Introduction to BLAST

1. Introduction to BLAST David Fristrom Bibliographer/Librarian Science and Engineering Library fristrom@bu.edu 617 358-4124

2. What is BLAST? Free, online service from National Center for Biotechnology Information (NCBI) http://blast.ncbi.nlm.nih.gov/Blast.cgi

3. What is BLAST? as Google : Internet BLAST : Nucleotide/Protein Sequence Databases

4. Some Uses for BLAST • Identify an unknown sequence • Build a homology tree for a protein • Get clues about protein structure by finding similar proteins with known structures • Map a sequence in a genome • Etc., etc.

5. What is BLAST? Basic Local Alignment Search Tool

6. Alignment AACGTTTCCAGTCCAAATAGCTAGGC ===--=== =-===-==-====== AACCGTTC TACAATTACCTAGGC Hits(+1): 18 Misses (-2): 5 Gaps (existence -2, extension -1): 1 Length: 3 Score = 18 * 1 + 5 * (-2) – 2 – 2 = 6

7. Global Alignment • Compares total length of two sequences • Needleman, S.B. and Wunsch, C.D. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol. 48(3):443-53(1970).

8. Local Alignment • Compares segments of sequences • Finds cases when one sequence is a part of another sequence, or they only match in parts. • Smith, T.F. and Waterman, M.S. Identification of common molecular subsequences. J Mol Biol. 147(1):195-7 (1981)

9. Search Tool • By aligning query sequence against all sequences in a database, alignment can be used to search database for similar sequences • But alignment algorithms are slow

10. What is BLAST? • Quick, heuristic alignment algorithm • Divides query sequence into short words, and initially only looks for (exact) matches of these words, then tries extending alignment. • Much faster, but can miss some alignments • Altschul, S.F. et al. Basic local alignment search tool. J Mol Biol. 215(3):403-10(1990).

11. What is BLAST? • BLAST is not Google • BLAST is like doing an experiment: to get good, meaningful results, you need to optimize the experimental conditions

12. Sample Search • Human beta globin (HBB) • Subunit of hemoglobin • Acquisition number: NP_000509 • Limit to mouse to more easily show differences between searches

13. Interpreting Results • Score: Normalized score of alignment (substitution matrix and gap penalty). Can be compared across searches • Max score: Score of single best aligned sequence • Total score: Sum of scores of all aligned sequences

14. Interpreting Results • Query coverage: What percent of query sequence is aligned • E Value: Number of matches with same score expected by chance. For low values, equal to p, the probability of a random alignment • Typically, E < .05 is required to be considered significant

15. Getting the most out of BLAST • What kind of BLAST? • Pick an appropriate database • Pick the right algorithm • Choose parameters

16. Step 0: Do you need to use BLAST?

17. Step 1:Nucleotide BLAST vs. protein BLAST • Largely determined by your query sequence BUT • If your nucleotide sequence can be translated to a peptide sequence, you probably want to do it (use tool such as ExPASy Translate Tool) • Protein blasts are more sensitive and biologically significant • Sometimes it makes sense to use other blasts

18. Specialized Search: blastx • Search protein database using a translated nucleotide query • Use to find homologous proteins to a nucleotide coding region • Translates the query sequence in all six reading frames  • Often the first analysis performed with a newly determined nucleotide sequence http://www.ncbi.nlm.nih.gov/blast/producttable.shtml#blastx

19. Specialized Search: tblastn • Search translated nucleotide database using a protein query • Does six-frame translations of the nucleotide database • Find homologous protein coding regions in unannotated nucleotide sequences such as expressed sequence tags (ESTs) and draft genome records (HTG) http://www.ncbi.nlm.nih.gov/blast/producttable.shtml#tblastn

20. Specialized Search: tblastx • Search translated nucleotide database using a translated nucleotide query • Both translations use all six frames • Useful in identifying potential proteins encoded by single pass read ESTs • Good tool for identifying novel genes • Computationally intensive   http://www.ncbi.nlm.nih.gov/blast/producttable.shtml#tblastx

21. Even More Specialized • Make specific primers with Primer-BLAST • Search trace archives • Find conserved domains in your sequence (cds) • Find sequences with similar conserved domain architecture (cdart) • Search sequences that have gene expression profiles (GEO) • Search immunoglobulins (IgBLAST) • Search for SNPs (snp) • Screen sequence for vector contamination (vecscreen) • Align two (or more) sequences using BLAST (bl2seq) • Search protein or nucleotide targets in PubChem BioAssay • Search SRA transcript libraries • Constraint Based Protein Multiple Alignment Tool

22. Step 2: Choose a Database • Too large: • Takes longer • Too many results • More random, meaningless matches • Too small or wrong one: • Miss significant matches

23. Protein Databases • Non-redundant protein sequences (nr) • Kitchen-sink: • Translations of GenBank coding sequences (CDS) • RefSeq Proteins • PDB (RCSB Protein Data Bank - 3d-structure) • SwissProt • Protein Information Resource (PIR) • Protein Research Foundation (Japanese DB) • Reference proteins (refseq_protein) • NCBI Reference Sequences: Comprehensive, integrated, non-redundant, well-annotated set of sequences • Swissprot protein sequences (swissprot) • Swiss-Prot: European protein database (no incremental updates)

