Sequence Alignment Storing, retrieving and comparing DNA sequences in Databases. Comparing two or more sequences for similarities. Searching databases for related sequences and subsequences. Exploring frequently occurring patterns of nucleotides. Finding informative elements in protein and DNA sequences. Various experimental applications (reconstruction of DNA, etc.) Motivation:

Seq.Align. Protein Function Similar function More than 25% sequence identity ? Similar 3D structure ? Similar sequences produce similar proteins Gene1Gene2 ?

Alignment - inexact matching Substitution - replacing a sequence base by another. Insertion - an insertion of a base (letter) or several bases to the sequence. Deletion - deleting a base (or more) from the sequence. ( Insertion and deletion are the reverse of one another )

Seq. Align. Score Commonly used matrices: PAM250, BLOSUM64

Global Alignment INPUT: Two sequences S and T of roughly the same length. QUESTION: What is the maximum similarity between them? Find one of the best alignments.

The IDEA s[1…n] t[1…m] To align s[1...i] with t[1…j] we have three choices: * align s[1…i-1] with t[1…j-1] and match s[i] with t[j] * align s[1…i] with t[1…j-1] and match a space with t[j] * align s[1…i-1] with t[1…j] and match s[i] with a space s[1…i-1] i t[1…j-1] j s[1… i ] - t[1…j -1 ] j s[1…i -1 ] i t[1… j ] -

Global Alignment Alignment Score: V(n,m) Needleman-Wunsch 1970

Local Alignment INPUT: Two sequences S and T. QUESTION: What is the maximum similarity between a subsequence of S and a subsequence of T ? Find most similar subsequences.

Local Alignment Smith-Waterman 1981 *Penalties should be negative*

Local alignment match 2 mismatch -1 cxde c-de s: xxxcde t: abcxdex xxxcde abcxdex

Sequence Alignment Complexity: Time O(n*m) Space O(n*m) (exist algorithm with O(min(n,m)) )

Ends free alignment Ends free alignment INPUT: Two equences S and T (possibly of different length). QUESTION: Find one of the best alignments between subsequences of S and T when at least one of these subsequences is a prefix of the original sequence and one (not necessarily the other) is a suffix. or

Ends free alignment m n

Gap Alignment Definition: A gap is the maximal contiguous run of spaces in a single sequence within a given alignment.The length of a gap is the number of indel operations on it. A gap penalty function is a function that measure the cost of a gap as a (nonlinear) function of its length. Gap penalty INPUT: Two sequences S and T (possibly of different length). QUESTION: Find one of the best alignments between the two sequences using the gap penalty function. Affine Gap: W total = W g + qW s W g – weight to open the gap W s – weight to extend the gap

What Kind of Alignment to Use? The same protein from the different organisms. Two different proteins sharing the same function. Protein domain against a database of complete proteins....

M. Dayhoff Scoring Matrices Point Accepted Mutations or PAM matrices Proteins with 85% identity were used -> the function is not significantly changed -> the mutations are “accepted” PAM units – the measure of the amount of evolutionary distance between two amino acid sequences. One PAM unit – S 1 has converted (mutated) to S 2 with an average of one accepted point-mutation event per 100 amino acids.

PAM250

p a (=N a /N) – probability of occurrence of amino acid ‘a’ over a large, sufficiently varied, data set.  a p a = 1 f ab – the number of times the mutation a b was observed to occur. f a =  b  a f ab the total number of mutations in which a was involved f =  a f a - the total number of amino acid occurrences involved in mutations. 1)Probability matrix 2)Scoring matrix

M - 20x20 probability matrix M ab - the probability of amino acid ‘a’ changing into ‘b’ m a = f a / f * 1/(100 * p a ) - relative mutability of amino acid ‘a’. It is the probability that the given amino acid will change in the evolutionary period of interest. Assumptions – (a) 1 in 100 amino acids on average is changed. (b) mutations are position independent. (c) mutations are independent on its past.

M aa =1- m a - the probablity of ‘a’ to remain unchanged. M ab = Pr(a -> b) = Pr(a -> b | a changed) Pr(a changed)= = (f ab /f a )m a Easy to see:  b M ab =1 = M aa +  b  a (f ab /f a )m a = 1- m a + m a /f a  b  a f ab = 1  a p a M aa =0.99 -> in average 1 mutation every 100 positions.

What is the probability that ‘a’ mutates into ‘b’ in two PAM units of evolution? a->c->b or a->d-> …  c M ac M cb = M 2 ab ->M 2, M 3, M 4 … M 250 … k->  M k converges to a matrix with identical rows. M k ac = p c - no matter what amino acid you start with, after a long period of evolution the resulting amino acid will be ‘c’ with probability p c.

