Module: Sequence Alignment Theory and Applica8ons Session: BLAST

Transcription

1 Module: Sequence Alignment Theory and Applica8ons Session: BLAST

2 Learning Objec8ves and Outcomes v Understand the principles of the BLAST algorithm v Understand the different BLAST algorithms, parameters and their applica:ons v Be able to adjust the sensi:vity and specificity of BLAST searches v Understand and evaluate BLAST results

3 Database Similarity Searching v A main applica:on of pairwise alignment is retrieving biological sequences in databases based on similarity v This process involves submission of a query sequence and performing a pairwise comparison of the query sequence with all individual sequences in a database

4 Database Similarity Searching v Database similarity searching is pairwise alignment on a large scale v Is one of the most effec:ve ways to assign puta:ve func:ons to newly determined sequences

5 DNA/Protein Search (1) v DNA is composed of 4 characters: A,G,C,T, at least 25% of the nucleotides of any 2 unrelated aligned sequences, would be identical v Protein sequence is composed of 20 a.a., the sensitivity of the comparison is improved v It is accepted that convergence of proteins is rare, meaning high similarity between 2 proteins always means homology

6 DNA / Protein search (2) v When comparing DNA sequences, we get significantly more random matches than we get with proteins v The DNA databases are much larger, and grow faster than protein databases v Bigger database means more random hits v For DNA we usually use identity matrices, for protein more sensitive matrices like PAM and BLOSUM allow for better search results

7 Database Search Requirements (1) v Requirements for implemen:ng algorithms for sequence database searching v Sensi:vity refers to the ability to find as many correct hits as possible v It is measured by the extent of inclusion of correctly iden:fied sequence members of the same family v These correct hits are considered true posi:ves in the database searching exercise

8 Database Search Requirements (2) v The second criterion is selec%vity, also called Specificity v Refers to the ability to exclude incorrect hits v These incorrect hits are unrelated sequences mistakenly iden:fied in database searching and are considered false posi:ves v The third criterion is speed, which is the :me it takes to get results from database searches v Depending on the size of the database, speed some:mes can be a primary concern

9 Database Search Requirements (3) v Ideally, one wants to have the greatest sensi:vity, selec:vity, and speed in database searches v However, sa:sfying all three requirements is difficult in reality v What generally happens is that an increase in sensitivity is associated with decrease in selectivity

10 Database Search Requirements (5) v A very inclusive search tends to include many false posi:ves v Similarly, an improvement in speed omen comes at the cost of lowered sensi:vity and selec:vity v A compromise between the three criteria omen has to be made

11 Heuris8c Database Searching (1) v Two major heuris:c algorithms for performing database searches - BLAST and FASTA v These methods are not guaranteed to find the op:mal alignment or true homologous, but are :mes faster than dynamic programming v Programs can provide a reasonably good indica:on of sequence similarity by iden:fying similar sequence segments

12 Heuris8c Database Searching (2) v Both BLAST and FASTA use a heuris:c word method for fast pairwise sequence alignment v It works by finding short stretches of iden:cal or nearly iden:cal leuers in two sequences v These short stretches of characters are called words v The basic assump:on is that two related sequences must have at least one word in common

13 v First iden:fying word matches, a longer alignment can be obtained by extending similarity regions from the words

14 BLAST v BLAST Basic Local Alignment Search Tool v Is an approximate algorithm v Finds the highest scoring local op:mal alignment between query sequence and a database of sequences v To discover all of the similar sequences in the database v Create a sta:s:cal interpreta:on to enable the user dis:nguish a par:cular DNA or protein

15 BLAST (2) v The sequence you give to blast is the query sequence v Sequences similar to the query that blast returns are the hits or matches v The database you search is the target database

16 v One innova:on introduced in BLAST is the idea of neighborhood words v Instead of requiring words to match exactly, a word hit is achieved if the word taken from the subject sequence has a score of at least T when a comparison is made using a subs:tu:on matrix to the word from the query v This strategy allows the word size (W) to be kept high (for speed) without sacrificing sensi:vity

17 v The first step is to create a list of words from the query sequence v Each word is typically three residues for protein sequences and eleven residues for DNA sequences

18 1. Query sequence: AFRICANISED 2. Scan every 3 residues to be used in searching BLAST word database 3. Finds matches in the database Query CAN CAN CAN CAN CAN Database CTN CAN CWN CAY CCSA 4. Calculate sums of match scores based on BLOSUM62 matrix Query CAN CAN CAN CAN CAN Database CTN CAN CWT CAY CSA Sum of score Sequence Alignment Theory and Applica8ons, Pandam

