliary libraries include
bioperl-run, bioperl-db, bioperl-pipeline, bioperl-microarray and
bioperl-ext among others.  Generally, modules are placed in an
auxilliary library if either:

=over 4

=item *

the module requires the installation of additional non-standard
external programs or modules, or

=item *

the module is perceived to be of interest to only a small percentage
of the bioinformatics community

=back

However there are exceptions and it is not always obvious whether a
given module will be found in the "core" or in an auxiliary library.

At present, modules in the auxilliary packages can be obtained only by
means of the CVS system. To browse through the auxilliary libraries and
to obtain the download files, go to:

http://cvs.bioperl.org/cgi-bin/viewcvs/viewcvs.cgi/?cvsroot=bioperl

Generally CVS packages are not as well tested as the released core
library.  Consequently after downloading and running:

  $ perl Makefile.PL
and
  $ make
one should always run:
  $make test
before using the packages. Even if "make test" runs successfully. it
may be safer to _not_ run "make install" and instead to include
the library with the auxilliary modules (say in
/home/peter/auxmodules) by adding this line to each of your scripts:

use lib '/home/peter/auxmodules';

or by adding a "switch" to your invocation of perl on the command
line, e,g,:

  $perl -I/home/peter/auxmodules myscript.pl

Once the auxiliary library has been installed in this manner, the
modules can be used in exactly the same manner as if they were in the
bioperl core.

=for html <A NAME ="iv.2"></A>

=head2 IV.2 Running programs (Bioperl-run, Bioperl-ext)

It possible to run various external (to Bioperl) sequence alignment
and sequence manipulation programs via a perl interface using bioperl.
However in most cases this requires having the bioperl-run auxilliary
library (some cases may require bioperl-ext). Prior to bioperl release
1.2, many of these features were available within the bioperl "core"
release.  However currently some of the required modules have been
transferred out of the core library. Some of the more commonly used of
these modules are described in this section.

=for html <A NAME ="iv.2.2"></A>

=head2 IV.2.1 Sequence manipulation using the Bioperl EMBOSS and PISE interfaces

EMBOSS (European Molecular Biology Open Source Software) is an extensive
collection of sequence analysis programs written in the C
programming language, from http://www.uk.embnet.org/Software/EMBOSS.
There are a number of algorithms in EMBOSS that are not found in "Bioperl
proper" (eg. calculating DNA melting temperature, finding repeats,
identifying prospective antigenic sites) so if you cannot find
the function you want in bioperl you might be able to find it in EMBOSS.

EMBOSS programs are usually called from the command line but the
bioperl-run auxilliary library provides a Perl "wrapper" for EMBOSS
function calls so that they can be executed from within a Perl script.
Of course, the EMBOSS package as well as the bioperl-run must be
installed for the Bioperl wrapper to function.

In the future, it is planned that Bioperl EMBOSS objects will return
appropriate Bioperl objects to the calling script in addition to
generating standard EMBOSS reports.  This functionality is
being initially implemented with the EMBOSS sequence alignment
programs, so that they will return SimpleAlign objects in a manner
similar to the way the Bioperl-run modules TCoffee.pm and Clustalw.pm
work (see section L<"III.5"> for a discussion of SimpleAlign).

An example of the Bioperl EMBOSS wrapper where a file is returned
would be:

  $factory = new Bio::Factory::EMBOSS;
  $compseqapp = $factory->program('compseq');
  %input = ( -word     => 4,
	     -sequence => $seqObj,
	     -outfile  => $compseqoutfile );
  $compseqapp->run(\%input);
  $seqio = Bio::SeqIO->new( -file => $compseqoutfile ); # etc...

Note that a Seq object was used as input. The EMBOSS object can also
accept a file name as input, eg

  -sequence => "inputfasta.fa"

Some EMBOSS programs will return strings, others will create files
that can be read directly using Bio::SeqIO (section L<"III.2.1">), as
in the example above. It's worth mentioning that as another way to
align sequences with bioperl one can run the Smith-Waterman algorithm
in the EMBOSS program 'water'.  This can produce an output file that
bioperl can read in with the AlignIO system

  use Bio::AlignIO;
  my $in = new Bio::AlignIO(-format => 'emboss', -file => 'filename');
  my $aln = $in->next_aln();

The Pise interface is another approach to extending Bioperl's
sequence manipulation capabilities by using standard bioinformatics
programs.  To use EMBOSS programs within Bioperl you need to
have EMBOSS locally installed (in addition to having the auxiliary
bioperl-run library installed). In contrast, with Pise you only need 
install bioperl-run, since  you run the actual analysis programs over 
the internet at the Pise site. Advantages of Pise include not having
to load additional programs locally and having access to a wider
library of programs (Pise includes EMBOSS and many other programs as
well.) Howerver Pise has the disadvantages of needing an internet
connection, lower performance, decreased security, etc. stemming form
running programs over the net.  For more information on the Bioperl
Pise interface see:  http://www-alt.pasteur.fr/~letondal/Pise/.

=for html <A NAME ="iv.2.2"></A>

=head2 IV.2.2 Aligning 2 sequences with Blast using  bl2seq and AlignIO

As an alternative to Smith-Waterman, two sequences can also be aligned
in Bioperl using the bl2seq option of Blast within the StandAloneBlast
object.  To get an alignment - in the form of a SimpleAlign object -
using bl2seq, you need to parse the bl2seq report with the Bio::AlignIO
file format reader as follows:

  $factory = Bio::Tools::Run::StandAloneBlast->new('outfile' => 'bl2seq.out');
  $bl2seq_report = $factory->bl2seq($seq1, $seq2);
  # Use AlignIO.pm to create a SimpleAlign object from the bl2seq report
  $str = Bio::AlignIO->new('-file '=>'bl2seq.out',
                           '-format' => 'bl2seq');
  $aln = $str->next_aln();

=for html <A NAME ="iv.2.3"></A>

=head2 IV.2.3 Aligning multiple sequences (Clustalw.pm, TCoffee.pm)

For aligning multiple sequences (ie two or more), bioperl offers a
perl interface to the bioinformatics-standard clustalw and tcoffee
programs.  Clustalw has been a leading program in global multiple
sequence alignment (MSA) for several years.  TCoffee is a relatively
recent program - derived from clustalw - which has been shown to
produce better results for local MSA.

To use these capabilities, the clustalw and/or tcoffee programs
themselves need to be installed on the host system.  In addition, the
environmental variables CLUSTALDIR and TCOFFEEDIR need to be set to
the directories containg the executables.  See section L<"I.4"> and the
L<Bio::Tools::Run::Alignment::Clustalw> and
L<Bio::Tools::Run::Alignment::TCoffee> for information on downloading
and installing these programs.

From the user's perspective, the bioperl syntax for calling
Clustalw.pm or TCoffee.pm is almost identical.  The only differences
are the names of the modules themselves appearing in the initial "use"
and constructor statements and the names of the some of the individual
program options and parameters.

In either case, initially, a "factory object" must be created. The
factory may be passed most of the parameters or switches of the
relevant program.  In addition, alignment parameters can be changed
and/or examined after the factory has been created.  Any parameters
not explicitly set will remain as the underlying program's
defaults. Clustalw.pm/TCoffee.pm output is returned in the form of a
SimpleAlign object.  It should be noted that some Clustalw and TCoffee
parameters and features (such as those corresponding to tree
production) have not been implemented yet in the Perl interface.

Once the factory has been created and the appropriate parameters set,
one can call the method align() to align a set of unaligned sequences,
or profile_align() to add one or more sequences or a second alignment
to an initial alignment.  Input to align() consists of a set of
unaligned sequences in the form of the name of file containing the
sequences or a reference to an array of Seq objects. Typical
syntax is shown below. (We illustrate with Clustalw.pm, but the same
syntax - except for the module name - would work for TCoffee.pm)

  use Bio::Tools::Run::Alignment::Clustalw;
  @params = ('ktuple' => 2, 'matrix' => 'BLOSUM');
  $factory = Bio::Tools::Run::Alignment::Clustalw->new(@params);
  $ktuple = 3;
  $factory->ktuple($ktuple);  # change the parameter before executing
  $seq_array_ref = \@seq_array;
      # where @seq_array is an array of Bio::Seq objects
  $aln = $factory->align($seq_array_ref);

Clustalw.pm/TCoffee.pm can also align two (sub)alignments to each
other or add a sequence to a previously created alignment by using the
profile_align method. For further details on the required syntax and
options for the profile_align method, the user is referred to
L<Bio::Tools::Run::Alignment::Clustalw> and
L<Bio::Tools::Run::Alignment::TCoffee>. The user is also
encouraged to examine the script clustalw.pl in the examples/align 
directory.

