Lecture 3.11 BLAST

Lecture 3.12 BLAST B asic L ocal A lignment S earch T ool Developed in 1990 and 1997 (S. Altschul) A heuristic method for performing local alignments through searches of high scoring segment pairs (HSP’s) 1st to use statistics to predict significance of initial matches - saves on false leads Offers both sensitivity and speed

Lecture 3.13 Looks for clusters of nearby or locally dense “similar or homologous” k-tuples Uses “look-up” tables to shorten search time Uses larger “word size” than FASTA to accelerate the search process Performs both Global and Local alignment Fastest and most frequently used sequence alignment tool -- THE STANDARD BLAST

Lecture 3.14 BLAST Access NCBI BLAST Canadian Bioinformatics Resource BLAST European Bioinformatics Institute BLAST

Lecture 3.15

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8 Different Flavours of BLAST BLASTP - protein query against protein DB BLASTN - DNA/RNA query against GenBank (DNA) BLASTX - 6 frame trans. DNA query against proteinDB TBLASTN - protein query against 6 frame GB transl. TBLASTX - 6 frame DNA query to 6 frame GB transl. PSI-BLAST - protein ‘profile’ query against protein DB PHI-BLAST - protein pattern against protein DB

Lecture 3.19 Other BLAST Services MEGABLAST - for comparison of large sets of long DNA sequences RPS-BLAST - Conserved Domain Detection BLAST 2 Sequences - for performing pairwise alignments for 2 chosen sequences Genomic BLAST - for alignments against select human, microbial or malarial genomes VecScreen - for detecting cloning vector contamination in sequenced data

Lecture Running NCBI BLAST

Lecture MT0895 MMKIQIYGTGCANCQMLEKNAREAVKELG IDAEFEKIKEMDQILEAGLTALPGLAVDG ELKIMGRVASKEEIKKILS

Lecture Paste in sequence (FASTA format, raw sequence or type in GI or accession number) Running NCBI BLAST >Mysequence MT0895 KIQIYGTGCANCQMLEKNAREAVKELGIDAE FEKIKEMDQILEAGLTALPGLAVDGELKIDS > KIQIYGTGCANCQMLEKNAREAVKELGIDAE FEKIKEMDQILEAGLTALPGLAVDGELKIDS OR KIQIYGTGCANCQMLEKNAREAVKELGIDAE FEKIKEMDQILEAGLTALPGLAVDGELKIDS OR

Lecture Choose a range of interest in the sequence “set subsequences” (not usually used) Select the database from pull-down menu (usually choose nr = non-redundant) Keep CD Search “check box” on Leave “Options” unchanged (use defaults) Go to “Format” menu and adjust Number of descriptions and alignments as desired Running NCBI BLAST

Lecture Running NCBI BLAST Select Database

Lecture Conserved Domain Database Contains a collection of pre-identified functional or structural domains Derived from Pfam and Smart databases as well as other sources Uses Reverse Position Specific BLAST (RPS-BLAST) to perform search Query sequence is compared to a PSSM derived from each of the aligned domains

Lecture Running NCBI BLAST Click BLAST!

Lecture Formatting Results

Lecture BLAST Format Options

Lecture BLAST Output

Lecture BLAST Output

Lecture BLAST Output

Lecture BLAST Output

Lecture BLAST Output

Lecture BLAST Output

Lecture BLAST Parameters Identities - No. & % exact residue matches Positives - No. and % similar & ID matches Gaps - No. & % gaps introduced Score - Summed HSP score (S) Bit Score - a normalized score (S’) Expect (E) - Expected # of chance HSP aligns P - Probability of getting a score > X T - Minimum word or k-tuple score (Threshold)

Lecture BLAST - Rules of Thumb Expect (E-value) is equal to the number of BLAST alignments with a given Score that are expected to be seen simply due to chance Don’t trust a BLAST alignment with an Expect score > 0.01 (Grey zone is between ) Expect and Score are related, but Expect contains more information. Note that %Identies is more useful than the bit Score Recall Doolittle’s Curve (%ID vs. Length, next slide) %ID > 30 - numres/50 If uncertain about a hit, perform a PSI-BLAST search

