Database Searching Using BLAST

Transcription

1 Mahidol University Objectives SCMI512 Molecular Sequence Analysis Database Searching Using BLAST Lecture 2B After class, students should be able to: explain the FASTA algorithm for database searching explain the original BLAST algorithm for database searching interpret BLAST output Pravech Ajawatanawong, Ph.D. Department of Microbiology Faculty of Science Mahidol University Searching against Database FASTA Keyword search use keyword (field value) to search in the database optional forse to search in some field (tag option) powerful search with Boolean operation Homology search compare a sequence (query) against all sequences that stored in the database (subject) pairwise sequence alignment for all combustion (greedy method) time consuming FASTA and BLAST algorithm (both use local alignment) FASTA = FAST-ALL FASTA is the very first algorithm for searching sequence against all sequences in a database algorithm is developed from the concept of dot-plot step 1: identify the exact match of substring in a dot-plot space step 2: extend the alignment in both sides to find the best region of alignment step 3: optimize alignment using dynamic programing

2 Some Parameters for Text Analysis FASTA Algorithm step by step sequence = { ATTACGTGCTGAACGGTAGCTGGATTGCTGAGTCTGATCGTAG } sub-sequence 1 = { TGCTGAACGGTAGCTGGA } sub-sequence 2 = { TGAGTCTGATCGTAG } string = type of variables contains several characters DNA is a string of four letter A, T, C and G in any pattern (basic definition) k-mer or substring = subsequence with a defining length (e.g.: k-mer = 8 means sequence with 8 residues in length) FASTA stands for FAST-ALL based on dot plot for identification of regions with exact match default for DNA = 6 nucleotides default for protein = 2 amino acid residues each regions will be recalculated the alignment score using PAM matrix (for protein sequence) FASTA Algorithm step by step FASTA Algorithm step by step only top 10 regions with the highest identity scores are kept (regions with lower identity scores are discarded) pieces of diagonal line represent regions of exact match sequence, called word horizontal and vertical offsets between two diagonal lines have been added to make the longest alignment region

3 FASTA Algorithm step by step FASTA Algorithm step by step compute dynamic programming for the best alignment regions (red area) the large gap region will be ignored to maintain the alignment score Original Basic Local Alignment Search Tool BLAST begins with the generation of words (w) or substring) default for nucleotide sequences = 11 bases default for protein sequences = 3 residues maximum number of words generated from a query sequence with length L = L w + 1 ex.: (10) (3) + 1 = 8 number of word might be smaller than L w + 1 if some words are redundant N L G L I R A V E K N L G L G L G L I L I R I R A R A V breaking query into several words A V E V E K

4 N L G L I R A V E K N L G L G L G L I L I R I R A R A V A V E V E K compare the words with sequences in the database and identification of the exact match time consuming process N L G L I R A V E K N L G neighborhood words Word Score NLM 5 NLV 7 NLF 5 NLW 1 NMG 12 NCG 2 HLG 12 WLG 5 BLAST will calculate score for each words and their neighborhood words using PAM120 matrix any neighborhood word with neighborhood score threshold (T) > 10 will be kept in the list For PAM120, any amino acid that is hardly replaced to the original amino acid will have negative scores, otherwise the scores will be positive. Word query sequence NMG HLG scanning the sequences in a database with the list of neighborhood words (in the table) With this method, sequence alignment no need to be 100% identity (allow some mismatch, based on natural selection) For nucleotide sequence, the original BLAST scores match = +5 and mismatch = 4. BLAST will map the neighborhood words against the query and sequences in the database BLAST identifies a main diagonal line and search for the hits in the main dragon line all hits that are not located on the main diagonal line will be discarded a sequence in the database main diagonal line However, other scoring systems (user define) are acceptable.

5 HSP Each word match (hit) will be extended by adding a single residue of amino acid in both sides. This will generate an ungapped alignment. Once BLAST adds a new residue, it will calculate a score for word match, called High-scoring Segment Pairs (HSPs). cumulative score word X extension extension will stop when the score (S) is decreased than X X is the highest score value during the process of extension BLAST prone to report several short fragments with high score than a long fragment of sequence with lower score BLAST Algorithm summary query sequence any multiple hits that have a small gap in between, can be merged into a bigger HSP a sequence in the database A step 1 generate words from the query sequences (word size of DNA = 11 and protein = 3 by default) step 2 identify neighborhood words that have score > T step 3 scan the sequences in a database with the words in the list step 4 identify hit and extension hit by adding ungapped residue in both directions step 5 gapped extension to make the longer HSPs

6 BLAST Main Page BLASTn Header of BLAST Result BLAST Result graphic summary Reference Zheng Zhang, Scott Schwartz, Lukas Wagner, and Webb Miller (2000), "A greedy algorithm for aligning DNA sequences", J Comput Biol 2000; 7(1-2): Reference - database indexing Aleksandr Morgulis, George Coulouris, Yan Raytselis, Thomas L. Madden, Richa Agarwala, Alejandro A. Schäffer (2008), "Database Indexing for Production MegaBLAST Searches", Bioinformatics 24: red (>=200) excellent pink (80 200) good green (50 80) fine, but should consider the alignment too blue (40 50) hmm!! not OK black (<40) untrustable

7 BLAST Result description section E-value Is Not P-value p-value Max score the highest alignment score (if BLAST returned more than one HSP) Total score sum of the alignment score (but not arithmetic sum). If BLAST returned only one HSP, then the Max score will equal to Total score Query cover how much the HSP cover the query sequence in length E-value parameter for description of the background noise of search Ident % identity between query and subject sequences S (non-significant) S (significant) P-value probability that an alignment with this score occurs by chance in a database size N E-value number of matches with this score one can expect to find by chance in a database of size N BLAST Result alignment section BLAST Family nucleotide sequence blastn nucleotide database translation in all six frames protein sequences tblastx protein database translation in all six frames tblastn blastx protein sequence blastp protein database

8 BLASTp BLASTn compare a protein sequence with a protein database identify function of sequence compare two distinctly related sequences (sequences of organisms in different groups family, phylum or kingdom) identify common region (domain) in the protein compare a nucleotide sequence with a nucleotide database mapping short nucleotide sequence (e.g. cdna or PCR product) to a genome annotate genomic DNA searching for DNA repeats limitation work only coding sequences tblastn tblastx compare a protein sequence with a nucleotide database discovery of a new gene fromm multiple species map protein to genomic DNA compare a DNA translated into protein sequences with a nucleotide database translated into protein sequences cross-species gene prediction at the genome or transcript level (EST) searching for gene not yet in protein database

9 blastx compare a DNA translated into protein sequences with a protein database searching for protein coding gene in genomic DNA identify function of mrna or determine that mrna corresponds to a known protein