=for html <A NAME ="iv.2.4"></A>

=head2    IV.2.4 Aligning 2 sequences with Smith-Waterman (pSW)

The Smith-Waterman (SW) algorithm is the standard method for producing
an optimal local alignment of two sequences.  Bioperl supports the
computation of SW alignments via the pSW object with the auxilliary
bioperl-ext library. Note that pSW only supports the alignment of
protein sequences, not nucleotide.

The SW algorithm itself is implemented in C and incorporated into
bioperl using an XS extension. This has significant efficiency
advantages but means that pSW will B<not> work unless you have
compiled the bioperl-ext auxiliary library.  If you have compiled the
bioperl-ext package, usage is simple, where the method align_and_show
displays the alignment while pairwise_alignment produces a (reference
to) a SimpleAlign object.

  use Bio::Tools::pSW;
  $factory = new Bio::Tools::pSW( '-matrix' => 'blosum62.bla',
  				  '-gap' => 12,
                                  '-ext' => 2, );
  $factory->align_and_show($seq1, $seq2, STDOUT);
  $aln = $factory->pairwise_alignment($seq1, $seq2);

SW matrix, gap and extension parameters can be adjusted as shown.
Bioperl comes standard with blosum62 and gonnet250 matrices.  Others
can be added by the user.  For additional information on accessing the
SW algorithm via pSW see the script psw.pl in the examples/tools
directory and the documentation in L<Bio::Tools::pSW>.

=for html <A NAME ="iv.3"></A>

=head2 IV.3 bioperl-db

The Bioperl-db package is intended to enable the easy access and
manipulation of biology relational databases via a perl
interface. Obviously it requires having adminsitrative access to a
relational database.  Currently the bioperl-db interface is
implemented primarily to support databases in the Mysql format
(http://www.mysql.com). More details on bioperl-db can be found in the
bioperl-db CVS directory at
http://cvs.bioperl.org/cgi-bin/viewcvs/viewcvs.cgi/bioperl-db/?cvsroot=bioperl.
It is worth mentioning that most of the bioperl objects mentioned
above map directly to tables in the bioperl-db schema. Therefore
object data such as sequences, their features, and annotations can be
easily loaded into the databases, as in

  $loader->store($newid,$seqobj)

Similarly one can query the database in a variety of ways and retrieve
arrays of Seq objects. See biodatabases.pod,
L<Bio::DB::SQL::SeqAdaptor>, L<Bio::DB::SQL::QueryConstraint>, and
L<Bio::DB::SQL::BioQuery> for examples. The README file in the
bioperl-db package has a helpful overview of the approach used in
bioperl-db.

=for html <A NAME ="iv.4"></A>

=head2 IV.4 Other Bioperl auxilliary libraries

There a several other auxilliary libraries in the bioperl CVS
repository including bioperl-microarray, bioperl-gui,
bioperl-pipeline, bioperl-das-client and bioperl-corba-client. They
are typically for specialized uses and/or require multiple external
programs to run and/or are still pretty new and undeveloped. But if
you have a need for any of these capabailities, it is easy to take a
look at them at:
http://cvs.bioperl.org/cgi-bin/viewcvs/viewcvs.cgi/?cvsroot=bioperl
and see if they might be of use to you.

=for html <A NAME ="v.1"></A>

=head2 V.1 Appendix: Finding out which methods are used by which
Bioperl Objects

At numerous places in the tutorial, the reader is directed to the
"documentation included with each of the modules."  As was mentioned in
the introduction, it is sometimes not easy in perl to determine the
appropriate documentation to look for, because objects inherit methods
from other objects (and the relevant documentation will be stored in the
object from which the method was inherited.)

For example, say you wanted to find documentation on the "parse" method
of the object Genscan.pm.  You would not find this documentation in
the code for Genscan.pm, but rather in the code for AnalysisResult.pm
from which Genscan.pm inherits the parse method!

So how would you know to look in AnalysisResult.pm
for this documentation? The easy way is to use the special function
"option 100" in the bptutorial script. Specifically if you run:

 > perl -w bptutorial.pl 100 Bio::Tools::Genscan

you will receive the following output:

 ***Methods for Object Bio::Tools::Genscan ********

 Methods taken from package Bio::Root::IO
 catfile   close   gensym   new   qualify   qualify_to_ref
 rmtree   tempdir   tempfile   ungensym

 Methods taken from package Bio::Root::RootI
 DESTROY   stack_trace   stack_trace_dump   throw   verbose   warn

 Methods taken from package Bio::SeqAnalysisParserI
 carp   confess   croak   next_feature

 Methods taken from package Bio::Tools::AnalysisResult
 analysis_date   analysis_method   analysis_method_version   analysis_query   analysis_subject   parse

 Methods taken from package Bio::Tools::Genscan
 next_prediction

From this output, it is clear exactly from which object each method
of Genscan.pm is taken, and, in particular that "parse" is
taken from the package Bio::Tools::AnalysisResult.

With this approach you can easily determine the source of any method
in any bioperl object.

=head2 V.2 Appendix: Tutorial demo scripts

The following scripts demonstrate many of the features of bioperl. To
run all the core demos, run:

 > perl -w  bptutorial.pl 0

To run a subset of the scripts do

 > perl -w  bptutorial.pl

and use the displayed help screen.

It may be best to start by just running one or two demos at a time. For 
example, to run the basic sequence manipulation demo, do:

 > perl -w  bptutorial.pl 1

Some of the later demos require that you have an internet connection
and/or that you have an auxilliary bioperl library and/or external
cpan module and/or external program installed.  They may also fail if
you are not running under Linux or Unix.  In all of these cases, the
script should fail "gracefully" simply saying the demo is being
skipped.  However if the script "crashes", simply run the other demos
individually (and perhaps send an email to bioperl-l@bioperl.org
detailing the problem :-).

=cut

#!/usr/bin/perl

# PROGRAM  : bptutorial.pl
# PURPOSE  : Demonstrate various uses of the bioperl package
# AUTHOR   : Peter Schattner schattner@alum.mit.edu
# CREATED  : Dec 15 2000
# REVISION : $Id$

use strict;
use Bio::SimpleAlign;
use Bio::AlignIO;
use Bio::SeqIO;
use Bio::Seq;
my $outputfh = *STDOUT;

# subroutine references

my ($access_remote_db, $index_local_db, $fetch_local_db,
    $sequence_manipulations, $seqstats_and_seqwords,
    $restriction_and_sigcleave, $other_seq_utilities, $run_remoteblast,
    $run_standaloneblast,  $blast_parser, $bplite_parsing, $hmmer_parsing,
    $run_clustalw_tcoffee, $run_psw_bl2seq, $simplealign,
    $gene_prediction_parsing, $sequence_annotation, $largeseqs,
    $run_tree, $run_map, $run_struct, $run_perl, $searchio_parsing,
    $liveseqs, $demo_variations, $demo_xml, $display_help, $bpinspect1 );

# global variable file names.  Edit these if you want to try
#out a tutorial script on a different file

 Bio::Root::IO->catfile("t","data","ecolitst.fa");

my $dna_seq_file = Bio::Root::IO->catfile("t","data","dna1.fa");      # used in $sequence_manipulations
my $amino_seq_file = Bio::Root::IO->catfile("t","data","cysprot1.fa");  # used in $other_seq_utilities and in $run_perl
                                       #and $sequence_annotation
my $blast_report_file = Bio::Root::IO->catfile("t","data","blast.report");   # used in $blast_parser
my $bp_parse_file1 = Bio::Root::IO->catfile("t","data","blast.report");       # used in $bplite_parsing
my $bp_parse_file2 = Bio::Root::IO->catfile("t","data","psiblastreport.out"); # used in $bplite_parsing
my $bp_parse_file3 = Bio::Root::IO->catfile("t","data","bl2seq.out");        # used in $bplite_parsing
my $unaligned_amino_file = Bio::Root::IO->catfile("t","data","cysprot1a.fa"); # used in $run_clustalw_tcoffee
my $aligned_amino_file = Bio::Root::IO->catfile("t","data","testaln.pfam");    # used in $simplealign

# other global variables
my (@runlist, $n );


##############################
#  display_help
$display_help = sub {


# Prints usage information for tutorial script.

    print STDERR <<"QQ_PARAMS_QQ";

The following numeric arguments can be passed to run the corresponding demo-script.
1  => sequence_manipulations 
2  => seqstats_and_seqwords 
3  => restriction_and_sigcleave 
4  => other_seq_utilities 
5  => run_perl
6  => searchio_parsing 
7  => bplite_parsing 
8  => hmmer_parsing 
9  => simplealign 
10 => gene_prediction_parsing 
11 => access_remote_db 
12 => index_local_db 
13 => fetch_local_db    (NOTE: needs to be run with demo 12)
14 => sequence_annotation 
15 => largeseqs 
16 => liveseqs 
17 => run_struct
18 => demo_variations 
19 => demo_xml 
20 => run_tree 
21 => run_map
22 => run_remoteblast 
23 => run_standaloneblast 
24 => run_clustalw_tcoffee 
25 => run_psw_bl2seq 

In addition the argument "100" followed by the name of a single
bioperl object will display a list of all the public methods
available from that object and from what object they are inherited.

Using the parameter "0" will run all the tests that do not require 
external programs (i.e. tests 1 to 22). 
Using any other argument (or no argument) will run this display.

So typical command lines might be:
To run all core demo scripts:
 > perl -w  bptutorial.pl 0
or to just run the local indexing demos:
 > perl -w  bptutorial.pl 12 13
or to list all the methods available for object Bio::Tools::SeqStats -
 > perl -w  bptutorial.pl 100 Bio::Tools::SeqStats

QQ_PARAMS_QQ

exit(0);
};


## Note: "main" routine is at very end of script

#################################################
#   access_remote_db():
#

$access_remote_db = sub {
    eval { 
	require Bio::DB::GenBank;
    };
    if($@ ) {
	warn "LWP::UserAgent or HTTP::Request or IO::String doesn't appear to be loaded, cannot run the access_remote_db method";
	return 0;
    }
    print $outputfh "Beginning remote database access example... \n";

    # III.2.1 Accessing remote databases (Bio::DB::GenBank, etc)

    my ($gb, $seq1, $seq2, $seq1_id, $seqobj, $seq2_id,$seqio);

    eval {
        $gb = new Bio::DB::GenBank();
        $seq1 = $gb->get_Seq_by_id('MUSIGHBA1');
    };

if ($@ || !$seq1) {
    warn "Warning: Couldn't connect to Genbank with Bio::DB::GenBank.pm!\nProbably no network access.\n Skipping method 'access_remote_db'.\n";
    return 0;
}
    $seq1_id =  $seq1->display_id();
    print $outputfh "seq1 display id is $seq1_id \n";
    $seq2 = $gb->get_Seq_by_acc('AF303112');
    $seq2_id =  $seq2->display_id();
    print $outputfh "seq2 display id is $seq2_id \n";
    $seqio = $gb->get_Stream_by_id([ qw(2981014 J00522 AF303112)]);
    $seqobj = $seqio->next_seq();
    print $outputfh "Display id of first sequence in stream is ", $seqobj->display_id()  ,"\n";
    return 1;
} ;


#################################################
#  index_local_db ():
#

$index_local_db = sub {

    use Bio::Index::Fasta; # using fasta file format
    use strict; # some users have reported that this is required

    my ( $Index_File_Name, $inx1, $inx2, $id, $dir, $key,
         $keyfound, $seq, $indexhash);
    print $outputfh "\nBeginning indexing local_db example... \n";
    print $outputfh "This subroutine unlikely to run unless OS = unix\n";


    # III.2.2 Indexing and accessing local databases
    # (Bio::Index::*,  bpindex.pl,  bpfetch.pl)

    # first create the index
    $Index_File_Name = 'bptutorial.indx';

    eval { use Cwd; $dir = cwd; };
    # CWD not installed, revert to unix behavior, best we can do
    if( $@) { $dir = `pwd`;}


    $inx1 = Bio::Index::Fasta->new
        ('-FILENAME' => "$dir/$Index_File_Name",
         '-write_flag' => 1);

    $inx1->make_index(Bio::Root::IO->catfile("$dir","t","data","multifa.seq") );
    $inx1->make_index(Bio::Root::IO->catfile("$dir","t","data","seqs.fas") );


#    $inx1->make_index("$dir/t/data/multifa.seq");
#    $inx1->make_index("$dir/t/data/seqs.fas");
    print $outputfh "Finished indexing local_db example... \n";

    return 1;
} ;


#################################################
#  fetch_local_db ():
#

$fetch_local_db = sub {

    use Bio::Index::Fasta; # using fasta file format
    use strict; # some users have reported that this is required

    my ( $Index_File_Name, $inx2, $id, $dir, $key,
         $keyfound, $seq, $indexhash,$value );
    print $outputfh "\nBeginning retrieving local_db example... \n";

    # then retrieve some files
    #$Index_File_Name = shift;
    $Index_File_Name = 'bptutorial.indx';

    eval { use Cwd; $dir = cwd; };
    # CWD not installed, revert to unix behavior, best we can do
    if( $@) { $dir = `pwd`;}

    eval {$inx2 = Bio::Index::Abstract->new
	      ('-FILENAME'  => Bio::Root::IO->catfile("$dir","$Index_File_Name") ); };
   if( $@ ) {
      print STDERR "Cannot find local index file $Index_File_Name\n";
      print STDERR "Perhaps you didn't run the index_local_db demo? \n";
      print STDERR "Skipping fetch_local_db example.\n\n";
      return 0;
    }      

    $indexhash = $inx2->db();
    $keyfound = "";
    while ( ($key,$value) = each %$indexhash ) {
        if ( $key =~ /97/ ) { $keyfound = 'true'; $id = $key; last; }
    }
    if ($keyfound) {
        $seq = $inx2->fetch($id);
        print $outputfh "Sequence ", $seq->display_id(),
        " has length  ", $seq->length()," \n";
    }

    unlink "$dir/$Index_File_Name" ;

    return 1;
} ;



#################################################
# sequence_manipulations  ():
#

$sequence_manipulations = sub {

    my ($infile, $in, $out, $seqobj);
    $infile = $dna_seq_file;

    print $outputfh "\nBeginning sequence_manipulations and SeqIO example... \n";


    # III.3.1 Transforming sequence files (SeqIO)

    $in  = Bio::SeqIO->new('-file' => $infile ,
                           '-format' => 'Fasta');
    $seqobj = $in->next_seq();

    # perl "tied filehandle" syntax is available to SeqIO,
    # allowing you to use the standard <> and print operations
    # to read and write sequence objects, eg:
    #$out = Bio::SeqIO->newFh('-format' => 'EMBL');

    $out = Bio::SeqIO->newFh('-format'   => 'fasta',
			     '-noclose'  => 1,
			     '-fh'       => $outputfh);

    print $outputfh "First sequence in fasta format... \n";
    print $out $seqobj;

    # III.4 Manipulating individual sequences

    # The following methods return strings


    print $outputfh "Seq object display id is ",
    $seqobj->display_id(), "\n"; # the human read-able id of the sequence
    print $outputfh "Sequence is ",
    $seqobj->seq()," \n";        # string of sequence
    print $outputfh "Sequence from 5 to 10 is ",
    $seqobj->subseq(5,10)," \n"; # part of the sequence as a string
    print $outputfh "Acc num is ",
    $seqobj->accession_number(), " \n"; # when there, the accession number
    print $outputfh "Alphabet is ",
    $seqobj->alphabet(), " \n";    # one of 'dna','rna','protein'
    print $outputfh "Primary id is ", $seqobj->primary_seq->primary_id()," \n";
    # a unique id for this sequence irregardless
    #print $outputfh "Primary id is ", $seqobj->primary_id(), " \n";
    # a unique id for this sequence irregardless
    # of its display_id or accession number

    # The following methods return an array of  Bio::SeqFeature objects
    $seqobj->top_SeqFeatures; # The 'top level' sequence features
    $seqobj->all_SeqFeatures; # All sequence features, including sub
    # seq features

    # The following methods returns new sequence objects,
    # but do not transfer features across
    # truncation from 5 to 10 as new object
    print $outputfh "Truncated Seq object sequence is ",
    $seqobj->trunc(5,10)->seq(), " \n";
    # reverse complements sequence
    print $outputfh "Reverse complemented sequence 5 to 10  is ",
    $seqobj->trunc(5,10)->revcom->seq, "  \n";
    # translation of the sequence
    print $outputfh "Translated sequence 6 to 15 is ",
    $seqobj->translate->subseq(6,15), " \n";


    my $c = shift;
    $c ||= 'ctgagaaaataa';

    print $outputfh "\nBeginning 3-frame and alternate codon translation example... \n";

    my $seq = new Bio::PrimarySeq('-SEQ' => $c,
                                  '-ID' => 'no.One');
    print $outputfh "$c translated using method defaults   : ",
    $seq->translate->seq, "\n";

    # Bio::Seq uses same sequence methods as PrimarySeq
    my $seq2 = new Bio::Seq('-SEQ' => $c, '-ID' => 'no.Two');
    print $outputfh "$c translated as a coding region (CDS): ",
    $seq2->translate(undef, undef, undef, undef, 1)->seq, "\n";

    print $outputfh "\nTranslating in all six frames:\n";
    my @frames = (0, 1, 2);
    foreach my $frame (@frames) {
        print $outputfh  " frame: ", $frame, " forward: ",
        $seq->translate(undef, undef, $frame)->seq, "\n";
        print $outputfh  " frame: ", $frame, " reverse-complement: ",
        $seq->revcom->translate(undef, undef, $frame)->seq, "\n";
    }

    print $outputfh "Translating with all codon tables using method defaults:\n";
    my @codontables = qw( 1 2 3 4 5 6 9 10 11 12 13 14 15 16 21 );
    foreach my $ct (@codontables) {
        print $outputfh $ct, " : ",
        $seq->translate(undef, undef, undef, $ct)->seq, "\n";
    }

    return 1;
} ;

