Programming in Computational Biology

Role of Programming in
Computational
Biology
Atreyi Banerjee

Programming languages
• Self-contained language
– Platform-independent
– Used to write O/S
– C (imperative, procedural)
– C++, Java (object-oriented)
– Lisp, Haskell, Prolog (functional)
• Scripting language
– Closely tied to O/S
– Perl, Python, Ruby
• Domain-specific language
– R (statistics)
– MatLab (numerics)
– SQL (databases)• An O/S typically manages…
– Devices (see above)
– Files & directories
– Users & permissions
– Processes & signals

Role of Programming

Reduces time

Reduces money on R&D

Reduces human effort

Streamlines workflow

Standard Algorithm unifies data result and
assures reproducability

Reduces human error

Applications of Programming

Data Mining

Genome Annotation

Microarray Analysis

Website Development

Tool Development

Statistical Analyses

Phylogeny

Genome Wide Association Studies (GWAS)

Next Generation Sequencing studies

Bioinformatics “pipelines” often involve
chaining together multiple tools

Perl is the most-used bioinformatics language
Most popular bioinformatics programming languages
Bioinformatics career survey, 2008
Michael Barton

PERL

Practical Extraction & Report Language

Interpreted, not compiled
− Fast edit-run-revise cycle
• Procedural & imperative
− Sequence of instructions (“control flow”)
− Variables, subroutines
• Syntax close to C (the de facto standard minimal language)
− Weakly typed (unlike C)
− Redundant, not minimal (“there’s more than one way to do it”)
− “Syntactic sugar”

High-level data structures & algorithms
– Hashes, arrays

Operating System support (files, processes, signals)

String manipulation

Pros and Cons of Perl
• Reasons for Perl’s popularity in bioinformatics (Lincoln Stein)
– Perl is remarkably good for slicing, dicing, twisting, wringing, smoothing,
summarizing and otherwise mangling text
– Perl is forgiving
– Perl is component-oriented
– Perl is easy to write and fast to develop in
– Perl is a good prototyping language
– Perl is a good language for Web CGI scripting
• Problems with Perl
– Hard to read (“there’s more than one way to do it”, cryptic syntax…)
– Too forgiving (no strong typing, allows sloppy code…)

General principles of programming

Make incremental changes

Test everything you do
− the edit-run-revise cycle

Write so that others can read it
− (when possible, write with others)

Think before you write

Use a good text editor

Good debugging style

Regular expressions

Perl provides a pattern-matching engine

Patterns are called regular expressions

They are extremely powerful
− probably Perl's strongest feature, compared to other
languages

Often called "regexps" for short

Programming in PERL

Data Types

Scalars ($)

Arrays (@)

Hashes (%)

Conditional Operators

AND (&&),

OR (||),

NOT (!)

Arithmetic Operators (+, -,*, /)


CONDITIONS

If else

Elsif ladder

LOOPS

For

While

Foreach

Default Variables

$_ default variable

@_ default array

Finding all sequence lengths
Open file
Read line
End of file?
Line starts with “>” ?
Remove “n” newline
character at end of line
Sequence name Sequence data
Add length of line
to running totalRecord the name
Reset running total of
current sequence length
First sequence?Print last
sequence
length
Stop
noyes
yes
yes
no
no
Start
Print last
sequence
length

Normalizing microarray data
• Often microarray data are normalized as a precursor
to further analysis (e.g. clustering)
• This can eliminate systematic bias; e.g.
− if every level for a particular gene is elevated, this might
signal a problem with the probe for that gene
− if every level for a particular experiment is elevated, there
might have been a problem with that experiment, or with
the subsequent image analysis
• Normalization is crude (it can eliminate real signal as
well as noise), but common

Rescaling an array

For each element of the array:
add a, then multiply by b
@array = (1, 3, 5, 7, 9);
print "Array before rescaling: @arrayn";
rescale_array (@array, -1, 2);
print "Array after rescaling: @arrayn";
sub rescale_array {
my ($arrayRef, $a, $b) = @_;
foreach my $x (@$arrayRef) {
$x = ($x + $a) * $b;
}
}
Array before rescaling: 1 3 5 7 9
Array after rescaling: 0 4 8 12 16
Array is passed by reference

Microarray expression data

A simple format with tab-separated fields

First line contains experiment names

Subsequent lines contain:
− gene name
− expression levels for each experiment
* EmbryoStage1 EmbryoStage2 EmbryoStage3 ...
Cyp12a5 104.556 102.441 55.643 ...
MRG15 4590.15 6691.11 9472.22 ...
Cop 33.12 56.3 66.21 ...
bor 5512.36 3315.12 1044.13 ...
Bx42 1045.1 632.7 200.11 ...
... ... ... ...
Messages: readFrom(file), writeTo(file), normalizeEachRow, normalizeEachColumn…

