Sequence Alignment Ktuple methods Statistics of alignments Phylogenetics

Sequence Alignment K-tuple methods Statistics of alignments Phylogenetics

Database searches n What is the problem? ¡ ¡ Large number of sequences to search your query sequence against. Various indexing schemes and heuristics are used, one of which is BLAST. n heuristic is a technique to solve a problem that ignores whether the solution can be proven to be correct, but usually produces a good solution, are intended to gain computational performance or conceptual simplicity potentially at the cost of accuracy or precision. http: //en. wikipedia. org/wiki/Heuristics#Computer_science

Concepts of Sequence Similarity Searching n The premise: ¡ The sequence itself is not informative; it must be analyzed by comparative methods against existing databases to develop hypothesis concerning relatives and function.

Important Terms for Sequence Similarity Searching with very different meanings n Similarity ¡ n Identity ¡ n The extent to which nucleotide or protein sequences are related. In BLAST similarity refers to a positive matrix score. The extent to which two (nucleotide or amino acid) sequences are invariant. Homology ¡ Similarity attributed to descent from a common ancestor.

Sequence Similarity Searching: The Approach n n Sequence similarity searching involves the use of a set of algorithms (such as the BLAST programs) to compare a query sequence to all the sequences in a specified database. Comparisons are made in a pairwise fashion. Each comparison is given a score reflecting the degree of similarity between the query and the sequence being compared.

Blast QUERY sequence(s) BLAST results BLAST program BLAST database

Topics: n n There are different blast programs Understanding the BLAST algorithm ¡ ¡ n BLAST program Word size HSPs (High Scoring Pairs) Understanding BLAST statistics ¡ ¡ The alignment score (S) Scoring Matrices Dealing with gaps in an alignment The expectation value (E)

The BLAST algorithm n The BLAST programs (Basic Local Alignment Search Tools) are a set of sequence comparison algorithms introduced in 1990 for optimal local alignments to a query. ¡ ¡ Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) “Basic local alignment search tool. ” J. Mol. Biol. 215: 403 -410. Altschul SF, Madden TL, Schaeffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. ” NAR 25: 3389 -3402.

http: //www. ncbi. nlm. nih. gov/BLAST blastn blastx tblastn blastp

Other BLAST programs n BLAST 2 Sequences (bl 2 seq) ¡ ¡ Aligns two sequences of your choice Gives dot-plot like output

More BLAST programs n BLAST against genomes ¡ ¡ ¡ n Many available BLAST parameters pre-optimized Handy for mapping query to genome Search for short exact matches ¡ ¡ BLAST parameters pre-optimized Great for checking probes and primers

How Does BLAST Work? n n The BLAST programs improved the overall speed of searches while retaining good sensitivity (important as databases continue to grow) by breaking the query and database sequences into fragments ("words"), and initially seeking matches between fragments. Word hits are then extended in either direction in an attempt to generate an alignment with a score exceeding the threshold of “T".

Picture used with permission from Chapter 11 of “Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins”

Each BLAST “hit” generates an alignment that can contain one or more high scoring pairs (HSPs)

Each BLAST “hit” generates an alignment that can contain one or more high scoring pairs (HSPs)

Where does the score (S) come from? n n n The quality of each pair-wise alignment is represented as a score 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.

What’s a scoring matrix? n Substitution matrices are used for amino acid alignments. These are matrices in which each possible residue substitution is given a score reflecting the probability that it is related to the corresponding residue in the query.

PAM vs. BLOSUM scoring matrices n BLOSUM 62 is the default matrix in BLAST 2. 0. Though it is tailored for comparisons of moderately distant proteins, it performs well in detecting closer relationships. A search for distant relatives may be more sensitive with a different matrix.

PAM vs BLOSUM scoring matrices The PAM Family The BLOSUM family n PAM matrices are n BLOSUM matrices are based on global based on local alignments of closely n BLOSUM 62 is a matrix related proteins. calculated from n The PAM 1 is the matrix comparisons of sequences calculated from with no less than 62% comparisons of divergence. sequences with no n All BLOSUM matrices are more than 1% based on observed divergence. alignments; they are not n Other PAM matrices extrapolated from are extrapolated from comparisons of closely PAM 1. related proteins.

What happens if you have a gap in the alignment? n n A gap is a position in the alignment at which a letter is paired with a null Gap scores are negative. Since a single mutational event may cause the insertion or deletion of more than one residue, the presence of a gap is frequently ascribed more significance than the length of the gap. ¡ Hence the gap is penalized heavily, whereas a lesser penalty is assigned to each subsequent residue in the gap.

Percent Sequence Identity n The extent to which two nucleotide or amino acid sequences are invariant AC C TG A G – AG AC G TG – G C AG mismatch 70% identical indel

BLAST algorithm n n Keyword search of all words of length w in the query of default length n in database of length m with score above threshold ¡ w = 11 for nucleotide queries, 3 for proteins Do local alignment extension for each hit of keyword search ¡ Extend result until longest match above threshold is achieved and output

BLAST algorithm (cont’d) keyword Query: KRHRKVLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVLKIFLENVIRD GVK 18 GAK 16 Neighborhood GIK 16 words GGK 14 neighborhood GLK 13 score threshold GNK 12 (T = 13) GRK 11 GEK 11 GDK 11 extension Query: 22 VLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVLK 60 +++DN +G + IR L G+K I+ L+ E+ RG++K Sbjct: 226 IIKDNGRGFSGKQIRNLNYGIGLKVIADLV-EKHRGIIK 263 High-scoring Pair (HSP)

Original BLAST n n n Dictionary ¡ All words of length w Alignment ¡ Ungapped extensions until score falls below statistical threshold T Output ¡ All local alignments with score > statistical threshold

Original BLAST: Example • • • w = 4, T = 4 Exact keyword match of GGTC Extend diagonals with mismatches until score falls below a threshold Output result GTAAGGTCC GTTAGGTCC From lectures by Serafim Batzoglou (Stanford) C T G A T C C T G G A T T G C G A • A C G A A G T A A G G T C C A G T

Gapped BLAST: Example n n n Original BLAST exact keyword search, THEN: Extend with gaps in a zone around ends of exact match Output result GTAAGGTCCAGT GTTAGGTC-AGT From lectures by Serafim Batzoglou (Stanford) C T G A T C C T G G A T T G C G A A G T A A G G T C C A G T

Gapped BLAST : Example (cont’d) n n Original BLAST exact keyword search, THEN: Extend with gaps around ends of exact match until score <T, then merge nearby alignments Output result GTAAGGTCCAGT GTTAGGTC-AGT From lectures by Serafim Batzoglou (Stanford) C T G A T C C T G G A T T G C G A n A C G A A G T A A G G T C C A G T

Topics: n The different blast databases provided by the NCBI ¡ ¡ ¡ n n BLAST databases Protein databases Nucleotide databases Genomic databases Considerations for choosing a BLAST database Custom databases for BLAST

BLAST protein databases available at through blastp web interface @ NCBI blastp db

Considerations for choosing a BLAST database n First consider your research question: ¡ Are you looking for an ortholog in a particular species? n ¡ Are you looking for additional members of a protein family across all species? n ¡ BLAST against the genome of that species. BLAST against nr, if you can’t find hits check wgs, htgs, and the trace archives. Are you looking to annotate genes in your species of interest? n BLAST against known genes (Ref. Seq) and/or ESTs from a closely related species.

When choosing a database for BLAST… n It is important to know your reagents. ¡ ¡ Changing your choice of database is changing your search space completely Database size affects the BLAST statistics n ¡ record BLAST parameters, database choice, database size in your bioinformatics lab book, just as you would for your wet-bench experiments. Databases change rapidly and are updated frequently n It may be necessary to repeat your analyses

Topics: BLAST results n n Choosing the right BLAST program Running a blastp search ¡ n BLAST parameters and options to consider Viewing BLAST results ¡ ¡ Look at your alignments Using the BLAST taxonomy report

BLAST parameters and options to consider: conserved domains Entrez query E-value cutoff Word size

More BLAST parameters and options to consider: filtering matrix gap penalities

Run your BLAST search: BLAST

The BLAST Queue: click for more info Note your RID

Formatting and Retrieving your BLAST results: Results options

A graphical view of your BLAST results:

The BLAST “hit” list: Score E-Value Gen. Bank alignment Entrez. Gene

The BLAST pairwise alignments Identity Similarity

Sample BLAST output • Blast of human beta globin protein against zebra fish Sequences producing significant alignments: Score E (bits) Value gi|18858329|ref|NP_571095. 1| ba 1 globin [Danio rerio] >gi|147757. . . gi|18858331|ref|NP_571096. 1| ba 2 globin; SI: d. Z 118 J 2. 3 [Danio rer. . . gi|37606100|emb|CAE 48992. 1| SI: b. Y 187 G 17. 6 (novel beta globin) [D. . . gi|31419195|gb|AAH 53176. 1| Ba 1 protein [Danio rerio] 171 170 168 ALIGNMENTS >gi|18858329|ref|NP_571095. 1| ba 1 globin [Danio rerio] Length = 148 Score = 171 bits (434), Expect = 3 e-44 Identities = 76/148 (51%), Positives = 106/148 (71%), Gaps = 1/148 (0%) Query: 1 Sbjct: 1 Query: 61 Sbjct: 61 MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK 60 MV T E++A+ LWGK+N+DE+G +AL R L+VYPWTQR+F +FG+LS+P A+MGNPK MVEWTDAERTAILGLWGKLNIDEIGPQALSRCLIVYPWTQRYFATFGNLSSPAAIMGNPK 60 VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG 120 V AHG+ V+G + ++DN+K T+A LS +H +KLHVDP+NFRLL + + A FG VAAHGRTVMGGLERAIKNMDNVKNTYAALSVMHSEKLHVDPDNFRLLADCITVCAAMKFG 120 Query: 121 KE-FTPPVQAAYQKVVAGVANALAHKYH 147 + F VQ A+QK +A V +AL +YH Sbjct: 121 QAGFNADVQEAWQKFLAVVVSALCRQYH 148 3 e-44 7 e-44 3 e-43

Sample BLAST output (cont’d) • Blast of human beta globin DNA against human DNA Sequences producing significant alignments: Score E (bits) Value gi|19849266|gb|AF 487523. 1| Homo sapiens gamma A hemoglobin (HBG 1. . . gi|183868|gb|M 11427. 1|HUMHBG 3 E Human gamma-globin m. RNA, 3' end gi|44887617|gb|AY 534688. 1| Homo sapiens A-gamma globin (HBG 1) ge. . . gi|31726|emb|V 00512. 1|HSGGL 1 Human messenger RNA for gamma-globin gi|38683401|ref|NR_001589. 1| Homo sapiens hemoglobin, beta pseud. . . gi|18462073|gb|AF 339400. 1| Homo sapiens haplotype PB 26 beta-glob. . . 289 280 260 151 149 1 e-75 1 e-72 1 e-66 7 e-34 3 e-33 ALIGNMENTS >gi|28380636|ref|NG_000007. 3| Homo sapiens beta globin region (HBB@) on chromosome 11 Length = 81706 Score = 149 bits (75), Expect = 3 e-33 Identities = 183/219 (83%) Strand = Plus / Plus Query: 267 ttgggagatgccacaaagcacctggatgatctcaagggcacctttgcccagctgagtgaa 326 || | |||||| |||| Sbjct: 54409 ttcggaaaagctgttatgctcacggatgacctcaaaggcacctttgctacactgagtgac 54468 Query: 327 ctgcactgtgacaagctgcatgtggatcctgagaacttc 365 |||||||||| Sbjct: 54469 ctgcactgtaacaagctgcacgtggaccctgagaacttc 54507

What do the Score and the evalue really mean? n The quality of the alignment is represented by the Score. ¡ Score (S) n n The score of an alignment is calculated as the sum of substitution and gap scores. Substitution scores are given by a look-up table (PAM, BLOSUM) whereas gap scores are assigned empirically. The significance of each alignment is computed as an E value. ¡ E value (E) n 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.

E value n E value (E) ¡ Expectation value. The number of different alignments with scores equivalent to or better than S expected to occur in a database search by chance. The lower the E value, the more significant the score.