#################################################                                      ;
#  seqstats_and_seqwords ():
#

$seqstats_and_seqwords = sub {

    use Bio::Tools::SeqStats;
    use Bio::Tools::SeqWords;

    print $outputfh "\nBeginning seqstats_and_seqwords example... \n";


    my ($seqobj, $weight, $monomer_ref, $codon_ref,
        $seq_stats, $words, $hash);
    $seqobj = Bio::Seq->new
        ('-seq'=>'ACTGTGGCGTCAACTGACTGTGGCGTCAACTGACTGTGGGCGTCAACTGACTGTGGCGTCAACTG',
         '-alphabet'=>'dna',
         '-id'=>'test');


    # III.4.1 Obtaining basic sequence statistics- MW,
    # residue &codon frequencies(SeqStats, SeqWord)


    $seq_stats  =  Bio::Tools::SeqStats->new($seqobj);
    $weight = $seq_stats->get_mol_wt();
    $monomer_ref = $seq_stats->count_monomers();
    $codon_ref = $seq_stats->count_codons();  # for nucleic acid sequence
    print $outputfh "Sequence \'test\' is ", $seqobj->seq(), " \n";
    print $outputfh "Sequence ", $seqobj->id()," molecular weight is $$weight[0] \n";
    # Note, $weight is an array reference
    print $outputfh "Number of A\'s in sequence is $$monomer_ref{'A'} \n";
    print $outputfh "Number of ACT codon\'s in sequence is $$codon_ref{'ACT'} \n";

    $words = Bio::Tools::SeqWords->new($seqobj);
    $hash = $words->count_words(6);
    print $outputfh "Number of 6-letter \'words\' of type ACTGTG in",
    " sequence is $$hash{'ACTGTG'} \n";

    return 1;
    } ;


#################################################
# restriction_and_sigcleave  ():
#

$restriction_and_sigcleave = sub {

    use Bio::Tools::RestrictionEnzyme;
    use Bio::Tools::Sigcleave;

    my ($re, $re1, $re2, @sixcutters, @fragments1,
        @fragments2, $seqobj, $dna);
    print $outputfh "\nBeginning restriction enzyme example... \n";

    # III.4.4 Identifying restriction enzyme sites (RestrictionEnzyme)

    $dna = 'CCTCCGGGGACTGCCGTGCCGGGCGGGAATTCGCCATGGCGACCCTGGAAAAGCTGATATCGAAGGCCTTCGA';

    # Build sequence and restriction enzyme objects.
    $seqobj = new Bio::Seq('-ID'  => 'test_seq',
                           '-SEQ' =>$dna);

    #$re  = new Bio::Tools::RestrictionEnzyme();
    $re  = new Bio::Tools::RestrictionEnzyme('-name'=>'EcoRI');
    @sixcutters = $re->available_list(6);

    print $outputfh "The following 6-cutters are available\n";
    print $outputfh join(" ",@sixcutters),"\n";

    $re1  = new Bio::Tools::RestrictionEnzyme('-name'=>'EcoRI');
    @fragments1 =  $re1->cut_seq($seqobj);
    #$seqobj is the Seq object for the dna to be cut

    print $outputfh "\nThe sequence of " . $seqobj->display_id . " is " .
    $seqobj->seq . "\n";
    print $outputfh "When cutting " . $seqobj->display_id() . " with " .
    $re1->seq->id . " the initial fragment is\n" . $fragments1[0];

    $re2 = new Bio::Tools::RestrictionEnzyme
        ('-NAME' =>'EcoRV--GAT^ATC',
         '-MAKE' =>'custom');
    @fragments2 =  $re2->cut_seq($seqobj);

    print $outputfh "\nWhen cutting ", $seqobj->display_id(),
    " with ", $re2->seq->id;
    print $outputfh " the second fragment is\n", $fragments2[1], " \n";

    # III.4.7 Identifying amino acid cleavage sites (Sigcleave)

    print $outputfh "\nBeginning  signal cleaving site location example... \n";

    my ( $sigcleave_object, %raw_results , $location,
         $formatted_output, $protein, $in, $seqobj2);

    $protein = 
"MKVILLFVLAVFTVFVSSRGIPPEEQSQFLEFQDKFNKKYSHEEYLERFEIFKSNLGKIEELNLIAINHKADTKFGVNKFADLSSDEFKNYYLNNKEAIFTDDLPVADYLDDEFINSIPTAFDWRTRGAVTPVKNQGQCGSCWSFSTTGNVEGQHFISQNKLVSLSEQNLVDCDHECMEYEGEEACDEGCNGGLQPNAYNYIIKNGGIQTESSYPYTAETGTQCNFNSANIGAKISNFTMIPKNETVMAGYIVSTGPLAIAADAVEWQFYIGGVFDIPCNPNSLDHGILIVGYSAKNTIFRKNMPYWIVKNSWGADWGEQGYIYLRRGKNTCGVSNFVSTSII";
    $formatted_output = "";

    # Build object
    # Note that Sigcleave is passed a raw sequence
    # rather than a sequence object when it is created.
    $sigcleave_object = new Bio::Tools::Sigcleave
        ('-id' => 'test_sigcleave_seq',
         '-type' => 'amino',
         '-threshold' => 3.0,
         '-seq' => $protein);

    %raw_results      = $sigcleave_object->signals;
    $formatted_output = $sigcleave_object->pretty_print;

    print $outputfh " SigCleave \'raw results\': \n";
    foreach $location (sort keys %raw_results) {
        print $outputfh " Cleave site found at location $location ",
        "with value of $raw_results{$location} \n";
    }
    print $outputfh "\nSigCleave formatted output: \n $formatted_output \n";
    return 1;
} ;


#################################################
#  run_standaloneblast ():
#

$run_standaloneblast = sub {
    eval {require Bio::Tools::Run::StandAloneBlast; };
    if ( $@ ) {
      print STDERR "Cannot find Bio::Tools::Run::StandAloneBlast\n";
      print STDERR "You will need the bioperl-run library to run StandAlnoeBlast\n";
      print STDERR "Skipping local blast example:\n";
      return 0;
    }
    print $outputfh "\nBeginning run_standaloneblast example... \n";

    my (@params, $factory, $input, $blast_report, $blast_present,
        $database, $sbjct, $str, $seq1);

#    $database = $_[0] || 'ecoli.nt'; # user can select local nt database
    $database = $_[0] || defined $ENV{BLASTDB} ? "$ENV{BLASTDB}/ecoli.nt" : 
	"$ENV{BLASTDIR}/data/ecoli.nt"; # user can select local nt database
    #@params = ('program' => 'blastn', 'database' => 'ecoli.nt');
    @params = ('program' => 'blastn', 'database' => $database);
    $factory = Bio::Tools::Run::StandAloneBlast->new(@params);

    unless ($factory->executable('blastall')) {
        warn "blast program not found. Skipping StandAloneBlast example\n";
        return 0;
    }

    $str = Bio::SeqIO->new('-file'=> 
			   Bio::Root::IO->catfile("t","data","dna2.fa") ,
#    $str = Bio::SeqIO->new('-file'=>'t/data/dna2.fa' ,
                           '-format' => 'fasta', );
    $seq1 = $str->next_seq();

    $blast_report = $factory->blastall($seq1);
    my $result = $blast_report->next_result; 
    $sbjct = $result->next_hit;

    print $outputfh " Hit name is ", $sbjct->name, " \n";

    return 1;
} ;

#################################################
#  run_remoteblast ():
#

$run_remoteblast = sub {
    print $outputfh "\nBeginning run_remoteblast example... \n";
    eval { require Bio::Tools::Run::RemoteBlast; };

    if ( $@ ) {
      print STDERR "Cannot find Bio::Tools::Run::RemoteBlast\n";
      print STDERR "Skipping run_remoteblast example:\n";
    } else {
      my (@params, $remote_blast_object, $blast_file, $r, $rc,
         $database);

      $database =  'ecoli';
      @params = ('-prog'   => 'blastp', 
                 '-data'   => $database,
                 '-expect' => '1e-10',
                 '-readmethod' => 'BPlite' );