19 5. Find the database sequence corresponding to the best word match and extend alignment in both direc:ons Query A F R I C A N I S E D Database A G R I C A N I T E D 6. Determine high scored segment above threshold Query A F R I C A N I S E D Database A G R I C A N I T E D High Scoring segment pair, total score 42

20 v The extension con:nues un:l the score of the alignment drops below a threshold due to mismatches Proteins = 22 DNA = 20

21 Types of BLAST v There are several variants of BLAST, each dis:nguished by the type of sequence (DNA or protein) of the query and database sequences v BLASTP program compares a protein query to a protein database v BLASTX compares a DNA query sequence to the protein database, which is useful for analyzing new sequence data and ESTs v TBLASTN for a protein query against a nucleo:de database v TBLASTX takes DNA query and database sequences, translates them both, and compares them as protein sequences

22 Sequence Databases Used With BLAST v Nr - database provides comprehensive collec:ons of both amino acid and nucleo:de sequence data, with redundancy reduced by merging sequences that are completely iden:cal v Swissprot The SWISS- PROT database v 16S ribosomal RNA sequences v Transcriptome shortgun assembly v Human RefSeqGene sequences

23 Database Searching Ar8facts v A query sequence that contains repe::ve elements is likely to produce many false and confounding database matches v Likely ar:facts would be finding hits to many proteins that seem to have no func:onal rela:onship to one another or hits to genomic sequences from many different chromosomes v Both the query and the database are contaminated with foreign sequences from the same source, for instance, cloning vectors

24 Database Searching Ar8facts (2) v It is always good prac:ce to cri:cally evaluate database search results and be suspicious of ar:facts when the data don t make sense v A more proac:ve approach involves masking problema:c sequences in the query before doing the search v Both proteins and nucleic acids contain regions of biased composi:on, which can lead to confusing database search results

25 Low- Complexity Regions (LCRs) v In both protein and DNA sequences, there may be regions that contain highly repe::ve residues v Es:mates indicate that LCRs account for about 15% of the total protein sequences in public databases v These elements in query sequences can cause spurious database matches and lead to ar:ficially high alignment scores with unrelated sequences

26 LCRs (1) v Alignment of LCR- containing sequences is problema:c because they do not fit the model of residue- by- residue sequence conserva:on v Methods for assessing the sta:s:cal significance of alignments are based on certain no:ons of randomness which LCRs do not obey

27 LCRs (2) v The evolu:onary, func:onal, and structural proper:es of LCRs are not well understood v LCRs arise by such mechanisms as v polymerase slippage v biased nucleo:de subs:tu:on v unequal crossing- over v In proteins, LCRs are likely to exist structurally as nonglobular regions v Regions that have been defined physico- chemically as nonglobular

28 Masking of LRCs (1) v To avoid the problem of high similarity scores owing to matching of LCRs that obscure the real similari:es v It is important to filter out the problema:c regions in both the query and database sequences to improve the signal- to- noise ra:o v = Masking v Two types, hard masking and som masking

29 Masking of LRCs (2) v Hard masking involves replacing LCR sequences with an ambiguity character such as N for v Nucleo:de residues or X for amino acid residues v The drawback is that matching scores with true homologs may be lowered because of shortened alignments v So4 masking involves conver:ng the problema:c sequences to lower case leuers, which are ignored in construc:ng the word dic:onary v But are used in word extension and op:miza:on of alignments

30 BLAST Programs

31 BLAST Suite

32 BLAST Search Op8misa8ons

33 BLAST Search Op8misa8ons

34 BLAST OUTPUT v The graphical output includes coloured horizontal bars and a diagonal in a two- dimensional diagram showing the overall extent of matching between the two sequences v The colour coding of the horizontal bars corresponds to the ranking of similari:es of the sequence hits v Red = most related; Green and Blue = moderately related and Black = unrelated

35 Graphical Report

36 BLAST Output v The list of hits v Database accession codes, name, description, general information about the hit v Score in bits, the alignment score expressed in units of information v Usually 30 bits are required for significance

37 v Expectation value E(), how many hits we expect to find by chance with this score, when comparing this query to the database v It is important to keep in mind that the E() value does not represent a measure of similarity between the two sequences.

