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Why Life is Beautiful James Tisdall

Why Life is Beautiful James Tisdall. James Tisdall (Jim). Biocomputing Associates PO Box 9965 Philadelphia PA 19118 USA (610)933-1200 http:/www.biocomputingassociates.com And DuPont Experimental Station James.Tisdall@DuPont.com. Who am I?. Musician

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Why Life is Beautiful James Tisdall

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  1. Why Life is BeautifulJames Tisdall

  2. James Tisdall (Jim) Biocomputing Associates PO Box 9965 Philadelphia PA 19118 USA (610)933-1200 http:/www.biocomputingassociates.com And DuPont Experimental Station James.Tisdall@DuPont.com

  3. Who am I? • Musician • Bell Laboratories, Information Principles Research • Human Genome Project • early Perl program DNA WorkBench • Mercator Genetics, Computational Biologist • Fox Chase Cancer Center, Bioinformatics Manager • Biocomputing Associates, consulting • DuPont Genetics Research • Author of "Beginning Perl for Bioinformatics" and "Mastering Perl for Bioinformatics" from O'Reilly

  4. Bioinformatics means using computers for biology research, and • Perl is a popular computer language for bioinformatics programming

  5. OUTLINE • Perl resources • Overview of basic Perl • The art of programming • Sequences and strings • Motifs and loops • Subroutines and bugs

  6. OUTLINE (Continued) • Mutations and Randomization • The Genetic Code • Restriction Maps and Regular Expressions • GenBank • Protein Data Bank • BLAST • Further Topics

  7. Advantages of Perl • Ease of Programming • Simplify common programming tasks. A lot of free software for bioinformatics, web programming, … • Rapid Prototyping • Often less programming time than other languages • Portability • Most operating systems: Windows, Mac, Unix, etc… • Free • Runspeed - suitable for most tasks

  8. Biological Sequence Data Bases and amino acids are represented as letters in Perl, just as in biology literature: • DNA and RNA A C G T (or U) • Amino acids A C D E F G H I K L M N P Q R S T V W X Y

  9. A Small Bioinformatics Program Sequences are represented as Strings # First we store the DNA in a variable called $DNA $DNA = 'ACGGGAGGACGGGAAAATTACTACGGCATTAGC'; # Next, we print the DNA onto the screen print $DNA; # Finally, we'll specifically tell the program to exit. exit;

  10. Virtual Transcription: DNA to RNA $DNA = 'ACGGGAGGACGGGAAAATTACTACGGCATTAGC'; print "$DNA\n\n"; # Transcribe the DNA to RNA by substituting all T's with U's. $RNA = $DNA; $RNA =~ s/T/U/g; print "$RNA\n";

  11. Reverse Complement $DNA = 'ACGGGAGGACGGGAAAATTACTACGGCATTAGC'; $revcom = reverse $DNA; # Next substitute all bases by their complements, # A->T, T->A, G->C, C->G, uppercase or lowercase $revcom =~ tr/ACGTacgt/TGCAtgca/;

  12. Mutations and Randomization

  13. Modeling mutations randomly • How to randomly select a position in a string • How to randomly select an index in an array • Randomly select a base in DNA • Mutate a base to some other random base • Generate random DNA data sets • Repeatedly mutate DNA

  14. A program to simulate DNA mutation • Pseudocode: 1. Select a random position in the DNA 2. Choose a random nucleotide 3. Substitute the random nucleotide in the random position

  15. A program to simulate DNA mutation 1. Select a random position in the DNA $position = int(rand(length($DNA))) 2. Choose a random nucleotide @nucleotides = ('A', 'C', 'G', 'T'); $new = $nucleotides[rand @nucleotides] 3. Substitute the random nucleotide in the random position substr($DNA, $position, 1, $new)

  16. Generating random DNA sub make_random_DNA { # Collect arguments, declare variables my($length) = @_; my $dna; for (my $i=0 ; $i < $length ; ++$i) { $dna .= $nucleotides[rand @nucleotides]; } return $dna; }

  17. The Genetic Code

  18. The genetic code • DNA encodes amino acids • 3 bases of DNA = a codon • Each codon represents an amino acid or a "stop" • There are 64 codons, but 20 amino acids • Most amino acids are coded for by multiple codons, which makes the genetic code a redundant code

  19. Translating codons to amino acids sub codon2aa { my($codon) = @_; $codon = uc $codon; my(%genetic_code) = ( 'TCA' => 'S', # Serine 'TCC' => 'S', # Serine 'TCG' => 'S', # Serine 'TCT' => 'S', # Serine 'TTC' => 'F', # Phenylalanine … et cetera …

  20. Translating DNA into proteins sub dna2peptide { my($dna) = @_; # Initialize variables my $protein = ''; # Translate each three-base codon to an amino acid, and append to a protein for(my $i=0; $i < (length($dna) - 2) ; $i += 3) { $protein .= codon2aa( substr($dna,$i,3) ); } return $protein; }

