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Database Similarity Search

Database Similarity Search. Why do we care to align sequences?. Sequences that are similar probably have the same function. new sequence. ?. Similar function. ≈. Discover Function of a new sequence. Sequence Database. Discover Function of a new sequence.

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Database Similarity Search

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  1. Database Similarity Search

  2. Why do we care to align sequences? • Sequences that are similar probably have the same function

  3. new sequence ? Similar function ≈ Discover Function of a new sequence Sequence Database

  4. Discover Function of a new sequence

  5. Searching Databases for similar sequences Naïve solution: Use exact algorithm to compare each sequence in the database to query. Is this reasonable ?? How much time will it take to calculate?

  6. Complexity for genomes • Human genome contains3  109base pairs • Searching an mRNA against HG requires~1012 cells -Even efficient exact algorithms will be extremely slow when preformed millions of times even with parallel computing.

  7. So what can we do?

  8. Searching databases Solution: Use a heuristic (approximate) algorithm

  9. Heuristic strategy Reduce the search space Remove regions that are not useful for meaningful alignments Perform efficient search strategies Preprocess database into new data structure to enable fast accession

  10. Heuristic strategy • Reduce the search space Remove regions that are not useful for meaningful alignments • Preprocess database into new data structure to enable fast accession

  11. What sequences to remove? • AAAAAAAAAAA • ATATATATATATA • Transposable elements 53% of the genome is repetitive DNA Low complexity sequences (JUNK???)

  12. Low Complexity Sequences What's wrong with them? * Not informative * Produce artificial high scoring alignments. So what do we do? We apply Low Complexity masking to the database and the query sequence Mask TCGATCGTATATATACGGGGGGTA TCGATCGNNNNNNNNCNNNNNNTA

  13. Heuristic strategy • Remove low-complexity regions that are not useful for meaningful alignments • Perform efficient search strategies Preprocess database into new data structure to enable fast accession

  14. BLAST Basic Local Alignment Search Tool • General idea - a good alignment contains subsequences of high identity (local alignment): ACGCCCGGGAGCGC CTGGGCGTATAGCCC • First, identify (most efficiently) short almost exact matches . • Next, extended to longer regions of similarity. • Finally, optimize the alignment using an exact algorithm. Altschulet al 1990

  15. DNA/RNA vs protein alphabet DNA(4) RNA(4) Protein (20) A T G C A U G C ACDEFGHIKLMNPQRSTVWY A T=A G…. A T=A G…. A G>>A W…. WHY is it different?

  16. The 20 Amino Acids

  17. The 20 Amino Acids A G W

  18. Scoring system for amino acids mismatches

  19. BLAST Basic Local Alignment Search Tool • General idea - a good alignment contains subsequences of high identity (local alignment): ACGCCCGGGAGCGC CTGGGCGTATAGCCC • First, identify (most efficiently) short almost exact matches . • Next, extended to longer regions of similarity. • Finally, optimize the alignment using an exact algorithm. Altschulet al 1990

  20. BLAST(Protein Sequence Example) First, identify (most efficiently) short almost exact matches between the query sequence and the database. Query sequence…FSGTWYA… Words of length 3: FSG, SGT, GTW, TWY, WYA

  21. BLAST Preprocessing of the database Seq 1 FSGTWYA FSG, SGT, GTW, TWY, WAY Seq 2 FDRTSYV FDR, DRT, RTS, TSY, SYV Seq 3 SWRTYVA SWR, WRT,RTY, TYV, YVA ……. FSG SGT GTW TWY WYA YSG TGT ATW SWY WFA FTG.. SVT. GSW. TWF.. WYS…. Seq 1 BAG OF WORDS Seq 102 Seq 3546

  22. BLAST Query sequence …FSGTWYA… Words of length 3: FSG, SGT, GTW, TWY, WYA… DATABASE FSG SGT GTW TWY WYA YSG TGT ATW SWY WFA FTG SVT GSW TWF WYS…. SEQ N INVIEIAFDGTWTCATTNAMHEWASNINETEEN

  23. BLAST Basic Local Alignment Search Tool • General idea - a good alignment contains subsequences of high identity (local alignment): ACGCCCGGGAGCGC CTGGGCGTATAGCCC • First, identify (most efficiently) short almost exact matches . • Next, extended to longer regions of similarity. • Finally, optimize the alignment an exact algorithm. Altschulet al 1990

  24. BLAST 2.Extend word pairs as much as possible, i.e., as long as the total score increases High-scoring Segment Pairs (HSPs) Q: FIRSTLINIHFSGTWYAAMESIRPATRICKREAD D: INVIEIAFDGTWTCATTNAMHEWASNINETEEN 3. Finally, optimize the alignment using an exact algorithm. Q= query sequence, D= sequence in database

  25. Running BLAST to predict a function of a new protein >Arrestin protein (C. elegance) MFIANNCMPQFRWEDMPTTQINIVLAEPRCMAGEFFNAKVLLDSSDPDTVVHSFCAEIKG IGRTGWVNIHTDKIFETEKTYIDTQVQLCDSGTCLPVGKHQFPVQIRIPLNCPSSYESQF GSIRYQMKVELRASTDQASCSEVFPLVILTRSFFDDVPLNAMSPIDFKDEVDFTCCTLPF GCVSLNMSLTRTAFRIGESIEAVVTINNRTRKGLKEVALQLIMKTQFEARSRYEHVNEKK LAEQLIEMVPLGAVKSRCRMEFEKCLLRIPDAAPPTQNYNRGAGESSIIAIHYVLKLTAL PGIECEIPLIVTSCGYMDPHKQAAFQHHLNRSKAKVSKTEQQQRKTRNIVEENPYFR

  26. How to interpret a BLAST score: • The score is a measure of the similarity of the query to the sequence shown. How do we know if the score is significant? -Statistical significance -Biological significance

  27. How to interpret a BLAST search: For each blast score we can calculate an expectation value (E-value) The expectation value E-value is the number of alignments with scores greater than or equal to score S that are expected to occur by chance in a database search. page 105

  28. BLAST- E value: Increases linearly with length of query sequence Decreases exponentially with score of alignment Increases linearly with length of database m = length of query ; n= length of database ; s= score • K ,λ: statistical parameters dependent upon scoring system and background residue frequencies

  29. What is a Good E-value (Thumb rule) • E values of less than 0.00001 show that sequences are almost always related. • Greater E values, can represent functional relationships as well. • Sometimes a real (biological) match has an E value > 1 • Sometimes a similar E value occurs for a short exact match and long less exact match

  30. How to interpret a BLAST search: • The score is a measure of the similarity of the query to the sequence shown. How do we know if the score is significant? -Statistical significance -Biological significance

  31. Treating Gaps in BLAST >Human DNA CATGCGACTGACcgacgtcgatcgatacgactagctagcATCGATCATA >Human mRNA CATGCGACTGACATCGATCATA Sometimes correction to the model are needed to infer biological significance

  32. Gap Scores • Standard solution: affine gap model wx = g + r(x-1) wx : total gap penalty; g: gap open penalty; r: gap extend penalty ;x: gap length • Once-off cost for opening a gap • Lower cost for extending the gap • Changes required to algorithm

  33. Gapped BLAST 4. Connect several HSPs by aligning the sequences in between them: THEFIRSTLINIHFSGTWYAA____M_ESIRPATRICKREAD INVIEIAFDGTWTCATTNAMHEW___ASNINETEEN The Gapped Blast algorithm allows several segments that are separated by short gaps to be connected together to one alignment

  34. BLAST BLAST is a family of programs Query:DNAProtein Database:DNAProtein

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