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BLAST
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BLAST

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  1. BLAST Objectives • Gain familiarity with sequence searches and comparisons via web-based BLAST • To understand the BLAST algorithm • To understand the principles of BLAST scoring and BLAST statistics • To understand scoring matrices • To become aware of other BLAST services and applications

  2. BLAST • Basic Local Alignment Search Tool • Developed in 1990 and 1997 (Altschul et al.) • A heuristic method for performing local alignments through searches of high scoring pairs (HSPs) • First to use statistics to predict significance of initial matches – saves on false leads • Offers both sensitivity and speed

  3. BLAST • Looks for clusters of nearby or locally dense “similar or homologous” words/k-tuples • Uses look-up tables to shorten the search time • Use larger “word size” than FASTA to accelerate the search process • Does both Global and Local Alignment • Fastest and most frequently used sequence alignment program tool – de facto standard

  4. BLAST • NCBI BLASThttp://www.ncbi.nih.gov/BLAST/ • European Bioinformatics Institute • NCBI BLASThttp://www.ebi.ac.uk/Tools/sss/ncbiblast/ • WUBLASThttp://www.ebi.ac.uk/Tools/sss/wublast/ • Rosaceae BLAST (www.rosaceae.org) • Legume BLAST (http://lis.comparative-legumes.org; www.gabcsfl.org) • Grasses BLAST (www.gramene.org)

  5. Types of BLAST • BLASTP – protein query against protein DB • BLASTN – DNA/RNA query against DNA DB • BLASTX – 6 frame translation of DNA query against protein DB • TBLASTN – protein query against 6 frame translation of DNA DB • TBLASTX – 6 frame translation of DNA query against 6 frame translation of DNA DB • BLAST2SEQ – for performing pairwise alignments for 2 chosen sequences

  6. Types of BLAST • PSI-BLAST - protein “profile” query against protein DB • PHI-BLAST – protein pattern against protein DB • RPS-BLAST – Conserved Domain Detection • MEGABLAST – for comparison of large sets of long DNA sequences • Primer BLAST – uses Primer3 to design PCR primers • Genomic BLAST – for alignments against completed genomes • VecScreen – for detecting cloning vector contamination in sequenced data. see last weeks handout for rest of them

  7. Types of Comparison • What program will best suite your query and desired output? • DNA sequences contain less information with which to deduce homology than do the encoded protein sequences when compared using simple nucleotide substitution scores, 20 aa vs 4 nt! • Protein comparisons give more meaningful results • Moderately similar nt sequences often a highly similar protein sequence

  8. NCBI WEB BLAST Step 1: Select a BLAST programIn this example we willchoose nucleotide blast

  9. Basic BLAST Options Step 2: Type in your sequence in FASTA format or type in a GI or accessionnumber or upload a file • Using Nucleotide BLAST >my protein MT08976 KIQIYTGTCANGTCKIQIYTGTCANGTCKIQIY GTCANGTCKIQIYTGTCANGTC Step 3: Give your search a name/title Step 4: Choose a database to search MEGABLAST is specifically designed to efficiently find long alignments between very similar sequences

  10. NCBI WEB BLAST

  11. Step 2: Type in your sequence in FASTA format or type in a GI or accessionnumber or upload a file Basic BLAST Options • Using Nucleotide BLAST >my protein MT08976 KIQIYTGTCANGTCKIQIYTGTCANGTCKIQIY GTCANGTCKIQIYTGTCANGTC Note you can also restrict the range of your query to be searched Step 3: Give your search a name/title Step 4: Choose a database to search and/or restrict the database selection e.g. Viridiplantae [ORGN] of the nr Means restrict my search to just plant proteins

  12. Basic BLAST Options Step 5: Choose a BLAST program • Using Nucleotide BLAST Megablast is intended for comparing a query to closely related sequences and works best if the target percent identity is 95% or more but is very fast. Discontiguous megablast uses an initial seed that ignores some bases (allowing mismatches) and is intended for cross-species comparisons. BlastN is slow, but allows a word-size downto seven bases.

  13. Basic BLAST Options Steps 2 – 4 the same as in nt blast • Using Amino Acid BLAST Step 5: Choose a BLAST program BlastP simply compares a protein query to a protein database.It is used for finding similar sequences in protein databases. It is designed to find local regions of similarity but when sequence similarity spans the whole sequence, blastp will also report a global alignment. PSI-BLAST is the most sensitive BLAST program, making it useful for finding very distantly related proteins or new members of a protein family. PHI-BLAST performs the search but limits alignments to those that match a pattern in the query.

  14. Basic BLAST Options Steps 2 – 4 the same as in nt blast • BLASTP RESULTS for NP_001031578.1

  15. Basic BLAST Options Steps 2 – 4 the same as in nt blast • BLASTP RESULTS for NP_001031578.1

  16. BLAST OUTPUT by Column • The sequence accession name: this takes you to the database entry that contains the sequence. • Description of the match organism and any assigned/putative function • The alignment score. Higher scoring hits are at the top • Query coverage is how much of your sequence aligned to the match • The expectation value (E Value) which provides an estimate of statistical significance. This tells you the number of times you could have expected such a good match only by chance. The E value provides you with the most important measure of statistical significance.

  17. Interpreting Significance of BLAST Results • In general:DNA to DNA alignmentFor nucleotide sequences at least 100 bp long, if 70% of your nucleotides are identical with your match sequence then they can be considered to be homologousAA to AA alignmentFor amino acid sequences at least 100 aa long, if 25% of your aa are identical with your match sequence then they can be considered to be homologous Below these values, the alignments are considered to be in the twilight zone! However how do you tell the difference between 60 matched residues spread over a 100 residue segment and 120 matches spread over a 200 residue segment? The longest is probably the more meaningful but the percent identitysays nothing about this!

  18. Interpreting Significance of BLAST Results • So we use E values. In theory any match with an E value below 1 should all be trusted. In practice this is NOT true because BLAST uses an approximate formula for computing E values and strongly underestimates them. • Rule of thumb, look for E values above 1e-4 (0.0001). So if you want to be certain of homology , your E value must be lower than 0.0001 • Caveat – if you are doing a blast search with thousands of query sequences you need to take into account the size of your query database and lower the E value further.

  19. Interpreting Significance of BLAST Results • 15,000 EST query sequences • A 10-3 E-value cutoff means that you should expect one false positive in 1000 searches. • Thus with 15,000 searches, we should expect 15 false positives with a cutoff of 10-3. • To reduce the chances of identifying a false positive, set the E-value cutoff lower. • For 15,000 searches, an E-value cutoff of 10-5 will mean that you should expect 0.15 false positives. Most of the time we make it even lower < 10-6

  20. Database Searching Questions Sequence Analysis I • What database should I search? • What kind of sequences should I search with? • What E-value is significant? • What can I reliably infer about the function of my sequence based on homology?

  21. Sequence Analysis I Databases • Bigger databases have more sequences. • Bigger databases are also more redundant, which can skew the statistics. • Bigger databases are also poorly annotated (homology with an "unidentified sequence" doesn't really tell you much) • Bigger databases take lots of time to search.

  22. Sequence Analysis I Databases Cont. • Smaller databases (like Swiss-Prot) are often better curated and annotated. • Smaller databases are much less redundant. • Smaller databases can contain phylogenetically relevant sequences (all plant) • Smaller databases are much faster to search.