      $remote_blast_object = Bio::Tools::Run::RemoteBlast->new(@params);
      $blast_file = Bio::Root::IO->catfile("t","data","ecolitst.fa");
      eval {
	  $r = $remote_blast_object->submit_blast( $blast_file);
      };
      if (($r < 0) || $@)  {
	  warn "\n\n**Warning**: Couldn't connect to NCBI with Bio::Tools::Run::StandAloneBlast.pm!\nProbably no network access.\n Skipping Test\n";
	  return 0;
      }
      print $outputfh "submitted Blast job\n";
      while ( my @rids = $remote_blast_object->each_rid ) {
	  foreach my $rid ( @rids ) {
	      $rc = $remote_blast_object->retrieve_blast($rid);
	      print $outputfh "retrieving results...\n";
	      if( !ref($rc) ) {   # $rc not a reference => either error 
		  # or job not yet finished
		  if( $rc < 0 ) {
		      $remote_blast_object->remove_rid($rid);
		      print $outputfh "Error return code for BlastID code $rid ... \n";
		  }
		  sleep 5;
	      } else {
            $remote_blast_object->remove_rid($rid);
            while ( my $sbjct = $rc->nextSbjct ) {
              print $outputfh "sbjct name is ", $sbjct->name, "\n";
              while ( my $hsp = $sbjct->nextHSP ) {
                print $outputfh "score is ", $hsp->score, "\n"; 
              }
            }
          }
        }
      }
    }
  return 1;
} ;


#################################################
#  searchio_parsing ():
#



$searchio_parsing = sub {

my ($searchio, $result,$hit,$hsp);


use lib '.';
use Bio::SearchIO;
use Bio::Root::IO;

print $outputfh "\nBeginning searchio-parser example... \n";

$searchio = new Bio::SearchIO ('-format' => 'blast',
  '-file' => Bio::Root::IO->catfile('t','data','ecolitst.bls'));

$result = $searchio->next_result;

print $outputfh "Database name is ", $result->database_name , "\n";
print $outputfh "Algorithm is ", $result->algorithm , "\n";
print $outputfh "Query length used is ", $result->query_length , "\n";
print $outputfh "Kappa value is ", $result->get_statistic('kappa') , "\n";
print $outputfh "Name of matrix used is ", $result->get_parameter('matrix') , "\n";


$hit = $result->next_hit;
print $outputfh "First hit name is ", $hit->name , "\n";
print $outputfh "First hit length is ", $hit->length , "\n";
print $outputfh "First hit accession number is ", $hit->accession , "\n";

$hsp = $hit->next_hsp;
print $outputfh "First hsp query start is ", $hsp->query->start , "\n";
print $outputfh "First hsp query end is ", $hsp->query->end , "\n";
print $outputfh "First hsp hit start is ", $hsp->hit->start , "\n";
print $outputfh "First hsp hit end is ", $hsp->hit->end , "\n";

    return 1;
} ;


#################################################
#  bplite_parsing ():
#

$bplite_parsing = sub {

    use Bio::Tools::BPlite;
    my ($file1, $file2, $file3, $report,$report2 , $report3 ,
        $last_iteration, $sbjct,  $total_iterations, $hsp,
        $matches, $loop);

    print $outputfh "\nBeginning bplite, bppsilite, bpbl2seq parsing example... \n";

    ($file1, $file2, $file3) = ($bp_parse_file1,
                                $bp_parse_file2 ,$bp_parse_file3 );
    #open FH, "t/data/blast.report";
    $report = Bio::Tools::BPlite->new('-file'=>$file1);
    $sbjct = $report->nextSbjct;

    print $outputfh " Hit name is ", $sbjct->name, " \n";
    while ($hsp = $sbjct->nextHSP) {
        $hsp->score;
    }

    use Bio::Tools::BPpsilite;
    $report2 = Bio::Tools::BPpsilite->new('-file'=>$file2);
    $total_iterations = $report2->number_of_iterations;
    $last_iteration = $report2->round($total_iterations);

    # print first 10 psiblast hits
    for ($loop = 1; $loop <= 10; $loop++) {
        $sbjct =  $last_iteration->nextSbjct;
        $sbjct && print $outputfh " PSIBLAST Hit is ", $sbjct->name, " \n";
    }

    #
    use Bio::Tools::BPbl2seq;
    $report3 = Bio::Tools::BPbl2seq->new('-file'  => $file3,
                                         '-report_type' => 'BLASTP',
					 '-queryname' => "ALEU_HORVU");
    $hsp = $report3->next_feature;
    $matches = $hsp->match;
    print $outputfh " Number of Blast2seq matches for first hsp equals $matches \n";
    #

    return 1;
} ;


#################################################
#  hmmer_parsing ():
#

$hmmer_parsing = sub {

    print $outputfh "\nBeginning hmmer_parsing example \n" .
          " (note: this test may be a little slow, please be patient...) \n";

    # Parsing HMM reports
    use Bio::SearchIO;

    my $in = new Bio::SearchIO('-format' => 'hmmer',
          '-file' => Bio::Root::IO->catfile("t","data","hmmsearch.out"));
    my $res = $in->next_result;
    # get a Bio::Search::Result::HMMERResult object
    print $outputfh "The first result query name is ", $res->query_name,  "\n";
    print $outputfh "The first result HMM name is ",  $res->hmm_name, "\n";
    my $first_hit = $res->next_hit;
    print $outputfh "The name of the first hit is ", $first_hit->name, "\n";
    my $hsp = $first_hit->next_hsp;
    print $outputfh "The score of the first hsp of the first hit is ", $hsp->score, "\n";
    return 1;
} ;

#################################################
# run_clustalw_tcoffee  ():
#

$run_clustalw_tcoffee = sub {

#    use Bio::Tools::Run::Alignment::TCoffee;
#    use Bio::Tools::Run::Alignment::Clustalw;
    my (@params, $factory, $ktuple, $aln, $seq_array_ref, $strout);
    my (@seq_array, $seq, $str, $infile);

    $infile = $unaligned_amino_file;

    # Aligning multiple sequences (Clustalw.pm, TCoffee.pm)
    print $outputfh "\nBeginning run_clustalw example... \n";

    eval {require Bio::Tools::Run::Alignment::Clustalw; };
    if ( $@ ) { 
	print STDERR "Cannot find Bio::Tools::Run::Alignment::Clustalw\n";
        print STDERR "You will need the bioperl-run library to run clustalw with bioperl\n";
	print STDERR "Skipping local clustalw demo:\n";
    } else {

	# put unaligned sequences in a Bio::Seq array
	$str = Bio::SeqIO->new('-file'=> $infile,
			       '-format' => 'fasta');
	@seq_array =();
	while ($seq = $str->next_seq() ) { push (@seq_array, $seq) ;}
	$seq_array_ref = \@seq_array;
	# where @seq_array is an array of Bio::Seq objects
	@params = ('ktuple' => 2, 'matrix' => 'BLOSUM', 'quiet' => 1);
	$factory = Bio::Tools::Run::Alignment::Clustalw->new(@params);
	unless( $factory->executable ) {
	    $ktuple = 3;
	    $factory->ktuple($ktuple);  # change the parameter before executing
	    $aln = $factory->align($seq_array_ref);
	    $strout = Bio::AlignIO->newFh('-format' => 'msf',
					  '-noclose'=> 1,
					  '-fh'     => $outputfh);
	    print $outputfh "Output of clustalw alignment... \n";
	    print $strout $aln;
	} else {
	    warn "Clustalw program not found. Skipping run_clustalw example....\n";
	}
    }
    print $outputfh "\nBeginning run_tcoffee example... \n";

    eval {require Bio::Tools::Run::Alignment::TCoffee; };
    if ( $@ ) { 
	print STDERR "Cannot find Bio::Tools::Run::Alignment::TCoffee\n";
        print STDERR "You will need the bioperl-run library to run TCoffee with bioperl\n"; 
        print STDERR "Skipping local TCoffee demo:\n";
	return 0;
    }
    @params = ('ktuple' => 2, 'matrix' => 'BLOSUM', 'quiet' => 1);
    $factory = Bio::Tools::Run::Alignment::TCoffee->new(@params);
    unless( $factory->executable ) {
	$ktuple = 3;
        $factory->ktuple($ktuple);  # change the parameter before executing
        $aln = $factory->align($seq_array_ref);
        $strout = Bio::AlignIO->newFh('-format' => 'msf',
				      '-noclose'=> 1,
				      '-fh'     => $outputfh);
        print $outputfh "Output of TCoffee alignment... \n";
        print $strout $aln;
    } else {
        warn "TCoffee program not found. Skipping run_TCoffee example....\n";
    }
    return 1;
} ;

#################################################
# simplealign  ():
#

$simplealign = sub {

    my ($in, $out1,$out2, $aln, $threshold_percent, $infile);
    my ( $str, $resSlice1, $resSlice2, $resSlice3, $tmpfile);

    print $outputfh "\nBeginning simplealign and alignio example... \n";

