Class 4 fast sequence alignment
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Class 4: Fast Sequence Alignment. Alignment in Real Life. One of the major uses of alignments is to find sequences in a “database” Such collections contain massive number of sequences (order of 10 6 )

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Class 4: Fast Sequence Alignment

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Class 4 fast sequence alignment

Class 4: Fast Sequence Alignment

.


Alignment in real life

Alignment in Real Life

  • One of the major uses of alignments is to find sequences in a “database”

  • Such collections contain massive number of sequences (order of 106)

  • Finding homologies in these databases with the standard dynamic programming can take too long

  • Example:

    • query protein : 232 AAs

    • NR protein DB: 2.7 million sequences; 748 million AAs

    • m*n = ~ 1.7 *1011cells !


Heuristic search

Heuristic Search

  • Instead, most searches rely on heuristic procedures

  • These are not guaranteed to find the best match

  • Sometimes, they will completely miss a high-scoring match

  • We now describe the main ideas used by some of these procedures

    • Actual implementations often contain additional tricks and hacks


Basic intuition

Basic Intuition

  • The main resource consuming factor in the standard DP is decision of where the gaps are. If there were no gaps, life was easy!

  • Almost all heuristic search procedures are based on the observation that real-life well-matching pairs of sequences often do contain long strings with gap-less matches.

  • These heuristics try to find significant local gap-less matches and then extend them.


Banded dp

Banded DP

  • Suppose that we have two strings s[1..n] and t[1..m] such that nm

  • If the optimal global alignment of s and t has few gaps, then path of the alignment will be close to the diagonal

s

t


Banded dp1

Banded DP

  • To find such a path, it suffices to search in a diagonal region of the matrix

  • If the diagonal band has presumed width a, then the dynamic programming step takes O(an)

  • Much faster than O(n2) of standard DP in this case

s

a

t


Banded dp2

Banded DP

Problem (for local alignment):

  • If we know that t[i..j] matches the query s[p..q], then we can use banded DP to evaluate quality of the match

  • However, we do not know i,j,p,q !

  • How do we select which sub-sequences to align using banded DP?


Fasta overview

FASTA Overview

  • Main idea:

    Find (fast!) “good” diagonals and extend them to complete matches

  • Suppose that we have a relatively long gap-less local match (diagonal):

    …AGCGCCATGGATTGAGCGA…

    …TGCGACATTGATCGACCTA…

  • Can we find “clues” that will let us find it quickly?


Signature of a match

s

t

Signature of a Match

Assumption: good matches contain several “patches” of perfect matches

AGCGCCATGGATTGAGCGA

TGCGACATTGATCGACCTA


Fasta

FASTA

  • Given s and t, and a parameter k

  • Find all pairs (i,j) such that s[i..i+k] and t[j..j+k] match perfectly

  • Locate sets of pairs that are on the same diagonal by sorting according to i-j thus…

  • Locating diagonals that contain

    many close pairs.

  • This is faster than O(nm) !

s

i i+k

j

j+k

t


Fasta1

FASTA

  • Extend the “best” diagonal matches to imperfect (yet ungapped) matches, compute alignment scores per diagonal. Pick the best-scoring matches.

  • Try to combine close diagonals to potential gapped matches, picking the best-scoring matches.

  • Finally, run banded DP on the regions containing these matches, resulting in several good candidate alignments.

  • Most applications of FASTA use very small k(2 for proteins, and 4-6 for DNA)


Blast overview

BLAST Overview

  • FASTA drawback is its reliance on perfect matches

  • BLAST (Basic Local Alignment Search Tool)uses similar intuition, but relies on high scoringmatches rather than exact matches

  • Given parameters: length k, and threshold T

  • Two strings s and t of length k are a high scoring pair (HSP) if d(s,t) > T


High scoring pair

High-Scoring Pair

  • Given a query string s, BLAST construct all words w (“neighborhood words”), such that w is an HSP with a k-substring of s.

  • Note: not all k-mers have an HSP in s


Blast phase 1

BLAST: phase 1

  • Phase 1: compile a list of word pairs (k=3)

  • above threshold T

  • Example: for the following query:

    …FSGTWYA… (query word is in green)

  • A list of words (k=3) is:

  • FSG SGT GTW TWY WYA

  • YSG TGT ATW SWY WFA

  • FTG SVT GSW TWF WYS


Class 4 fast sequence alignment

BLAST: phase 1

scores

GTW 6,5,11 22

neighborhoodASW 6,1,11 18

word hitsATW 0,5,1116

> threshold NTW 0,5,1116

GTY 6,5,213

GNW10

neighborhood GAW9

word hits

below threshold

(T=11)


Blast phase 2

BLAST: phase 2

  • Search the database for perfect matches with neighborhoodwords. Those are “hits” for further alignment.

  • We can locate seed words in a large database in a single pass, given the database is properly preprocessed (using hashing techniques).


Extending potential matches

s

t

Extending Potential Matches

  • Once a hit is found, BLAST attempts to find a local alignment that extends it.

  • Seeds on the same diagonal tend to be combined (as in FASTA)


Two hsp diagonal

Two HSP diagonal

  • An improvement: look for 2 HSPs on close diagonals

  • Extend the alignment between them

  • Fewer extensions considered

  • There is a version of BLAST,

    involving gapped

    extensions.

  • Generally faster then FASTA,

    arguably better.

s

t


Blast variants

Blast Variants

  • blastn (nucleotide BLAST)

  • blastp (protein BLAST)

  • tblastn (protein query, translated DB BLAST)

  • blastx (translated query, protein DB BLAST)

  • tblastx (translated query, translated DB BLAST)

  • bl2seq (pairwise alignment)


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