Reading a file of expression data
sub read_expr {
my ($filename) = @_;
open EXPR, "<$filename";
my $firstLine = <EXPR>;
chomp $firstLine;
my @experiment = split /t/, $firstLine;
shift @experiment;
my %expr;
while (my $line = <EXPR>) {
chomp $line;
my ($gene, @data) = split /t/, $line;
if (@data+0 != @experiment+0) {
warn "Line has wrong number of fieldsn";
}
$expr{$gene} = @data;
}
close EXPR;
return (@experiment, %expr);
}
Note use of
scalar context
to compare
array sizes
Reference to array of experiment names
Reference to hash of arrays
(hash key is gene name, array
elements are expression data)

Normalizing by gene

A program to normalize expression data from
a set of microarray experiments

Normalizes by gene
($experiment, $expr) = read_expr ("expr.txt");
while (($geneName, $lineRef) = each %$expr) {
normalize_array ($lineRef);
}
sub normalize_array {
my ($data) = @_;
my ($mean, $sd) = mean_sd (@$data);
@$data= map (($_ - $mean) / $sd, @$data);
}
NB $data
is a reference
to an array
Could also use the following:
rescale_array($data,-$mean,1/$sd);

Normalizing by column

Remaps gene arrays to column arrays
($experiment, $expr)
= read_expr ("expr.txt");
my @genes = sort keys %$expr;
for ($i = 0; $i < @$experiment; ++$i) {
my @col;
foreach $j (0..@genes-1) {
$col[$j] = $expr->{$genes[$j]}->[$i];
}
normalize_array(@col);
foreach $j (0..@genes-1) {
$expr->{$genes[$j]}->[$i] = $col[$j];
}
}
Puts column
data in @col
Puts @col
back into %expr
Normalizes (note use
of reference)

GFF annotation format• Nine-column tab-delimited format for simple annotations:
• Many of these now obsolete, but name/start/end/strand (and sometimes
type) are useful
• Methods: read, write, compareTo(GFF_file), getSeq(FASTA_file)
SEQ1 EMBL atg 103 105 . + 0 group1
SEQ1 EMBL exon 103 172 . + 0 group1
SEQ1 EMBL splice5 172 173 . + . group1
SEQ1 netgene splice5 172 173 0.94 + . group1
SEQ1 genie sp5-20 163 182 2.3 + . group1
SEQ1 genie sp5-10 168 177 2.1 + . group1
SEQ2 grail ATG 17 19 2.1 - 0 group2
Sequence
name
Program
Feature
type Start
residue
(starts at 1)
End
residue
(starts at 1)
Score
Strand
(+ or -)
Coding
frame
("." if not
applicable)
Group

Artemis
A tool for genome annotation

Reading a GFF file
• This subroutine reads a GFF file
• Each line is made into an array via the split command
• The subroutine returns an array of such arrays
sub read_GFF {
my ($filename) = @_;
open GFF, "<$filename";
my @gff;
while (my $line = <GFF>) {
chomp $line;
my @data = split /t/, $line, 9;
push @gff, @data;
}
close GFF;
return @gff;
}
Splits the line into at most nine
fields, separated by tabs ("t")
Appends a reference to @data
to the @gff array

Writing a GFF file
• We should be able to write as well as read all datatypes
• Each array is made into a line via the join command
• Arguments: filename & reference to array of arrays
sub write_GFF {
my ($filename, $gffRef) = @_;
open GFF, ">$filename" or die $!;
foreach my $gff (@$gffRef) {
print GFF join ("t", @$gff), "n";
}
close GFF or die $!;
}
open evaluates FALSE if
the file failed to open, and
$! contains the error message
close evaluates FALSE if
there was an error with the file

GFF intersect detection
• Let (name1,start1,end1) and (name2,start2,end2) be the co-
ordinates of two segments
• If they don't overlap, there are three possibilities:
• name1 and name2 are different;
• name1 = name2 but start1 > end2;
• name1 = name2 but start2 > end1;
• Checking every possible pair takes time N2
to run, where N is
the number of GFF lines (how can this be improved?)

Self-intersection of a GFF file
sub self_intersect_GFF {
my @gff = @_;
my @intersect;
foreach $igff (@gff) {
foreach $jgff (@gff) {
if ($igff ne $jgff) {
if ($$igff[0] eq $$jgff[0]) {
if (!($$igff[3] > $$jgff[4]
|| $$jgff[3] > $$igff[4])) {
push @intersect, $igff;
last;
}
}
}
}
}
return @intersect;
}
Note: this code is slow.
Vast improvements in
speed can be gained if
we sort the @gff array
before checking for
intersection.
Fields 0, 3 and 4 of the
GFF line are the sequence
name, start and end co-
ordinates of the feature

Converting GFF to sequence
• Puts together several previously-described subroutines
• Namely: read_FASTA read_GFF revcomp print_seq
($gffFile, $seqFile) = @ARGV;
@gff = read_GFF ($gffFile);
%seq = read_FASTA ($seqFile);
foreach $gffLine (@gff) {
$seqName = $gffLine->[0];
$seqStart = $gffLine->[3];
$seqEnd = $gffLine->[4];
$seqStrand = $gffLine->[6];
$seqLen = $seqEnd + 1 - $seqStart;
$subseq = substr ($seq{$seqName}, $seqStart-1, $seqLen);
if ($seqStrand eq "-") { $subseq = revcomp ($subseq); }
print_seq ("$seqName/$seqStart-$seqEnd/$seqStrand", $subseq);
}

