Programming in Computational Biology

8,297 views

Published on

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
8,297
On SlideShare
0
From Embeds
0
Number of Embeds
13
Actions
Shares
0
Downloads
89
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Programming in Computational Biology

  1. 1. Role of Programming in Computational Biology Atreyi Banerjee
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. Bioinformatics “pipelines” often involve chaining together multiple tools
  6. 6. Perl is the most-used bioinformatics language Most popular bioinformatics programming languages Bioinformatics career survey, 2008 Michael Barton
  7. 7. 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
  8. 8. 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…)
  9. 9. 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
  10. 10. 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
  11. 11. Programming in PERL  Data Types  Scalars ($)  Arrays (@)  Hashes (%)  Conditional Operators  AND (&&),  OR (||),  NOT (!)  Arithmetic Operators (+, -,*, /)
  12. 12.  CONDITIONS  If else  Elsif ladder  LOOPS  For  While  Foreach  Default Variables  $_ default variable  @_ default array
  13. 13. Finding all sequence lengths Open file Read line End of file? Line starts with “>” ? Remove “n” newline character at end of line Sequence name Sequence data Add length of line to running totalRecord the name Reset running total of current sequence length First sequence?Print last sequence length Stop noyes yes yes no no Start Print last sequence length
  14. 14. DNA Microarrays
  15. 15. Normalizing microarray data • Often microarray data are normalized as a precursor to further analysis (e.g. clustering) • This can eliminate systematic bias; e.g. − if every level for a particular gene is elevated, this might signal a problem with the probe for that gene − if every level for a particular experiment is elevated, there might have been a problem with that experiment, or with the subsequent image analysis • Normalization is crude (it can eliminate real signal as well as noise), but common
  16. 16. Rescaling an array  For each element of the array: add a, then multiply by b @array = (1, 3, 5, 7, 9); print "Array before rescaling: @arrayn"; rescale_array (@array, -1, 2); print "Array after rescaling: @arrayn"; sub rescale_array { my ($arrayRef, $a, $b) = @_; foreach my $x (@$arrayRef) { $x = ($x + $a) * $b; } } Array before rescaling: 1 3 5 7 9 Array after rescaling: 0 4 8 12 16 Array is passed by reference
  17. 17. Microarray expression data  A simple format with tab-separated fields  First line contains experiment names  Subsequent lines contain: − gene name − expression levels for each experiment * EmbryoStage1 EmbryoStage2 EmbryoStage3 ... Cyp12a5 104.556 102.441 55.643 ... MRG15 4590.15 6691.11 9472.22 ... Cop 33.12 56.3 66.21 ... bor 5512.36 3315.12 1044.13 ... Bx42 1045.1 632.7 200.11 ... ... ... ... ... Messages: readFrom(file), writeTo(file), normalizeEachRow, normalizeEachColumn…
  18. 18. Reading a file of expression data sub read_expr { my ($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)
  19. 19. 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);
  20. 20. 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)
  21. 21. Genome annotations
  22. 22. 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
  23. 23. Artemis A tool for genome annotation
  24. 24. 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/, $line, 9; push @gff, @data; } close GFF; return @gff; } Splits the line into at most nine fields, separated by tabs ("t") Appends a reference to @data to the @gff array
  25. 25. Writing a GFF file • We should be able to write as well as read all datatypes • Each array is made into a line via the join command • Arguments: filename & reference to array of arrays sub write_GFF { my ($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
  26. 26. 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?)
  27. 27. 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, $igff; last; } } } } } return @intersect; } Note: this code is slow. Vast improvements in speed can be gained if we sort the @gff array before checking for intersection. Fields 0, 3 and 4 of the GFF line are the sequence name, start and end co- ordinates of the feature
  28. 28. Converting GFF to sequence • Puts together several previously-described subroutines • Namely: read_FASTA read_GFF revcomp print_seq ($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); }
  29. 29. Phylogenetics  Analysis of relationships between organisms through phylogenetic programs like PHYLIP can be automated or run on command line
  30. 30. 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"
  31. 31. 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
  32. 32. 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
  33. 33. OOP: Analogy
  34. 34. 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.
  35. 35. 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…
  36. 36. 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
  37. 37. 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
  38. 38. 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
  39. 39. GUI  Graphical User Interface (GUI) are standalone modules created to make the work of an end user simpler.  Can be achieved through PERL Tk
  40. 40. 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
  41. 41. Basic BioPerl modules  Bio::Perl  Bio::Seq  Bio::SeqIO  Bio::Align  Bio::AlignIO  Bio::Tools::Run::StandAloneBlast
  42. 42. BLAST
  43. 43. CLUSTALW
  44. 44. References  http://www.bioperl.org  http://www.cpan.org  http://www.pasteur.fr  Learning Perl  Beginning Perl for Bioinformatics  Mastering Perl for Bioinformatics
  45. 45. Thank You

×