PAM-k ab = M k ab / p b - probability that a pair ‘ab’ is a mutation as opposed to being a random occurrence (likelihood or odds ratio). M ab / p b = [(f ab /f a )m a ] / p b = (f ab /f a ) f a / (f * 100 * p a * p b ) = f ab / (f * 100 * p a * p b ) = M ba / p a The total alignment score is the product of Pam-k ab. To avoid accuracy problems: Pam-k ab = 10 log M k ab / p b -> The total alignment score is the sum of Pam-k ab. PAM-k matrix

BioPerl “Bioperl is a collection of perl modules that facilitate the development of perl scripts for bio-informatics applications.” Bioperl is open source software that is still under active development. Tutorial Documentation

BioPerl Accessing sequence data from local and remote databases Transforming formats of database/ file records Manipulating individual sequences Searching for "similar" sequences Creating and manipulating sequence alignments Searching for genes and other structures on genomic DNA Developing machine readable sequence annotations

BioPerl library at TAU BioPerl is NOT yet installed globally on CS network. In each script you should add the following line: use lib "/home/silly6/mol/lib/bioperl";

Sequence Object Seq – stores sequence, identification labels (id, accession number, molecule type = DNA, RNA, Protein, …), multiple annotations and associated “sequence features”.

Seq Objects PrimarySeq – light-weight Seq. Stores only sequence and id. labels. LocatableSeq – Seq object with “start” and “stop” positions. Used by SimpleAlign object. Created by pSW, Clustalw, Tcoffee or bl2seq. LargeSeq – special type of Seq (more than 100 MB). LiveSeq – handles changing over time locations on a sequence. SeqI – interface for Seq objects.

Sequence Object $seq = Bio::Seq->new('-seq'=>'actgtggcgtcaact', '-desc'=>'Sample Bio::Seq object', '-display_id' => 'something', '-accession_number' => 'accnum', '-moltype' => 'dna' ); Usually Seq is not created this way.

Sequence Object $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->moltype(); # one of 'dna','rna','protein' $seqobj->primary_id(); # a unique id for this sequence irregardless # of its display_id or accession number

Accessing Data Base $gb = new Bio::DB::GenBank(); $seq1 = $gb->get_Seq_by_id('MUSIGHBA1'); $seq2 = $gb->get_Seq_by_acc('AF303112')); $seqio = $gb->get_Stream_by_batch([ qw(J00522 AF )])); Databases: genbank, genpept, swissprot and gdb.

Seq module ATGGAGCCCAAGCAAGGATACCTTCTTGTAAAATTGATAGAAGCTCGCAAGCTAGCA TCTAAGGATGTGGGCGGAGGGTCAGATCCATAC MEPKQGYLLVKLIEARKLASKDVGGGSDPY use Bio::DB::GenBank; $gb = new Bio::DB::GenBank(); $seq1 = $gb->get_Seq_by_acc('AF303112'); $seq2=$seq1->trunc(1,90); print $seq2->seq(), "\n"; $seq3=$seq2->translate; print $seq3->seq(), “\n“;

SeqIO object $seq = $gb->get_Seq_by_acc('AF303112')); $out = Bio::SeqIO->new('-file' => ">f.fasta", '-format' => 'Fasta'); $out->write_seq($seq); SeqIO can read/write/transform data in the following formats : Fasta, EMBL. GenBank, Swissprot, PIR, GCG, SCF, ACE, BSML

Transforming Sequence Files $in = Bio::SeqIO->new('-file' => “f.fasta", '-format' => 'Fasta'); $out = Bio::SeqIO->new('-file' => ">f.embl", '-format' => ‘EMBL'); $out->write_seq($in->next_seq()); #for several sequences while ( my $seq = $in->next_seq() ) { $out->write_seq($seq); } #better $in = Bio::SeqIO->new('-file' => “f.fasta", '-format' => 'Fasta'); $out = Bio::SeqIO->new('-file' => ">f.embl", ' -format' => ‘EMBL'); # Fasta EMBL format converter: print $out $_ while ;

BioPerl: Pairwise Sequence Alignment use Bio::Tools::pSW; $factory = new Bio::Tools::pSW( '-matrix' => 'blosum62.bla', '-gap' => 12, '-ext' => 2, ); $factory->align_and_show($seq1, $seq2, STDOUT); Smith-Waterman Algorithm Currently works only on protein sequences.

Alignment Objects SimpleAlign handles multiple alignments of sequences. #pSW module $aln = $factory->pairwise_alignment($seq1, $seq2); foreach $seq ( $aln->eachSeq() ) { print $seq->seq(), "\n"; } $alnout = Bio::AlignIO->new(-format => 'fasta', -fh => \*STDOUT); $alnout->write_aln($aln);

Homework Write a cgi script (using Perl) that performs pairwise Local/Global Alignment for DNA sequences. All I/O is via HTML only. Input: 1.Choice for Local/Global alignment. 2.Two sequences – text boxes or two accession numbers. 3.Values for match, mismatch, ins/dels. 4.Number of iterations for computing random scores. Output: 1.Alignment score. 2.z-score value (z= (score-average)/standard deviation.) Remarks: 1) you are allowed to use only linear space. 2)To compute z-score perform random shuffling: srand(time| $$); #init, $$-proc.id int(rand($i)); #returns rand. number between [0,$i]. 3)Shuffling is done in windows (non-overlapping) of 10 bases length. Number of shuffling for each window is random [0,10].

BioPerl: Multiple Sequence = ('ktuple' => 2, 'matrix' => 'BLOSUM'); $factory = $aln = foreach $seq ( $aln->eachSeq() ) { print $seq->seq(), "\n"; }