1. School B&I TCD Bioinformatics Database homology searching May 2010

2. Why search a Database To find homologous sequences to your unknown to determine function To find other related sequences to do evolutionary studies (trees) or to make specialised database (nematode 16sRNA) To find the mouse or E.coli homolog of your gene of interest To find genes in a newly sequenced genome To predict 3-D structure (blast vs PDB)

3. BLAST For many molecular biologists a Blast search –with a DNA sequence –at NCBI –accepting default parameters IS bioinformatics –70% of searches at NCBI at blastN

4. BLAST Basic local alignment search tool More or less unchanged for 20 years Extraordinarily quick: –Submit seq via satellite to Bethesda MD –Crunch thro 400 GB of sequence –Return results via satellite –ALL in 30 seconds Available at other sites (EBI, ExPaSy etc.) with better response times, fewer conditions

5. NCBI penalties 1st request: current time 2nd request: current time + 60 seconds 3rd request: current time + 120 seconds 4th request: current time + 180 seconds 5th request: current time + 240 second DON’T submit multiple simultaneous queries

6. Reliable servers http://www.ch.embnet.org/software/bBLAST.html http://www.ch.embnet.org/software/aBLAST.html –Basic and advanced SwissBlast http://www.ebi.ac.uk/blast2/index.html –EBI (Cambridge, UK) NCBI Blast http://www.ncbi.nlm.nih.gov/blast/ –Not the only Blast server!

7. BLAST varieties blastn: searches a DNA sequence against a DNA database like EMBL, Genbank, or dbEST. also megablast for exact matches blastp: searches a protein sequence against a protein database such as UniProt, or "nr" a non-redundant database which ideally contains one copy of every available sequence. Then you have: blastx: searches a DNA sequence (translated in all six reading frames) against a protein database. tblastn: searches a protein sequence against a DNA database (translated in all six reading frames) – essential for searching EST databases. and in the interests of completeness there is: tblastx: searches a DNA sequence (translated in all six reading frames) against a DNA database (translated in all six reading frames). finally Psi-blast an iterative process using position specific subst matrix PSSM to find distantly related sequences

8. Algorithm Five steps –Break the query seq into short “words” – typically 3 consecutive residues for protein –Search the database for (nearly) exact matches to these words –Extend match “hits” out on either side until score stops going up – these are HSPs (high scoring segment pairs) –Sort the HSPs by some “optimum” criterion –Significant hits are then formally scored, aligned and displayed

9. Alternatives to BLAST FASTA –A little slower than Blast –More sensitive (so recommended) for DNA to DNA searches Smith-Waterman (Blitz) –Much slower than Blast (20x slower) –Much more sensitive But Blast is standard because it gives a “good enough” answer quickly.

10. Blast Blast at NCBI Parallel search in Conserved domain DB Paste your query sequence here

11. Input sequence parameters Optional parameters to change include Parameters that alter the hits found –Database searched –Word size –Substitution matrix –Gap penalties –Low complexity masking Parameters that alter the results delivered –Expectation cut-off –Limit by organism or taxonomic group –Number of hits reported –Number of alignments shown

12. Database If query is a coding gene: translate and search protein database Search PDB if you want a 3-D structure Search NR if you want any hit Search UniProt to know what the hits are Search dbEST to know if your sequence is expressed UniProt90: no seq is more than 90% ident to any other (for an uncluttered tree) also UniProt50

13. Word size Default at NCBI Blast –11 for DNA; option of 7 and 5 –3 for protein; option of 2 Increasing wordsize will speed search … but will lose sensitivity –It will miss some useful but distant hits Decreasing wordsize will be more sensitive … but take longer

14. Substitution matrix By default BLAST uses BLOSUM62 Can change this –Blosum90 mirrors changes in closely related sequences –Blosum30 changes in distant sequences Should run 3 blast searches with different substitution matrices.

15. Substitution matrices Top left part of a BLOSUM 90 matrix A R N D C Q E G H I L A 5 -2 -2 -3 -1 -1 -1 0 -2 -2 -2 R -2 6 -1 -3 -5 1 -1 -3 0 -4 -3 N -2 -1 7 1 -4 0 -1 -1 0 -4 -4 D -3 -3 1 7 -5 -1 1 -2 -2 -5 -5 C -1 -5 -4 -5 9 -4 -6 -4 -5 -2 -2 Q -1 1 0 -1 -4 7 2 -3 1 -4 -3 E -1 -1 -1 1 -6 2 6 -3 -1 -4 -4 G 0 -3 -1 -2 -4 -3 -3 6 -3 -5 -5 H -2 0 0 -2 -5 1 -1 -3 8 -4 -4 I -2 -4 -4 -5 -2 -4 -4 -5 -4 5 1 L -2 -3 -4 -5 -2 -3 -4 -5 -4 1 5 Changes 30-62-90 not linear Positive off- diagonals are similar

16. Reality of matrix change Query: GHDEICI GH + C Sbjct: GHACNCG –Scores 39 with Blosum30; 5 with Blosum90 Query: HEQCRLEN +E LEN Sbjct: QENAHLEN –Scores 19 with Blosum30; 24 with Blosum90 So GHDEICIHEQCRLEN will find different hits

17. Matrix families PAM – point accepted mutation –Margaret Dayhoff 1970s (before Genbank) BLOSUM – based on aligned blocks –Henikoff and Henikoff 1992 –Blosum 62 default (based on aligned seqs 62% identical – don’t ask why 62: it works) Low PAM = High Blosum –PAM 250 == Blosum 30 –PAM 30 == Blosum 90

18. Gap penalty Original Blast was gap-free –Because gapped aligns much more CPU Now can insert “affine” gaps –Gap open 10; gap extend 1 Raise gap penalty to discourage gaps –Preferentially gets closely related hits Lower gap penalty for sensitive search for distant relatives

19. Low Complexity Masking Seg masks low-complexity regions –“too many” serines, prolines etc. What a masked sequence looks like: >P04729 Wheat gamma gliadin MKTFLVFALIAVVATSAIAQMETSCISGLERPWQQQPLPPQQSFSQQPPFSQQQQQPLPQ QPSFSQQQPPFSQQQPILSQQPPFSQQQQPVLPQQSPFSQQQQLVLPPQQQQQQLVQQQI PIVQPSVLQQLNPCKVFLQQQCSPVAMPQRLARSQMWQQSSCHVMQQQCCQQLQQIPEQS RYEAIRAIIYSIILQEQQQGFVQPQQQQPQQSGQGVSQSQQQSQQQLGQCSFQQPQQQLG QQPQQQQQQQVLQGTFLQPHQIAHLEAVTSIALRTLPTMCSVNVPLYSATTSVPFGVGTG VGAY* and after low complexity masking : >P04729 SEG low-complexity masked MKTFLVFALIAVVATSAIAQMETSCISGLERPWXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXLNPCKVFLQQQCSPVAMPQRLARSQMWXXXXXXXXXXXXXXXXXXXXXXX RYEAIRAIIYSIIXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXHQIAHLEAVTSIALRTLPTMCSVNVPLYSATTSVPFGVGTG VGAY* Xnu masks repeats (from database of common repeats)

20. Masking Check if masking on/off by default (differs at sites) Run 2 searches with masking on, masking off ANNYway Masking by hand: –If you know about the DNA binding domain already Which is really common and will occupy the top 100 hits against any database –Replace the region with Xs –Re-run blast to find other motifs/domains/information –NCBI blast allows select subsequences DUST masks low info DNA sequences –Like polyA tails

21. Advanced

22. Expectation cut-off E value –Expected number of chance hits Given the database searched Query HSP length Related to probability Default is 10: “expect to find 10 hits as good as this by chance alone” E values less than 10 -4 unreliable So different from p < 0.05 For short seqs crank E up to 100 or 1000

23. Data deluge – hits delivered Default suits general purpose V = 50 num of “hits” one line descriptions B = 50 num of alignments between query and hit which are displayed

24. Swiss Blast

25. EBI blast

26. Limit by taxon/organism Also search at www.ensembl.org for “genomed” organismswww.ensembl.org

27. Database choice NR for getting aNNy hit swissprot or refseq for getting hits that have annotation Month for recent hits

28. The output Is in five parts 1.Admin – date, size of database, id of query 2.Graphic display of query and hits 3.List of hits with links to database and to… 4.…alignments (may be > 1 HSP per hit) 5.More admin, including errors/warnings

29. Notes! Record the top admin stuff. Your search will be different –Next week –On a different server –With different DB –With different input parameters Mouse-over any graphic display –Shows domain structure –Shows if hit is global or local Read the bottom admin stuff

30. Blast output sp|P06471|HOR3_HORVU B3-HORDEIN Length = 264 Score = 62.5 bits (149), Expect = 1e-09 Identities = 32/63 (50%), Positives = 38/63 (59%) Query: 131 LNPCARSQMWXXXXXXXXXXXXXXXXXXXXXXXRYEAIY LNPCARSQM R+EA+Y Sbjct: 111 LNPCARSQMLQQSSCHVLQQQCCQQLPQIPEQLRHEAVY Query: 191 SII 193 SI+ Sbjct: 171 SIV 173 Low-complexity masked region What sort of residues masked in The query sequence?? May be more HSPs for same hit If big deletion in either seq

31. General blast protocol 1.Find server, choose DB, paste seq, GO 2.Rerun search with/out masking 3.Rerun search with two diff subs matrix 4.2 x 3 for six searches 5.If top N hits all same family/domain then XXX this region and resubmit 6.LOOK at the results; esp strange ones 7.Limit results by organism

32. Blast notes Twilight zone <25% protein <70% NA Because two sequences give high blast scores to a third, doesn’t mean they are related –NNNNDOMAINANNNNDOMAINBNNN –NNNNDAMIANANNNNNNNNNNNNNN –NNNNNNNNNNNNNNNDIMIAMBNNN E-value vs % ID. >10 -4 unreliable Bit score <50 unreliable

33. PSI-BLAST Uses a PSSM rather than BloSum/PAM Iterative … –Can find very distant relatives –…so deep insight BUT can iterate off with the fairies