    # III.3.2 Transforming alignment files (AlignIO)
    $infile = $aligned_amino_file;
    #$tmpfile = "test.tmp";
    $in  = Bio::AlignIO->new('-file' => $infile ,
                             '-format' => 'pfam');
    $out1 = Bio::AlignIO->newFh('-format' => 'msf',
				'-noclose'=> 1,
				'-fh'     => $outputfh);

    # while ( my $aln = $in->next_aln() ) { $out->write_aln($aln);  }
    $aln = $in->next_aln() ;
    print $outputfh "Alignment to display in msf format... \n";
    print $out1 $aln;

    $threshold_percent = 60;
    $str = $aln->consensus_string($threshold_percent) ;
    print $outputfh "Consensus string with threshold = $threshold_percent is $str\n";

    print $outputfh "The average percentage identity of the alignment is ", $aln->percentage_identity. "\n";

    my $seqname = '1433_LYCES';
    my $residue_number = 14;
    my $pos = $aln->column_from_residue_number($seqname, $residue_number);
    print $outputfh "Residue number $residue_number of the sequence $seqname in the alignment occurs at column $pos. \n";

    my $s1 = new Bio::LocatableSeq (-id => 'AAA',  -seq => 'aawtat-tn-', -start => 1, -end => 8,
                            -alphabet => 'dna' );
    my $s2 = new Bio::LocatableSeq (-id => 'BBB',  -seq => '-aaaat-tt-', -start => 1,
                            -end => 7,  -alphabet => 'dna');
    my $a = new Bio::SimpleAlign;
    $a->add_seq($s1);
    $a->add_seq($s2);

    print $outputfh "The first sequence in the dna alignment is... \t", $s1->seq , "\n";
    print $outputfh "The second sequence in the dna alignment is... \t", $s2->seq , "\n";
    my $iupac_consensus =  $a->consensus_iupac;
    print $outputfh "The IUPAC consensus of the dna alignment is... \t",$iupac_consensus  , "\n";

    return 1;
} ;

#################################################
# run_psw_bl2seq  ():
#

$run_psw_bl2seq = sub {

    #use Bio::Tools::pSW;
    #use Bio::Tools::Run::StandAloneBlast;
    my ($factory, $aln, $out1, $out2, $str, $seq1, $seq2, @params);
    print $outputfh "\nBeginning run_psw_bl2seq example... \n";
    eval { require Bio::Tools::pSW; };
    if( $@ ) {
        print STDERR "\nThe C-compiled engine for Smith Waterman alignments (Bio::Ext::Align) has not been installed.\n";
        print STDERR "Please read the install the bioperl-ext package\n";
	print STDERR "Skipping Smith-Waterman demo:\n";
    } else {

	# Aligning 2 sequences with Smith-Waterman (pSW)

	# Get protein sequences from file

	$str = Bio::SeqIO->new('-file'=>Bio::Root::IO->catfile("t","data","amino.fa") ,
			       '-format' => 'Fasta', );
	$seq1 = $str->next_seq();
	$seq2 = $str->next_seq();

	$factory = new Bio::Tools::pSW( '-matrix' => 'scripts/tools/blosum62.bla',
					'-gap' => 12,
					'-ext' => 2, );

	$factory->align_and_show($seq1,$seq2,$outputfh);
	$aln = $factory->pairwise_alignment($seq1,$seq2);

	$out1 = Bio::AlignIO->newFh('-format' => 'fasta',
				    '-noclose'=> 1,
				    '-fh'     => $outputfh);
	print $outputfh "The Smith-Waterman alignment in fasta format... \n";
	print $out1 $aln;
    }

    # Aligning 2 sequences with Blast using  bl2seq and AlignIO

    # Bl2seq testing
    # first create factory for bl2seq

    eval { require Bio::Tools::Run::StandAloneBlast; };
    if ( $@ ){
	print STDERR "Cannot find Bio::Tools::Run::StandAloneBlast module\n";
	print STDERR "You will need the bioperl-run library to run bl2seq\n";
	print STDERR "Skipping bl2seq demo:\n";
	return 0;
    }

    @params = ('outfile' => 'bl2seq.out');
    $factory = Bio::Tools::Run::StandAloneBlast->new(@params);

    unless ($factory->executable('bl2seq') ) {
        warn "\n Blast program not found. Skipping bl2seq example\n\n";
        return 0;
    }

    $factory->bl2seq($seq1, $seq2);

    # Use AlignIO.pm to create a SimpleAlign object from the bl2seq report
    $str = Bio::AlignIO->new('-file'=> 'bl2seq.out',
                             '-format' => 'bl2seq');
    $aln = $str->next_aln();

    $out2 = Bio::AlignIO->newFh('-format' => 'fasta',
				'-noclose'=> 1,
				'-fh'     => $outputfh);
    print $outputfh "The Blast 2 sequence alignment in fasta format... \n";
    print $out2 $aln;

    return 1;
} ;


#################################################
#  gene_prediction_parsing ():
#

$gene_prediction_parsing = sub {

    use Bio::Tools::Genscan;

    print $outputfh "\nBeginning genscan_result_parsing example... \n";

    my ($genscan, $gene, @exon_arr, $first_exon);

    # III.7 Searching for genes and other structures
    # on genomic DNA (Genscan, ESTScan, MZEF)

    use Bio::Tools::Genscan;

    $genscan = Bio::Tools::Genscan->new('-file' => Bio::Root::IO->catfile("t","data","genomic-seq.genscan"));
    # $gene is an instance of Bio::Tools::Prediction::Gene
    # $gene->exons() returns an array of Bio::Tools::Prediction::Exon objects
    while($gene = $genscan->next_prediction()) {
        print $outputfh "Protein corresponding to current gene prediction is ",
        $gene->predicted_protein->seq()," \n";
        @exon_arr = $gene->exons();
        $first_exon = $exon_arr[0];
        print $outputfh "Start location of first exon of current predicted gene is ",
        $first_exon->start," \n";
    }
    $genscan->close();

    use Bio::Tools::Sim4::Results;
    print $outputfh "\nBeginning Sim4_result_parsing example... \n";
    my ($sim4,$exonset, @exons, $exon, $homol);

   $sim4 = new Bio::Tools::Sim4::Results(-file=>  Bio::Root::IO->catfile("t","data","sim4.rev"), -estisfirst=>0);
   $exonset = $sim4->next_exonset;
   @exons = $exonset->sub_SeqFeature();
   $exon = $exons[1];
    print $outputfh "Start location of first exon of first exon set is ",$exon->start(), " \n";
   print $outputfh "Name of est sequence is ",$exon->est_hit()->seq_id(), " \n";
   print $outputfh "The length of the est hit is ",$exon->est_hit()->seqlength(), " \n";
   $sim4->close();

    return 1;
} ;


#################################################
#  sequence_annotation ():
#

$sequence_annotation = sub {

    print $outputfh "\nBeginning sequence_annotation example... \n";
    my ($feat, $feature1, $feature2,$seqobj, @topfeatures, @allfeatures,
        $ann, $in, $infile, $other, $answer);
    # III.9.1 Representing sequence annotations (SeqFeature)

    $infile = $amino_seq_file;

    $in  = Bio::SeqIO->new('-file' => $infile ,
                           '-format' => 'Fasta');
    $seqobj = $in->next_seq();

    $feature1 = new Bio::SeqFeature::Generic
        ('-start' => 10,
         '-end' => 40,
         '-strand' => 1,
         '-primary' => 'exon',
         '-source'  => 'internal', );

    $feature2 = new Bio::SeqFeature::Generic
        ( '-start' => 20,
          '-end' => 30,
          '-strand' => 1,
          '-primary' => 'exon',
          '-source'  => 'internal', );

    $seqobj->add_SeqFeature($feature1);   # Add the SeqFeature to the parent
    $seqobj->add_SeqFeature($feature2);   # Add the SeqFeature to the parent

    # Once the features and annotations have been associated
    # with the Seq, they can be with retrieved, eg:
    @topfeatures = $seqobj->top_SeqFeatures(); # just top level, or
    @allfeatures = $seqobj->all_SeqFeatures(); # descend into sub features
    $feat = $allfeatures[0];
    $other = $allfeatures[1];

    print $outputfh " Total number of sequence features is: ", scalar @allfeatures, " \n";

    # The individual components of a SeqFeature can also be set
    # or retrieved with methods including:
    # attributes which return numbers
    $feat->start;          # start position
    $feat->end;            # end position
    $feat->strand;         # 1 means forward, -1 reverse, 0 not relevant
    # attributes which return strings
    $feat->primary_tag;    # the main 'name' of the sequence feature,  eg, 'exon'
    $feat->source_tag;     # where the feature comes from, eg'BLAST'
    # attributes which return Bio::PrimarySeq objects
    $feat->seq;            # the sequence between start,end
    $feat->entire_seq;     # the entire sequence

    print $outputfh " The primary tag of the feature is: ", $feat->primary_tag, "   \n";
    print $outputfh " The first feature begins at location ", $feat->start, " \n";
    print $outputfh "  ends at location ", $feat->end, " \n";
    print $outputfh "  and is located on strand ", $feat->strand, " \n";

