BLAST, BLAT and FASTA - Algorithms in Bioinformatics

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Bioinformatics I, WS’09-10, S. Henz (script by D. Huson) November 26, 2009 51 The number of random HSPs (s, t) with σ(s, t) ≥ S can be described by a Poisson distribution with parameter v = Kmne −λS . The number of HSPs with score ≥ S that we expect to see due to chance is then the parameter v, also called the E-value: E(S) =Kmne −λS The parameters K and λ depend on the background probabilities of the symbols and on the employed scoring matrix. We define λ as the unique value for y that satisfies the equation a,b∈Σ papbe S(a,b)y =1 K and λ are scaling-factors for the search space and for the scoring scheme, respectively. Hence the probability of finding exactly x HSPs with a score ≥ S is given by −E Ex P(X = x) =e x! The probability of finding at least one HSP “by chance” is where E is the E-value for S. P(S) = 1 − P(X = 0) = 1 − e −E , Thus we see that the probability distribution of the scores follows an extreme value distribution. BLAST reports E-values rather than ”P-values” as it is easier to interpret the difference between E-values than to interpret the difference between P-values. The raw scores S are of little use without detailed knowledge of the scoring system used, that is, of the statistical parameters K and λ. Therefore we introduced a normalized raw score called bit score S ′ that is defined as E-values and bit scores are related by (exercise!) 4.6 Gapped BLAST S ′ = λS − ln K . ln 2 E = mn2 −S′ A new version of BLAST called BLAST 2.0 2 allows gaps in the extension phase. 4.7 The BLAST family • BLASTN: compares a DNA query sequence to a DNA sequence database 2 S. F. Altschul, T. L. Madden, A.A. Schäffer, J. Zhang, Z. Zhang, W. Miller, and D. J. Lipman: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17):3389-402 (1997).
52 Bioinformatics I, WS’09-10, S. Henz (script by D. Huson) November 26, 2009 • BLASTP: compares a protein query sequence to a protein sequence database • TBLASTN: compares a protein query sequence to a DNA sequence database (6 frames translation) • BLASTX: compares a DNA query sequence (6 frames translation) to a protein sequence database • TBLASTX: compares a DNA query sequence (6 frames translation) to a DNA sequence database (6 frames translation) • Phi-BLAST: Pattern Hit Initiated BLAST searches for particular patterns in protein queries, incorp. into PSI-Blast • PSI-BLAST: Position specific iterated BLAST – profile of hits is computed – database is searched with profile – many iterations – designed to detect weak relationships between the query and members of the database not necessarily detectable by standard BLAST searches. – results in increased sensitivity 4.8 Available BLAST implementations • NCBI BLAST: Implementation of all BLAST programs maintained by NCBI. • AB-BLAST (former WU-BLAST): Alternative implementation of all BLAST programs (except for PHI- and PSI-BLAST) but the other BLAST families are 4.9 BLAT BLAT = Blast Like Alignment Tool 3 Motivation for the development of BLAT: For public assembly of the human genome 3 million ESTs and 13 million whole genome shotgun reads needed to be mapped to the human genome. For EST against genome alignment: 1.75 Gb in 3.72 million ESTs against 2.88 Gb bases of Human DNA. Application in particular for large query sequences, eg. genomes Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments. BLAT is especially designed for very fast and accurate alignments of both DNA and protein sequences. BLAST preprocesses the query. BLAT preprocesses the database: index of all non-overlapping K-mers in db (genome) Several stages: • use index to find regions in the genome that are possibly homologous to the query sequence. • perform an alignment between such regions. • stitch together the aligned regions (often exons) into larger alignments (typically genes). 3 W. J. Kent: BLAT - The BLAST-Like Alignment Tool. Genome Res. 12:656-664(2002)
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BLAST, BLAT and FASTA - Algorithms in Bioinformatics

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