Phylogenetics

Analysis of relationships between organisms
through phylogenetic programs like PHYLIP
can be automated or run on command line

Packages

Perl allows you to organise your subroutines in
packages each with its own namespace

Perl looks for the packages in a list of directories
specified by the array @INC

Many packages available at
http://www.cpan.org/
use PackageName;
PackageName::doSomething();
This line includes a file called
"PackageName.pm" in your code
print "INC dirs: @INCn";
INC dirs: Perl/lib Perl/site/lib .
The "." means the
directory that the
script is saved in
This invokes a subroutine called doSomething()
in the package called "PackageName.pm"

Object-oriented programming

Data structures are often associated with code
− FASTA: read_FASTA print_seq revcomp ...
− GFF: read_GFF write_GFF ...
− Expression data: read_expr mean_sd ...

Object-oriented programming makes this association
explicit.

A type of data structure, with an associated set of
subroutines, is called a class

The subroutines themselves are called methods

A particular instance of the class is an object

OOP concepts
• Abstraction
– represent the essentials, hide the details
• Encapsulation
– storing data and subroutines in a single unit
– hiding private data (sometimes all data, via accessors)
• Inheritance
– abstract base interfaces
– multiple derived classes
• Polymorphism
– different derived classes exhibit different behaviors in response
to the same requests

OOP: Analogy
o Messages (the words in the speech balloons, and also perhaps the coffee itself)
o Overloading (Waiter's response to "A coffee", different response to "A black coffee")
o Polymorphism (Waiter and Kitchen implement "A black coffee" differently)
o Encapsulation (Customer doesn't need to know about Kitchen)
o Inheritance (not exactly used here, except implicitly: all types of coffee can be drunk or spilled, all
humans can speak basic English and hold cups of coffee, etc.)
o Various OOP Design Patterns: the Waiter is an Adapter and/or a Bridge, the Kitchen is a Factory
(and perhaps the Waiter is too), asking for coffee is a Factory Method, etc.

OOP: Advantages
• Often more intuitive
– Data has behavior
• Modularity
– Interfaces are well-defined
– Implementation details are hidden
• Maintainability
– Easier to debug, extend
• Framework for code libraries
– Graphics & GUIs
– BioPerl, BioJava…

OOP: Jargon
• Member, method
– A variable/subroutine associated with a particular class
• Overriding
– When a derived class implements a method differently from its parent
class
• Constructor, destructor
– Methods called when an object is created/destroyed
• Accessor
– A method that provides [partial] access to hidden data
• Factory
– An [abstract] object that creates other objects
• Singleton
– A class which is only ever instantiated once (i.e. there’s only ever one
object of this class)
– C.f. static member variables, which occur once per class

Objects in Perl
• An object in Perl is usually a reference to a hash
• The method subroutines for an object are found in a
class-specific package
– Command bless $x, MyPackage associates variable
$x with package MyPackage
• Syntax of method calls
– e.g. $x->save();
– this is equivalent to PackageName::save($x);
– Typical constructor: PackageName->new();
– @EXPORT and @EXPORT_OK arrays used to export
method names to user’s namespace
• Many useful Perl objects available at CPAN

Common Gateway Interface
• CGI (Common Gateway Interface)
– Page-based web programming paradigm
• Can construct static (HTML) as well as
dynamic (CGI) web pages.
• CGI.pm (also by Lincoln Stein)
– Perl CGI interface
– runs on a webserver
– allows you to write a program that runs behind a
webpage
• CGI (static, page-based) is gradually being
supplemented by AJAX

GUI

Graphical User Interface (GUI) are standalone
modules created to make the work of an end
user simpler.

Can be achieved through PERL Tk

BioPerl
• Bioperl is a collection of Perl modules that facilitate the
development of Perl scripts for bioinformatics applications.
• A set of Open Source Bioinformatics packages
– largely object-oriented
• Implements sequence and alignments manipulation, accessing
of sequence databases and parsing of the results of various
molecular biology programs including Blast, clustalw, TCoffee,
genscan, ESTscan and HMMER.
• Bioperl enables developing scripts that can analyze large
quantities of sequence data in ways that are typically difficult
or impossible with web based systems.
• Parses BLAST and other programs
• Basis for Ensembl
– the human genome annotation project
– www.ensembl.org

Basic BioPerl modules

Bio::Perl

Bio::Seq

Bio::SeqIO

Bio::Align

Bio::AlignIO

Bio::Tools::Run::StandAloneBlast

References

http://www.bioperl.org

http://www.cpan.org

http://www.pasteur.fr

Learning Perl

Beginning Perl for Bioinformatics

Mastering Perl for Bioinformatics

Programming in Computational Biology

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