    # other useful methods include
    $answer = $feat->overlaps($other);  # do SeqFeature $feat and SeqFeature $other overlap?
    print $outputfh " Feature 1 does ", ($answer)? "":"not ", " overlap Feature2. \n";

    $answer = $feat->contains($other); # is $other completely within $feat?
    print $outputfh " Feature 1 does ", ($answer)? "":"not ", " contain Feature2. \n";

    $answer = $feat->equals($other);   # do $feat and $other completely $agree?
    print $outputfh " Feature 1 does ", ($answer)? "":"not ", " equal Feature2. \n";

    $feat->sub_SeqFeature();  # create/access an array of subsequence features

    return 1;
} ;


#################################################
#  largeseqs ():
#

$largeseqs = sub {

    print $outputfh "\nBeginning largeseqs example... \n";

    # III.7.2 Representing and large sequences
    my ( $tmpfile, $seqio, $pseq, $plength, $last_4);


    $tmpfile = Bio::Root::IO->catfile("t","data","largefastatest.out") ;
    $seqio = new Bio::SeqIO('-format'=>'largefasta',
                            '-file'  =>Bio::Root::IO->catfile("t","data","genomic-seq.fasta"));
    $pseq = $seqio->next_seq();
    $plength = $pseq->length();

    print $outputfh " The length of the sequence ",
    $pseq->display_id()," is $plength  \n";
    $last_4 = $pseq->subseq($plength-3,$plength);
    print $outputfh " The final four residues are $last_4 \n";

    #END { unlink $tmpfile; }
    unlink $tmpfile;

    return 1;
} ;


#################################################
# liveseqs  ():
#

$liveseqs = sub {
    
    use Bio::LiveSeq::IO::BioPerl;
    my ($loader, $gene, $id, $maxstart);

    print $outputfh "\nBeginning liveseqs example... \n";

    # Representing changing sequences (LiveSeq)

    $loader=Bio::LiveSeq::IO::BioPerl->load('-db'=>"EMBL",
                                            '-file'=>Bio::Root::IO->catfile("t","data","factor7.embl"));
    $gene=$loader->gene2liveseq('-gene_name' => "factor7");
    $id = $gene->get_DNA->display_id ;
    print $outputfh " The id of the gene is $id , \n";
    $maxstart = $gene->maxtranscript->start;
    print $outputfh " The start position of the max transcript is $maxstart , \n";

    return 1;
} ;


#################################################
# run_struct  ():
#

$run_struct = sub {
  use Bio::Root::IO;
  eval { require Bio::Structure::Entry;
         require Bio::Structure::IO;
       };
  if ( $@ ){
    print STDERR "Cannot find Bio::Structure modules\n";
    print STDERR "Cannot run run_struct:\n";
    return 0;
  } else {
    print $outputfh "\nBeginning Structure object example... \n";

    # testing PDB format
    my $pdb_file = Bio::Root::IO->catfile("t","data","pdb1bpt.ent"); 
    my $structio = Bio::Structure::IO->new(-file  => $pdb_file,
                                           -format=> 'PDB');
    my $struc = $structio->next_structure;
    my ($chain) = $struc->chain;
    print $outputfh " The current chain is ",  $chain->id ," \n";
    my $pseq = $struc->seqres;
    print $outputfh " The first 20 residues of the sequence corresponding " .
    "to this structure are " . $pseq->subseq(1,20) . " \n";
    return 1;
  }
} ;





#################################################
# run_map  ():
#

$run_map = sub {

use Bio::MapIO;
use Bio::Root::IO;

 print $outputfh "\nBeginning MapIO example... \n";


my $mapio = new Bio::MapIO(
			    '-format' => 'mapmaker',
			    '-file'   => Bio::Root::IO->catfile('t','data', 
			    'mapmaker.out'));

my $map = $mapio->next_map;

print $outputfh " The type of the map is " , $map->type  ," \n";
print $outputfh " The units of the map are " , $map->units  ," \n";

my $count = 0;
foreach my $marker ( $map->each_element ) {
    print $outputfh " The marker ", $marker->name," is at ordered position " ,  $marker->position->order  ," \n";
    if ($count++ >= 5) {return 1};
}


    return 1;
} ;

#################################################
# run_tree  ():
#

$run_tree = sub {

use Bio::TreeIO;
use Bio::Root::IO;

print $outputfh "\nBeginning phylogenetic tree example... \n";


my $treeio = new Bio::TreeIO( -format => 'newick',
			    -file   => Bio::Root::IO->catfile('t','data', 
							       'cysprot1b.newick'));

my $tree = $treeio->next_tree;
my @nodes = $tree->get_nodes;

 foreach my $node ( $tree->get_root_node()->each_Descendent() ) {
	print $outputfh "node id and branch length: ", $node->to_string(), "\n";
	my @ch = $node->each_Descendent();
	if( @ch ) {
	    print $outputfh "\tchildren are: \n";
	    foreach my $node ( $node->each_Descendent() ) {
		print $outputfh "\t\t id and length: ", $node->to_string(), "\n";
	    }
	}
    }
    return 1;
} ;



#################################################
# run_perl  ():
#

$run_perl = sub {

    use Bio::Perl qw( read_sequence 
		      read_all_sequences 
		      write_sequence 
		      new_sequence 
		      get_sequence );
    
    print $outputfh "\nBeginning example of sequence manipulation without explicit Seq objects... \n";
    
# getting a sequence from a database (assummes internet connection)
 
    my $seq_object;
    eval { 
	$seq_object = get_sequence('swissprot',"ROA1_HUMAN");
    };
    unless( $seq_object ) { return 0 } # deal with case when
    # we're missing a necessary module and this returns undef


    # sequences are Bio::Seq objects, so the following methods work
    # (for more info see Bio::Seq documentation - try perldoc Bio::Seq)
    
    print $outputfh "Name of sequence retrieved from swissprot is ",$seq_object->display_id,"\n";
    print $outputfh "Sequence acc  is ",$seq_object->accession_number,"\n";
    print $outputfh "First 5 residues are ",$seq_object->subseq(1,5),"\n";
    
  # getting sequence data from disk
    
    $seq_object = read_sequence($amino_seq_file,'fasta'); 
    print $outputfh "Name of sequence retrieved from disk is ",$seq_object->display_id,"\n";
   return 1;
} ;


#################################################
# demo_variations  ():
#

$demo_variations = sub {

    print $outputfh "\nBeginning demo_variations example... \n";

    # III.8.3 Representing related sequences
    # - mutations, polymorphisms etc (Allele, SeqDiff,)

    use Bio::Variation::SeqDiff;
    use Bio::Variation::Allele;
    use Bio::Variation::DNAMutation;
    my ($a1, $a2, $dnamut, $seqDiff, @current_variants);

    $a1 = Bio::Variation::Allele->new;
    $a1->seq('aaaa');
    $a2 = Bio::Variation::Allele->new;
    $a2->seq('ttat');

    $dnamut = Bio::Variation::DNAMutation->new
        ('-start'  => 1000,
         '-length'  => 4,
         '-upStreamSeq'  => 'actggg',
         '-proof'  => 'experimental',
         '-isMutation' => 1, );
    $dnamut->allele_ori($a1);
    $dnamut->add_Allele($a2);
    $seqDiff = Bio::Variation::SeqDiff->new
        ('-id' => 'M20132',
         '-alphabet' => 'rna',
         #$seqDiff = Bio::Variation::SeqDiff->new
         #( '-id' => 'M20132', '-alphabet' => 'RNA',
         '-gene_symbol' => 'AR',
         '-chromosome' => 'X',
         '-numbering' => 'coding');
    # add it to a SeqDiff container object
    $seqDiff->add_Variant($dnamut);
    @current_variants = $seqDiff->each_Variant();
    print $outputfh " The original sequence at current variation is ",
    $current_variants[0]->allele_ori->seq ," \n";
    print $outputfh " The mutated sequence is ",
    $current_variants[0]->allele_mut->seq ," \n";
    print $outputfh " The upstream sequence is ",
    $current_variants[0]->upStreamSeq ," \n";

    return 1;
} ;


#################################################
#  demo_xml ():
#

$demo_xml = sub {

    print $outputfh "\nBeginning demo_xml example... \n";
    my ($str, $seqobj, $id, @feats, $first_primary_tag);

    # III.8.4 Sequence XML representations
    # - generation and parsing (SeqIO::game)

    eval { 
	require XML::Parser;
	
