Sequence Identification using BLAST

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1 Sequence Identification using BLAST Vivek Krishnakumar JCVI Genomic Science and Leadership Workshop Presented on: 05/26/2016

2 Overview Introduction Why compare sequences? Sequence alignment steps Causes of sequence (dis)similarity Comparing two sequences Scoring a sequence alignment Introduction to BLAST Available programs Similarity statistics: Score and Expect value BLAST output Examples of various types of alignments FIN

3 Premise of Pairwise sequence alignment One sequence by itself is not informative; it must be analyzed by comparative methods against existing sequence databases to develop hypothesis concerning relatives and function. Image from Cesar Dog Food company 3

4 Pairwise sequence alignment as an Experiment Probably the most common experiment done in biology today Formally considered an experiment because you don t know what you ll get until you perform the operation As an experiment, it is based on a hypothesis; it uses a reproducible technique and it generates results that lead to conclusions or more experiments

5 Why compare sequences? Match two (pair-wise) or several (multiple) protein or nucleotide sequences to one another to assess their similarity Sequence similarity suggests similar function Similarity help us investigate evolution

6 Sequence Alignment Sequence alignment is the assignment of residue-residue correspondences. It involves: precise operators for alignment: matching, gaps quantitative scoring system for matches and gaps systematic search among possible alignments Sequence alignment: found by the use of an alignment algorithm ⓷ ⓶ Algorithm: a sequence of instructions that one must perform in order to solve a well-formulated problem ⓵ Problem: describes a class of computational tasks; for instance, an input from that task is one particular problem

7 Causes for sequence (dis)similarity Mutation: a nucleotide at a certain location is replaced by another nucleotide (e.g.: ATA AGA) Insertion: at a certain location one new nucleotide is inserted in between two existing nucleotides (e.g.: AA AGA) Deletion: at a certain location one existing nucleotide is deleted (e.g.: ACTG AC-G) Indel: an insertion or a deletion

8 Comparing two sequences Point mutations, easy: ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGATTCGCCCTATCGTCTATCT Insertions/deletions, difficult to compare: ACGTCTGATACGCCGTATAGTCTATCT CTGATTCGCATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT ----CTGATTCGC---ATCGTCTATCT

9 Scoring a sequence alignment Match score: +1 Mismatch score: +0 Gap penalty: 1 ACGTCTGATACGCCGTATAGTCTATCT ----CTGATTCGC---ATCGTCTATCT Matches: 18 (+1) Mismatches: 2 0 Gaps: 7 ( 1) Score = +11

10 Gap opening and extension penalties We want to find alignments that are evolutionarily likely. Which of the following alignments seems more likely to you? ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGAT ATAGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT AC-T-TGA--CG-CGT-TA-TCTATCT We can achieve this by penalizing more for a new gap, than for extending an existing gap

11 Scoring a sequence alignment (2) Match/mismatch score: +1/+0 Gap opening/extension penalty: 2/ 1 ACGTCTGATACGCCGTATAGTCTATCT ----CTGATTCGC---ATCGTCTATCT Matches: 18 (+1) Mismatches: 2 0 Gap opening: 2 ( 2) Extension: 7 ( 1) Score = +7

12 How can we find an optimal alignment? Finding the alignment is computationally hard: ACGTCTGATACGCCGTATAGTCTATCT CTGAT---TCG CATCGTC--T-ATCT C(27 bases,7 gaps) = ~888,000 possibilities Two options: Dynamic programming (most optimal: but computationally time consuming) Heuristics (most speediest: trade off on optimality, completeness, accuracy/precision

13 BLAST Basic Local Alignment Search Tool (1990) Altschul, Gish, Miller, Myers, & Lipman Uses short-cuts or heuristics to improve search speed Like speed-reading, does not examine every nucleotide of database However, many more choices (parameters) to make to adjust search success (over 30!!) Provides statistical significance Available on the web, standalone, and network clients