38 Hits

39 Pairwise Alignment

40 Lineage Report

41 Taxonomic Report

42 Mul8- Alignment

43 Graphical Report

44 Conserved Domains Database

45

46

47 Smart BLAST

48 Sta8s8cal Significance v The significance scores help to dis:nguish evolu:onarily related sequences from unrelated ones v The larger the database, the more unrelated sequence alignments there are v E- value (E) indicates the probability that the resul:ng alignments from a database search are caused by random chance

49 E- Value E = m x n x P v Where ² m = the total number of residues in a database, ² n = the number of residues in the query sequence, and ² p = the probability that an HSP alignment is a result of random chance

50 Example 1 v Find an E- value for aligning a query sequence of 100 residues to a database containing a total of residues results in a P- value for the ungapped HSP region in one of the database matches of 10 20

51 Answer for Example 1 E = m x n x P v E = 100 x x = 10-3 v E = 1e- 3 in BLAST output v The E- value provides informa:on about the likelihood that a given sequence match is purely by chance

52 Significance of E- value (1) v The lower the E- value, the less likely the database match is a result of random chance v E < 1e 50 (or 1 x ), there should be an extremely high confidence that the database match is a result of homologous rela:onships v E is between 0.01 and 1e 50, the match can be considered a result of homology v E between 0.01 and 10, the match is considered not significant, but may hint at a tenta:ve remote homology rela:onship

53 Significance of E- value (2) v E > 10, the sequences under considera:on are either unrelated or related by extremely distant rela:onships that fall below the limit of detec:on with the current method

54 Think! EEEEEEE!!!! v E = m x n x p v E m, i.e. E- value is propor:onally affected by the database size v The problem is that as the database grows, the E- value for a given sequence match also increases

55 Weakness of E- value v The genuine evolu:onary rela:onship between the two sequences remains constant v The decrease in credibility of the sequence match as the database grows v Means that one may lose previously detected homologous as the database enlarges v Thus, an alterna:ve to E- value calcula:ons is needed

56 Bit Score v Is another prominent sta:s:cal indicator used in addi:on to the E- value in a BLAST output v The bit score measures sequence similarity independent of query sequence length and database size and is normalized based on the raw pairwise alignment score v S = (λ x S lnk)/ ln2 v Where λ is the Gumble distribu:on constant S is the raw alignment score, and K is a constant associated with the scoring matrix used

57 Bit Score v Clearly, the bit score (S ) is linearly related to the raw alignment score (S) v Thus, the higher the bit score, the more highly significant the match is v The bit score provides a constant sta:s:cal indicator for searching different databases of different sizes or for searching the same database at different :mes as the database enlarges

58 Posi8on- Specific Scoring Matrices (PSSM) v In a standard subs:tu:on matrix, such as BLOSUM62 the subs:tu:on of one amino acid with another is associated with a single score v An obvious simplifica:on given that the same amino acid may have different conserva:on pauerns in one context than another in accordance with differing roles in biological func:on

59 PSSM v Database searches can be tailored to find specific proteins families or domains through the use of subs:tu:on scores that reflect the subs:tu:on frequencies of each individual amino acid posi:on in a domain v In its simplest form, a PSSM consists of a set of 20 subs:tu:on scores at each posi:on along the mo:f one for each of the amino acids v It is also possible to assign scores to inser:ons and dele:ons in a posi:on- specific manner

60 PSSM v A commonly used somware package, HMMER (Eddy et al., 1995), contains a set of related programs for construc:ng and using PSSMs v Given a mul:ple alignment of several related proteins (e.g., one made using CLUSTAL W), the hmmbuild program may be used to calculate the posi:on- specific scores and save it to a file (HMM file format).

61 HMM v Using the hmmsearch program, the HMM file may be used as a query against a sequence database v hmmpfam is used to compare a single query sequence against a database of PSSMs (HMMs) v The power of PSSMs in database searches can be further enhanced by itera:ve approaches in which the highest scoring matches in one search are incorporated into a PSSM used in successive searches

62 Posi:on- Specific Iterated BLAST (PSI- BLAST) v Provides an automated facility for construc:ng refining, and searching PSSMs within the context of a single program v Star:ng with a query sequence provided by the user, the process begins with a standard BLASTP search of a sequence database v Highly significant alignments found in this search are then used to construct a PSSM on- the- fly

63 PSI- BLAST v Comparisons of the PSSM against the sequence database are performed using a varia:on of the word- based BLAST algorithm used for standard sequence comparisons v The process con:nues un:l no new matches are found or a specified limit on number of itera:ons is reached

64 GAP- BLAST v Use two hit approach v Word can be followed by a second word within a certain gap threshold v Matches are extended in both direc:on using a matrix in both direc:ons un:l the scores drops v Weakness performs dynamic programming at the end v The best alignment may be lie outside of the range it has defined

65 Other Specialised BLASTs

66 CONCLUSIONS v Sequence alignment and database searching are performed tens of thousands of :mes per day by scien:sts around the world and represent cri:cal techniques that all molecular biologists should be familiar with v Described some of the fundamental concepts involved basic understanding of how the programs work so that parameters can be intelligently selected

67 CONCLUSIONS v Aware of poten:al ar:facts and know how to avoid them v Important to apply the same powers of observa:on and cri:cal evalua:on that are used with any experimental method