	$str = Bio::SeqIO->new('-file'=>
			       Bio::Root::IO->catfile("t","data","test.game"),
			       '-format' => 'game'); };
    if ( $@ ) {
	print STDERR "Cannot run demo_xml\n";
	print STDERR "\tProblem parsing GAME format or missing necessary installed modules XML::Parser XML::Parser::PerlSAX\n";
	return 0;
    } else {
      $seqobj = $str->next_seq();
      # $seq = $str->next_primary_seq();
      # $id = $seq->id;

      print $outputfh "Seq object display id is ", $seqobj->display_id(),
      "\n"; # the human read-able id of the sequence
      print $outputfh "The sequence description is: \n";
      print $outputfh "   ", $seqobj->desc(), " \n";
      print $outputfh "Acc num is ", $seqobj->accession_number(),
      " \n"; # when there, the accession number
      print $outputfh "Alphabet is ", $seqobj->alphabet(),
      " \n";    # one of 'dna','rna','protein'

      @feats = $seqobj->all_SeqFeatures();
      $first_primary_tag = $feats[0]->primary_tag;
      print $outputfh " Total number of sequence features is: ", 
      scalar @feats, "\n";
      print $outputfh " The primary tag of the first feature is: ",
      $first_primary_tag, "\n";
      print $outputfh " The first feature begins at location ", 
      $feats[0]->start," \n";
      print $outputfh "  and ends at location ", $feats[0]->end, " \n";

      return 1;
   }
} ;


################################################
#  other_seq_utilities ():
#

$other_seq_utilities = sub {

    use Bio::Tools::OddCodes;
    use Bio::Tools::SeqPattern;
    use Bio::Tools::CodonTable;

    my ( $oddcode_obj, $amino_obj, $in, $infile, $chargeseq, $chemseq);
    my (@ii, $i, @res, $myCodonTable);
    my ( $pattern,$pattern_obj, $pattern_obj2, $pattern_obj3);

    print $outputfh "\nBeginning sequence utilities example... \n";

    # III.4.6 Identifying amino acid characteristics
    # - eg charge, hydrophobicity (OddCodes)
    $infile = $amino_seq_file;
    $in  = Bio::SeqIO->new('-file' => $infile ,
                           '-format' => 'Fasta');
    $amino_obj = $in->next_seq->trunc(1,20);
    $oddcode_obj  =  Bio::Tools::OddCodes->new($amino_obj);
    print $outputfh "Initial sequence is: \n ", $amino_obj->seq(), " \n";
    $chargeseq = $oddcode_obj->charge;
    # Note OddCodes returns a reference to the desired
    # sequence, not the sequence itself.
    print $outputfh "Sequence in \'charge\' alphabet is : \n ",$$chargeseq, " \n";
    $chemseq = $oddcode_obj->chemical;
    print $outputfh "Sequence in \'chemical\' alphabet is : \n ",$$chemseq, " \n";

    # III.4.2.1 non-standard nucleotide translation tables (CodonTable)

    # create a table object by giving an ID
    $myCodonTable = Bio::Tools::CodonTable -> new ( '-id' => 16);
    print $outputfh "\nThe name of the current codon table is ",
    $myCodonTable->name() , " \n";

    # translate some codons
    @ii  = qw(act acc att ggg ttt aaa ytr yyy);
    @res =();
    for $i (0..$#ii) { $res[$i] = $myCodonTable->translate($ii[$i]); }
    print $outputfh "\nWith the current codon table, the sequence \n ",
    @ii, "\n is translated as \n" , @res, " \n";

    # III.4.2.2 utilities for ambiguous residues (SeqPattern, IUPAC)
    # III.4.2.3 regex-like nucleotide pattern manipulation

    print $outputfh "\nBeginning demo of SeqPattern object \n ";

    $pattern     = '(CCCCT)N{1,200}(agyyg)N{1,80}(agggg)';
    print $outputfh "\nInput regular expression = $pattern \n ";
    $pattern_obj = new Bio::Tools::SeqPattern('-SEQ' =>$pattern,
                                              '-TYPE' =>'dna');
    $pattern_obj2  = $pattern_obj->revcom();
    print $outputfh "\nReverse-complemented expression = ",$pattern_obj2->str, "\n ";
    $pattern_obj3 = $pattern_obj->revcom(1); ## returns expanded rev complement pattern.
    print $outputfh "\nExpanded reverse-complemented expression = ",$pattern_obj3->str, "\n ";
    #$pattern_obj->revcom(1); ## returns expanded rev complement pattern.

    return 1;
} ;

sub run_examples {
    @runlist = @_;
    @_ = ();
    if (scalar(@runlist)==0) {&$display_help;}; # display help if no option
    if( $runlist[0] == -1 ) { return }
    if ($runlist[0] == 0) {@runlist = (1..22); }; # argument = 0 means run tests 1 thru 22
    foreach $n  (@runlist) {
        if ($n ==100) {my $object = $runlist[1]; &$bpinspect1($object); last;}
        if ($n ==1) {&$sequence_manipulations; next;}
        if ($n ==2) {&$seqstats_and_seqwords; next;}
        if ($n ==3) {&$restriction_and_sigcleave; next;}
        if ($n ==4) {&$other_seq_utilities; next;}
        if ($n ==5) {&$run_perl; next;}
        if ($n ==6) {&$searchio_parsing; next;}
        if ($n ==7) {&$bplite_parsing; next;}
        if ($n ==8) {&$hmmer_parsing; next;}
        if ($n ==9) {&$simplealign ; next;}
        if ($n ==10) {&$gene_prediction_parsing; next;}
        if ($n ==11) {&$access_remote_db; next;}
        if ($n ==12) {&$index_local_db; next;}
        if ($n ==13) {&$fetch_local_db; next;}
        if ($n ==14) {&$sequence_annotation; next;}
        if ($n ==15) {&$largeseqs; next;}
        if ($n ==16) {&$liveseqs; next;}
        if ($n ==17) {&$run_struct; next;}
        if ($n ==18) {&$demo_variations; next;}
        if ($n ==19) {&$demo_xml; next;}
        if ($n ==20) {&$run_tree; next;}
        if ($n ==21) {&$run_map; next;}
        if ($n ==22) {&$run_remoteblast; next;}
        if ($n ==23) {&$run_standaloneblast; next;}
        if ($n ==24) {&$run_clustalw_tcoffee; next;}
        if ($n ==25) {&$run_psw_bl2seq; next;}
         &$display_help;
    }
    1;
}
#################################################
# bpinspect1  ():
#
#  Subroutine that identifies from which ancestor module each method
#  of an object is derived
#

$bpinspect1 = sub {
    my ($object, $object_class,$package );
    my @package_list = ();
    $object = shift;
#    ($object_class = $object) =~ s/::/\//g;  # replace "::" with "/"
#    $object_class = $object_class . ".pm";
    $object_class = $object . ".pm";
    eval "require $object_class"; # eval causes replacement of "::" with "/" or "\"
    my $method_hash = $object->methods('all');

    print $outputfh " \n ***Methods for Object $object ********  \n";
# get all the *unique* package names providing methods
    my %seen=();
    foreach $package  (values %$method_hash) {
        push(@package_list, $package) unless $seen{$package}++;
    }
    for $package (sort @package_list) {
        print $outputfh " \n\n Methods taken from package $package  \n";
        my $out_count = 1;
        for my $method (sort keys %$method_hash) {
            if ($$method_hash{$method} eq $package ) {
            print $outputfh " $method  ";
            $out_count++;
                if ($out_count == 7) {
                        print $outputfh " \n";    # keeps output line from getting too long
                        $out_count = 1;
                }
            }
         }
     }
    print $outputfh "\n";

# The following subroutine is based closely on Mark Summerfield's and Piers
# Cawley's clever Class_Methods_Introspection program
#
    package UNIVERSAL;
    sub methods {
        my ($class, $types) = @_;
        $class = ref $class || $class;
        $types ||= '';
        my %classes_seen;
        my %methods;
        my @class = ($class);

        no strict 'refs';
        while ($class = shift @class) {
            next if $classes_seen{$class}++;
            unshift @class, @{"${class}::ISA"} if $types eq 'all';
            # Based on methods_via() in perl5db.pl
            for my $method (grep {not /^[(_]/ and
                                  defined &{${"${class}::"}{$_}}}
                            keys %{"${class}::"}) {
		next if exists $methods{$method}; # method already found in heirarchy
                $methods{$method} = $class;        
            }
        }

        wantarray ? keys %methods : \%methods;
    }
    return 1;
} ;

    if( defined $ARGV[0] && $ARGV[0] == -1 ) {
	open(OUT, ">bptutorial.out") || die("cannot open outputfile: $!");
	$outputfh = *OUT;
    }
    &run_examples(@ARGV);

## End of "main"
1;