Assessing sequence homology n n Need to know how strong an alignment can be expected from chance alone “Chance” is the comparison of ¡ Real but non-homologous sequences ¡ Real sequences that are shuffled to preserve compositional properties ¡ Sequences that are generated randomly based upon a DNA or protein sequence model (favored)

High Scoring Pairs (HSPs) n All segment pairs whose scores can not be improved by extension or trimming n Need to model a random sequence to analyze how high the score is in relation to chance

Expected number of HSPs n n Expected number of HSPs with score > S E-value E for the score S: ¡ n E = Kmne-l. S Given: ¡ Two sequences, length n and m ¡ The statistics of HSP scores are characterized by two parameters K and λ n K: scale for the search space size n λ: scale for the scoring system

BLAST statistics to record in your bioinformatics labbook Record the statistics that are found at bottom of your BLAST results page

Scoring matrices n Amino acid substitution matrices ¡ PAM ¡ BLOSUM

Bit Scores n n Normalized score to be able to compare sequences Bit score ¡ S’ = l. S – ln(K) ln(2) n E-value of bit score ¡ E = mn 2 -S’

Assessing the significance of an alignment n How to assess the significance of an alignment between the comparison of a protein of length m to a database containing many different proteins, of varying lengths? n Calculate a "database search" E-value. Multiply the pairwise-comparison E-value by the number of sequences in the database N divided by the length of the sequence in the database n

Homology: Some Guidelines n n Similarity can be indicative of homology Generally, if two sequences are significantly similar over entire length they are likely homologous Low complexity regions can be highly similar without being homologous Homologous sequences not always highly similar

Homology: Some Guidelines n Suggested BLAST Cutoffs ¡ ¡ ¡ (source: Chapter 11 – Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins) For nucleotide based searches, one should look for hits with E-values of 10 -6 or less and sequence identity of 70% or more For protein based searches, one should look for hits with E-values of 10 -3 or less and sequence identity of 25% or more

Contributors http: //creativecommons. org/licenses/by-sa/2. 0/

Odds score in sequence alignment n The chance of an aligned amino acid pair being found in alignments of related sequences compared to the chance of that pair being found in random alignments of unrelated sequences.

Statistical significance of an alignment n The probability that random or unrelated sequences could be aligned to produce the same score. ¡ Smaller the probability is the better.

Alignment Statistics: n For two sequences of length n and m, n times m comparisons are being made; thus the longest length of the predicted match would be log 1/p(mn).

Alignment Statistics: n Expectation value or the mean longest match would be ¡ E(M) = log 1/p(Kmn), where K is a constant that depends on amino acid or base composition and p is the probability of a match. n This is only true for ungapped local alignments.

Distribution of alignment scores n resembles Gumbel extreme value distribution.

Extreme Value Distribution

Alignment Statistics n n n E(M)=log 1/p(Kmn) means that match length gets bigger as the log of the product of sequence lengths. Amino acid substitution matrices will turn match lengths into alignment scores (S). More commonly = ln(1/p) is used. Number of longest run HSP will be estimated E = Kmne- S How good a sequence score is evaluated based on how many HSPs (i. e. E value) one would expect for that score.

Alignment Statistics n Two ways to get K and : ¡ ¡ For 10000 random amino acid sequences with various gap penalties, K and lambda parameters have been tabulated. Calculation of the distribution for two sequences being aligned by keeping one of them fixed and scrambling the other, thus preserving both the sequence length and amino acid composition.

Alignment Statistics

Alignment Statistics

Alignment Statistics

Alignment Statistics

Gene Structure

Mutation Rates

Functional Constraint

Synonymous vs nonsynonymous substitutions

Synonymous vs nonsynonymous substitutions

Synonymous vs nonsynonymous substitutions

Mutation vs substitution

Estimating substitutions

Jukes-Cantor model

Transitions vs transversions

Kimura’s 2 -parameter model

Kimura’s 2 -parameter model

Kimura’s 2 -parameter model

Functional Constraints

Molecular Clocks

Relative Rate

Distance based phylogenetics

Distance based phylogenetics

Distance based phylogenetics

Distance based phylogenetics

Distance based phylogenetics

Distance based phylogenetics

Phylogenetics Programs