  21. Reading DNA from FASTA files • FASTA format is the most common way that DNA is stored in files (but there are many others) • Example: > sample dna (This is a typical fasta header.) agatggcggcgctgaggggtcttgggggctctaggccggccacctactgg tttgcagcggagacgacgcatggggcctgcgcaataggagtacgctgcct gggaggcgtgactagaagcggaagtagttgtgggcgcctttgcaaccgcc tgggacgccgccgagtggtctgtgcag

  22. Reading DNA from FASTA files > sample dna (This is a typical fasta header.) agatggcggcgctgaggggtcttgggggctctaggccggccacctactgg tttgcagcggagacgacgcatggggcctgcgcaataggagtacgctgcct gggaggcgtgactagaagcggaagtagttgtgggcgcctttgcaaccgcc Tgggacgccgccgagtggtctgtgcag • Notice that first line starts with a > symbol, and contains annotation. • Each other line just contains sequence data. • Simple!

  23. Reading FASTA files: core code From textbook, p. 170 foreach my $line (@fasta_file_data) { if($line =~ /^>/) {# discard fasta header line next; } else {# keep line, add to sequence string $sequence .= $line; } } # remove non-sequence data (in this case, whitespace) from $sequence string $sequence =~ s/\s//g;

  24. Regular expressions Regular expressions are a language within the Perl language that describe many kinds of patterns in strings, e.g. motifs in DNA. Regular expressions permit you to find and alter many patterns with relative ease. The excellent regular expressions in Perl are a major reason for Perl's success as a bioinformatics programming language.

  25. Restriction enzymes, sites, and maps • A restriction enzyme is a protein that cuts DNA at short, specific sequences • EcoRI cuts at the restriction site GAATTC, between the G and A, on both complementary strands (the reverse complement of GAATTC is GAATTC), leaving "sticky ends" for insertion into a vector for cloning and sequencing. • A restriction map is the list of locations where a given restriction site occurs in the sequence

  26. Finding restriction sites while ( $sequence =~ /$regexp/ig ) { push ( @positions, pos($sequence) - length($&) + 1); } • while loop conditional is a pattern match • ig: i stands for "case insensitive" • g stands for "global", will keep searching entire string • pos is a built-in function that gives the ending position of the last successful match; subtract the length of the match ($&) to get to the beginning of the match; add 1 because biologists call the first base in DNA as position 1, while Perl calls the first letter in a string as position 0

  27. GenBank

  28. GenBank • http://ncbi.nlm.nih.gov/Genbank

  29. GenBank Libraries • Examples: PRI: primate sequences ROD: rodent sequences PHG: bacteriophage sequences GSS: genome survey sequences

  30. Protein Data Bank

  31. PDB - Protein Data Bank • The primary depository for 3D protein structures • http://www.rcsb.org/pdb/

  32. BLAST

  33. BLAST • Basic Local Alignment Search Tool • Most popular bioinformatics program • Discovers sequence similarity between your sequence and large databases • Voluminous output • Need to process output by program

  34. Similarity vs Homology String matching is computer science term for finding sequence similarity • Exact • Approximate : percent identity Homology • Sequences are or are not related evolutionarily • Yes or no; no percentages

  35. Parsing BLAST output • Many bioinformatics projects require frequent BLAST searches • Need to automate collection and processing • BLAST output does not label lines by their function, so use regular expressions • Several BLAST parsers are available as Perl modules: see for example Bioperl modules Bio::Tools::BPlite and Bio::Tools::Blast::*

  36. Bioinformatics Resources • Books • Bioinformatics: Sequence and Genome Analysis by Mount • Developing Bioinformatics Computer Skills – Gibas & Jambeck • Introduction to Computational Biology: Maps,Sequences and Genomes, Waterman • Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins. Baxecvanis & Ouellette, editors • 2. Government/Public Information Resources • National Center for Biotechnology Information (NCBI) – U.S. Government Center – www.ncbi.nlm.nih.gov • European Molecular Biology Laboratory (EMBL). European Union Laboratory – www.embl.org • European Bioinformatics Institute (EBI)- From UK, part of EMBL- www.ebi.ac.uk • UCSC Genome Resource- serves constructed genome data – www.genome.ucsc.edu

  37. Bioinformatics Resources (cont.) • 3. Conferences • ISMB: Intellegent Systems for Molecular Biology, www.iscb.org/ismb2004/ • Bioinformatics Open Source Conference, www.bioinformatics.org • RECOMB: Conference on Computational Molecular Biology, recomb04.sdsc.edu/

  38. Molecular Biology • 1. Helpful Reference Books for Programmers • Recombinant DNA, 2nd edition, Watson et al. • Molecular Biology of the Gene, Watson et al. • Molecular Cell Biology, Lodish, et al.

  39. Approximate string matching As an example of advanced data structures, following is an algorithm that relies on a 2 dimensional array. It finds the best match for a (shorter) pattern in a (longer) text. It is one of several important dynamic programming algorithms in bioinformatics

  40. Approximate string matching 1 of 4 my $pattern = 'EIQADEVRL'; print "PATTERN:\n$pattern\n"; my $text = 'SVLQDRSMPHQEILAADEVLQESEMRQQDMISHDE'; print "TEXT:\n$text\n"; my $TLEN = length $text; my $PLEN = length $pattern; # D is the Distance matrix, which shows the "edit distance" between # substrings of the pattern and the text. # It is implemented as a reference to an anonymous array. my $D = []; # The rows correspond to the text # Initialize row 0 of D. for (my $t=0; $t <= $TLEN ; ++$t) { $D->[$t][0] = 0; }

  41. Approximate string matching 2 of 4 # Compute the edit distances. for (my $t=1; $t <= $TLEN ; ++$t) { for (my $p=1; $p <= $PLEN ; ++$p) { $D->[$t][$p] = # Choose whichever alternative has the least cost min3( # Text and pattern may or may not match at this character ... substr($text, $t-1, 1) eq substr($pattern, $p-1, 1) ? $D->[$t-1][$p-1] # If match, no increase in edit distance! : $D->[$t-1][$p-1] + 1, # If the text is missing a character $D->[$t-1][$p] + 1, # If the pattern is missing a character $D->[$t][$p-1] + 1 ) } }

  42. Approximate string matching 3 of 4 # Print D, the resulting edit distance array for (my $p=0; $p <= $PLEN ; ++$p) { for (my $t=0; $t <= $TLEN ; ++$t) { print $D->[$t][$p], " "; } print "\n"; } # Find the best match(es). # The edit distances appear in the last row. my @matches = (); my $bestscore = 10000000; for (my $t=1 ; $t <= $TLEN ; ++$t) { if( $D->[$t][$PLEN] < $bestscore) { $bestscore = $D->[$t][$PLEN]; @matches = ($t); }elsif( $D->[$t][$PLEN] == $bestscore) { push(@matches, $t); } }

  43. Approximate string matching 4 of 4 # Report the best match(es). print "\nThe best match for the pattern $pattern\n"; print "has an edit distance of $bestscore\n"; print "and appears in the text ending at location"; print "s" if ( @matches > 1); print " @matches\n";

  44. Approximate string matching: Output PATTERN: EIQADEVRL TEXT: SVLQDRSMPHQEILAADEVLQESEMRQQDMISHDE 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 2 2 2 2 2 2 2 2 2 2 2 2 1 0 1 2 2 2 1 1 2 2 1 1 1 1 2 2 2 2 2 1 2 2 2 1 3 3 3 3 2 3 3 3 3 3 3 2 2 1 1 2 3 3 2 2 2 2 2 2 2 2 2 2 2 3 3 2 2 3 3 2 4 4 4 4 3 3 4 4 4 4 4 3 3 2 2 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 4 3 5 5 5 5 4 3 4 5 5 5 5 4 4 3 3 2 2 2 3 4 4 4 4 4 4 4 4 4 4 3 4 4 4 4 3 4 6 6 6 6 5 4 4 5 6 6 6 5 4 4 4 3 3 3 2 3 4 5 4 5 4 5 5 5 5 4 4 5 5 5 4 3 7 7 6 7 6 5 5 5 6 7 7 6 5 5 5 4 4 4 3 2 3 4 5 5 5 5 6 6 6 5 5 5 6 6 5 4 8 8 7 7 7 6 5 6 6 7 8 7 6 6 6 5 5 5 4 3 3 4 5 6 6 6 5 6 7 6 6 6 6 7 6 5 9 9 8 7 8 7 6 6 7 7 8 8 7 7 6 6 6 6 5 4 3 4 5 6 7 7 6 6 7 7 7 7 7 7 7 6 The best match for the pattern EIQADEVRL has an edit distance of 3 and appears in the text ending at location 20

  45. Bioperl Bioperl is a collection of Perl modules for bioinformatics. It has been developed by a large group of volunteers. Bioperl has become a fairly stable platform with which to develop software; it is growing and changing rapidly, but its core is relatively mature. Bioperl is a must for anyone interested in writing bioinformatics software in Perl Don't reinvent the wheel! (Unless you like to, for the fun or interest of it.)

  46. What can Bioperl do? Sequence manipulation (DNA, RNA, proteins) • Several kinds of sequence objects • Sequence format conversion • Translation • Revcom • Sequence statistics (mol. weights, etc.) • Restriction enzyme maps • Signal cleavage sites

  47. What can Bioperl do? Sequence alignment • Interfaces with many alignment programs such as fasta, clustalw, gcg • Extract subalignment, consensus string • Alignment statistics

  48. What can Bioperl do? Biological databases • Retrieving Genbank, EMBL, acedb, etc. • Retrieving from remote databases • Indexing and retrieving from local databases • Parsing program reports from Blast and variants, HMMer, genefinding programs

  49. What can Bioperl do? Sequence annotation • Adding annotation • Parsing annotation from sequence databases

  50. What can Bioperl do? Protein structure • Read in PDB files • Extract chains and their residues

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