Lecture Doolittle’s Curve Twilight Zone

Lecture Getting the Most from BLAST

Lecture BLAST Options

Lecture BLAST Options Composition-based statistics (Yes) Sequence Complexity Filter (Yes) Expect (E) value (10) Word Size (3) Substitution or Scoring Matrix (Blosum62) Gap Insertion Penalty (11) Gap Extension Penalty (1)

Lecture Composition Statistics Recent addition to BLAST algorithm Permits calculated E (Expect) values to account for amino acid composition of queries and database hits Improves accuracy and reduces false positives Effectively conducts a different scoring procedure for each sequence in database

Lecture LCR’s (low complexity) Watch out for… –transmembrane or signal peptide regions –coil-coil regions –short amino acid repeats (collagen, elastin) –homopolymeric repeats BLAST uses SEG to mask amino acids BLAST uses DUST to mask bases

Lecture Scoring Matrices BLOSUM Matrices –Developed by Henikoff & Henikoff (1992) –BLOcks SUbstitution Matrix –Derived from the BLOCKS database PAM Matrices –Developed by Schwarz and Dayhoff (1978) –Point Accepted Mutation –Derived from manual alignments of closely related proteins

Lecture How to Make Your Own Matrix ACDEFGH.. ACDEFGK.. AADEFGH.. GCDEFGH.. ACAEYGK.. ACAEFAH.. PerformCalculateFill Sub AlignmentFrequenciesMatrix f (A,A) = A A C D C D... E #A obs #A exp f (C,A) = #C/A obs #A exp #C exp +

Lecture PAM versus BLOSUM First useful scoring matrix for protein Assumed a Markov Model of evolution (I.e. all sites equally mutable and independent) Derived from small, closely related proteins with ~15% divergence Much later entry to matrix “sweepstakes” No evolutionary model is assumed Built from PROSITE derived sequence blocks Uses much larger, more diverse set of protein sequences (30% - 90% ID)

Lecture PAM versus BLOSUM Higher PAM numbers to detect more remote sequence similarities Lower PAM numbers to detect high similarities 1 PAM ~ 1 million years of divergence Errors in PAM 1 are scaled 250X in PAM 250 Lower BLOSUM numbers to detect more remote sequence similarities Higher BLOSUM numbers to detect high similarities Sensitive to structural and functional subsitution Errors in BLOSUM arise from errors in alignment

Lecture PAM Matricies PAM 40 - prepared by multiplying PAM 1 by itself a total of 40 times best for short alignments with high similarity PAM prepared by multiplying PAM 1 by itself a total of 120 times best for general alignment PAM prepared by multiplying PAM 1 by itself a total of 250 times best for detecting distant sequence similarity

Lecture BLOSUM Matricies BLOSUM 90 - prepared from BLOCKS sequences with >90% sequence ID best for short alignments with high similarity BLOSUM 62 - prepared from BLOCKS sequences with >62% sequence ID best for general alignment (default) BLOSUM 30 - prepared from BLOCKS sequences with >30% sequence ID best for detecting weak local alignments

Lecture Scraping the Bottom of the Barrel with Psi-BLAST

Lecture PSI-BLAST Algorithm Perform initial alignment with BLAST using BLOSUM 62 substitution matrix Construct a multiple alignment from matches Prepare position specific scoring matrix Use PSSM profile as the scoring matrix for a second BLAST run against database Repeat steps 3-5 until convergence

Lecture PSI-BLAST

Lecture PSI-BLAST PresS Iterate!

Lecture PSI-BLAST PresS Iterate!

Lecture PSI-BLAST

Lecture PSI-BLAST For Protein Sequences ONLY Much more sensitive than BLAST Slower (iterative process) Often yields results that are as good as many common threading methods SHOULD BE YOUR FIRST CHOICE IN ANALYZING A NEW SEQUENCE

Lecture BLAST against PDB

Lecture Still Confused?

Lecture Conclusions BLAST is the most important program in bioinformatics (maybe all of biology) BLAST is based on sound statistical principles (key to its speed and sensitivity) A basic understanding of its principles is key for using/interpreting BLAST output Use NBLAST or MEGABLAST for DNA Use PSI-BLAST for protein searches