24. Protein Databases • Patented protein sequences (pat) • Patented sequences • Protein Data Bank proteins (pdb) • Sequences from RCSB Protein Data Bank with experimentally determined structures • Environmental samples (env_nr) • Protein sequences from environmental samples (not associated with known organism)

25. Nucleotide Databases • Human genomic + transcript • http://www.ncbi.nlm.nih.gov/genome/guide/human/ • Mouse genomic + transcript • http://www.ncbi.nlm.nih.gov/genome/guide/mouse/ • Nucleotide collection (nr/nt) • “nr” stands for “non-redundant,” but it isn’t • GenBank (NCBI) • EMBL (European Nucleotide Sequence Database) • DDBJ (DNA Databank of Japan) • PDB (RCSB Protein Data Bank - 3d-structure) • Kitchen sink but not HTGS0,1,2, EST, GSS, STS, PAT, WGS

26. Nucleotide Databases • Reference mRNA sequences (refseq_rna) • Reference genomic sequences (refseq_genomic) • NCBI Reference Sequences: Comprehensive, integrated, non-redundant, well-annotated set of sequences • NCBI Genomes (chromosome) • Complete genomes and chromosomes from Reference Sequences

27. Nucleotide Databases • Expressed sequence tags (est) • Non-human, non-mouse ESTs (est_others) • http://www.ncbi.nlm.nih.gov/About/primer/est.html • http://www.ncbi.nlm.nih.gov/dbEST/index.html • Genomic survey sequences (gss) • Like EST, but genomic rather than cDNA (mRNA) • random "single pass read" genome survey sequences. • cosmid/BAC/YAC end sequences • exon trapped genomic sequences • Alu PCR sequences • transposon-tagged sequences • http://www.ncbi.nlm.nih.gov/dbGSS/index.html

28. Nucleotide Databases • High throughput genomic sequences (HTGS) • Unfinished sequences (phase 1-2). Finished are already in nr/nt • http://www.ncbi.nlm.nih.gov/HTGS/ • Patent sequences (pat) • Patented genes • Protein Data Bank (pdb) • Sequences from RCSB Protein Data Bank with experimentally determined structures • http://www.rcsb.org/pdb/home/home.do

29. Nucleotide Databases • Human ALU repeat elements (alu_repeats) • Database of repetitive elements • Sequence tagged sites (dbsts) • Short sequences with known locations from GenBank, EMBL, DDBJ • Whole-genome shotgun reads (wgs) • http://www.ncbi.nlm.nih.gov/Genbank/wgs.html

30. Nucleotide Databases • Environmental samples (env_nt) • Nucleotide sequences from environmental samples (not associated with known organism)

31. Database Options • Limit to (or exclude) an organism • Exclude Models (XM/XP) •  Model reference sequences produced by NCBI's Genome Annotation project. These records represent the transcripts and proteins that are annotated on the NCBI Contigs … which may have been generated from incomplete data. • Entrez Query • Use Entrez query syntax to limit search

32. Step 3:Choose an Algorithm • How close a match are you looking for? • Determines how similarities are “scored” • Affects speed of search and chance of missing match • Again, what is the goal of the search?

33. blastp • Protein-protein BLAST • Standard protein BLAST

34. PSI-BLAST • Protein-protein BLAST • Position-Specific Iterated BLAST • Finds more distantly related matches • Iterates: Initial search results provide information on “allowed” mutations; subsequent searches use these to create custom substitution matrix

35. PHI-BLAST • Protein-protein BLAST • Pattern Hit Initiated BLAST • Variation of PSI-BLAST • Specify a pattern that hits must match • Use when you know protein family has a signature pattern: active site, structural domain, etc. • Better chance of eliminating false positives • Example: VKAHGKKV

36. megablast • Nucleotide BLAST • Finds highly similar sequences • Very fast • Use to identify a nucleotide sequence

37. blastn • Nucleotide BLAST • Use to find less similar sequences

38. discontiguous megablast • Nucleotide BLAST • Bioinformatics. 2002 Mar;18(3):440-5. PatternHunter: faster and more sensitive homology search. Ma B, Tromp J, Li M. • Even more dissimilar sequences • Use to find diverged sequences (possible homologies) from different organisms

39. Step: 4Algorithm Parameters Fine-tune the algorithm • Short Queries • Expect threshold: The lower it is, the fewer false positives (but you might miss real hits)

40. Algorithm Parameters Scoring Matrix: • PAM: Accepted Point Mutation • Empirically derived chance a substitution will be accepted, based on closely related proteins • Higher PAM numbers correspond to greater evolutionary distance • BLOSUM: Blacks Substitution Matrix • Another empirically derived matrix, based on more distantly related proteins • Lower BLOSUM numbers correspond to greater evolutionary distance • Compositional adjustment changes matrix to take into account overall composition of sequence

41. Algorithm Parameters Filters and Masking • Can ignore low complexity regions in searching

42. Additional Sources • Pevsner, Jonathan Bioinformatics and Functional Genomics, 2nd ed. (Wiley-Blackwell, 2009) • BLAST help pages: http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs • Slides from class on similarity searching; lots of technical details on algorithms and similarity matrices: http://www.ncbi.nlm.nih.gov/Class/NAWBIS/Modules/Similarity/simsrchlast.html