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15 BLAST programs

16 Sequence Similarity Searching The statistics are important Discriminating between real and artifactual matches is done using an estimate of probability that the match might occur by chance. Two types of metrics: scores (S) and e- values (E), are associated with BLAST hits

17 Where does the score (S) come from? The quality of each pairwise alignment is represented as a score (S) and the scores are ranked. Scoring matrices are used to calculate the score of the alignment base by base (DNA) or amino acid by amino acid (protein). The alignment score will be the sum of the scores for each position. A C G T A C G T

18 What does the E-value really mean? The significance of each alignment is computed as an E value (E). Expectation value. The number of different alignments with scores equivalent to or better than S that are expected to occur in a database search by chance. The lower the E value, the more significant the score. Statistical significance depends on both the size of the alignments and the size of the sequence database Important consideration for comparing results across different searches E-value increases as database gets bigger E-value decreases as alignments get longer

19 BLAST output List of sequences with scores Raw score (S) Higher is better Depends on aligned length Expect Value (Evalue) Smaller is better Dependent on length and database size List of alignments

20 Very Similar Sequences Query: HBA_HUMAN Hemoglobin alpha subunit Sbjct: HBB_HUMAN Hemoglobin beta subunit Score = 114 bits (285), Expect = 1e-26 Identities = 61/145 (42%), Positives = 86/145 (59%), Gaps = 8/145 (5%) Query 2 LSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHF------DLSHGSAQV 55 L+P +K+ V A WGKV + E G EAL R+ + +P T+ +F F D G+ +V Sbjct 3 LTPEEKSAVTALWGKV--NVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKV 60 Query 56 KGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPA 115 K HGKKV A ++ +AH+D++ + LS+LH KL VDP NF+LL + L+ LA H Sbjct 61 KAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGK 120 Query Sbjct EFTPAVHASLDKFLASVSTVLTSKY 140 EFTP V A+ K +A V+ L KY EFTPPVQAAYQKVVAGVANALAHKY 145

21 Quite Similar Sequences Query: HBA_HUMAN Hemoglobin alpha subunit Sbjct: MYG_HUMAN Myoglobin Score = 51.2 bits (121), Expect = 1e-07, Identities = 38/146 (26%), Positives = 58/146 (39%), Gaps = 6/146 (4%) Query 2 LSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHF------DLSHGSAQV 55 LS + V WGKV A +G E L R+F P T F F D S + Sbjct 3 LSDGEWQLVLNVWGKVEADIPGHGQEVLIRLFKGHPETLEKFDKFKHLKSEDEMKASEDL 62 Query 56 KGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPA 115 Sbjct K HG V AL + + L+ HA K S C++ L + P 63 KKHGATVLTALGGILKKKGHHEAEIKPLAQSHATKHKIPVKYLEFISECIIQVLQSKHPG 122 Query 116 EFTPAVHASLDKFLASVSTVLTSKYR 141 +F +++K L + S Y+ Sbjct 123 DFGADAQGAMNKALELFRKDMASNYK 148

22 Not Similar Sequences Query: Sbjct: HBA_HUMAN Hemoglobin alpha subunit SPAC869.02c [Schizosaccharomyces pombe] Score = 33.1 bits (74), Expect = 0.24 Identities = 27/95 (28%), Positives = 50/95 (52%), Gaps = 10/95 (10%) Query 30 ERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAH 89 ++M ++P P+F+ +H + + +A AL N ++DD+ +LSA D Sbjct 59 QKMLGNYPEV---LPYFNKAHQISL--SQPRILAFALLNYAKNIDDL-TSLSAFMDQIVV 112 Query 90 K---LRVDPVNFKLLSHCLLVTLAAHLPAEF-TPA 120 K L HCLL T+ LP++ TPA Sbjct 113 KHVGLQIKAEHYPIVGHCLLSTMQELLPSDVATPA 147

23 NCBI BLAST - Web Resources NCBI BLAST Webpage: BLAST Handbook: BLAST FAQs: PAGE_TYPE=BlastDocs&DOC_TYPE=FAQ Comprehensive list of BLAST related references: