CAP5510

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CAP5510 BioinformaticsDatabase Searches for
Biological Sequences or Imperfect Alignments

• Tamer Kahveci
• CISE Department
• University of Florida

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Goals

• Understand how major heuristic methods for
  sequence comparison work
• FASTA
• BLAST
• Understand how search results are evaluated

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What is Database Search ?

• Find a particular (usually) short sequence in a
  database of sequences (or one huge sequence).
• Problem is identical to local sequence alignment,
  but on a much larger scale.
• We must also have some idea of the significance
  of a database hit.
• Databases always return some kind of hit, how
  much attention should be paid to the result?
• A similar problem is the global alignment of two
  large sequences
• General idea good alignments contain high
  scoring regions.

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Imperfect Alignment

• What is an imperfect alignment?
• Why imperfect alignment?
• The result may not be optimal.
• Finding optimal alignment is usually to costly in
  terms of time and memory.

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Database Search Methods

• Hash table based methods
• FASTA family
• FASTP, FASTA, TFASTA, FASTAX, FASTAY
• BLAST family
• BLASTP, BLASTN, TBLAST, BLASTX, BLAT, BLASTZ,
  MegaBLAST, PsiBLAST, PhiBLAST
• Others
• FLASH, PatternHunter, SSAHA, SENSEI, WABA, GLASS
• Suffix tree based methods
• Mummer, AVID, Reputer, MGA, QUASAR

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History of sequence searching

• 1970 NW
• 1980 SW
• 1985 FASTA
• 1990 BLAST

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Hash Table
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Hash Table

• K-gram subsequence of length K
• Ak entries
• A is alphabet size
• Linear time construction
• Constant lookup time

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FASTP

• Lipman Pearson, 1985

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FASTP

• Three phase algorithm
• Find short good matches using k-grams
• K 1 or 2
• Find start and end positions for good matches
• Use DP to align good matches

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FASTP Phase 1 (1)
position 1 2 3 4 5 6 7 8 9 10 11 protein 1 n c s
p t a . . . . . protein 2 . . . . . a c s p r k
position in
offset amino acid protein A protein B pos
A - posB -----------------------------------------
------------ a 6 6
0 c 2 7
-5 k - 11 n
1 - p 4
9 -5 r -
10 s 3 8
-5 t 5
- ------------------------------------------------
----- Note the common offset for the 3 amino
acids c,s and p A possible alignment can be
quickly found protein 1 n c s p t a
protein 2 a c s p r k
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FASTP Phase 1 (2)

• Similar to dot plot
• Offsets range from 1-m to n-1
• Each offset is scored as
• matches - mismatches
• Diagonals (offsets) with large score show local
  similarities
• How does it depend on k?

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FASTP Phase 2

• 5 best diagonal runs are found
• Rescore these 5 regions using PAM250.
• Initial score
• Indels are not considered yet

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FASTP Phase 3

• Sort the aligned regions in descending score
• Optimize these alignments using Needleman-Wunsch
• Report the results

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FASTP - Discussion

• Results are not optimal. Why ?
• How does performance compare to Smith-Waterman?
• What is the impact of k?
• How does this idea work for DNAs ?
• K 4 or 6 for DNA

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FASTA Improvement Over FASTP

• Pearson 1995

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FASTA (1)

• Phase 2 Choose 10 best diagonal runs instead of 5

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FASTA (2)

• Phase 2.5
• Eliminate diagonals that score less than some
  given threshold.
• Combine matches to find longer matches. It incurs
  join penalty similar to gap penalty

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FASTA Variations

• TFASTAX and TFASTAY query protein against a DNA
  library in all reading frames
• FASTAX, FASTAY DNA query in all reading frames
  against protein database

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BLAST

• Altschul, Gish, Miller, Myers, Lipman, 1990

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BLAST (or BLASTP)

• BLAST Basic Local Alignment Search Tool
• An approximation of Smith-Waterman
• Designed for database searches
• Short query sequence against long database
  sequence or a database of many sequences
• Sacrifices search sensitivity for speed

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BLAST Algorithm (1)

• Eliminate low complexity regions from the query
  sequence.
• Replace them with X (protein) or N (DNA)
• Hash table on query sequence.
• K 3 for proteins

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BLAST Algorithm (2)

• For each k-gram find all k-grams that align with
  score at least cutoff T using BLOSUM62
• 20k candidates
• 50 on the average per k-gram
• 50n for the entire query
• Build hash table

PQGMCGPFILGTYC
QGM
PQG
PQG PQG 18 PEG 15 PRG 14 PSG 13 PQA 12
T 13
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BLAST Algorithm (3)

• Sequentially scan the database and locate each
  k-gram in the hash table
• Each match is a seed for an ungapped alignment.

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BLAST Algorithm (4)

• HSP (High Scoring Pair) A match between a query
  word and the database
• Find a hit Two non-overlapping HSPs on a
  diagonal within distance A
• Extend the hit until the score falls below a
  threshold value, X

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BLAST Algorithm (5)

• Keep only the extended matches that have a score
  at least S.
• Determine the statistical significance of the
  result

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What is Statistical Significance?

• Two one-on-one games, two scores.
• Which result is more significant?
• Expected maybe a random result.
• Unexpected significant, may have significant
  meanings.

13 15
13 15
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Statistical Significance

• E-value The expected number of matches with
  score at least S
• E Kmne-lambda.S
• m, n sequence lengths
• S alignment score
• K, lambda normalization parameters
• P-value The probability of having at least one
  match with score at least S
• 1 e-E
• The smaller these values are, the more
  significant the result
• http//www.ncbi.nlm.nih.gov/Education/BLASTinfo/gl
  ossary2.html

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BLAST - Analysis

• K (k-gram)
• Lower more sensitive. Slower.
• T (neighbor cutoff)
• Lower Find distant neighbors. Introduces noise
• X (extension cutoff)
• Higher lower chances of getting into a local
  minima. Slower.

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Sample Query

• http//www.ncbi.nlm.nih.gov/BLAST/

Dhal_ecoli
I D R A M S A A R G V F E R G D W S L S S P A K
R K A V L N K L A D L M E A H A E E L A L L E T L
D T G K P I R H S L R D D I P G A A R A I R W Y A
E A I D K V Y G E V A T T S S H E L A M I V R E P
V G V I A A I V P W N F P L L L T C W K L G P A L
A A G N S V I L K P S E K S P L S A I R L A G L A
K E A G L P D G V L N V V T G F G H E A G Q A L S
R H N D I D A I A F T G S T R T G K Q L L K D A G
D S N M K R V W L E A G G K S A N I V F A D C P D
L Q Q A A S A T A A G I F Y N Q G Q V C I A G T R
L L L E E S I A D E F L A L L K Q Q A Q N W Q P G
H P L D P A T T M G T L I D C A H A D S V H S F I
R E G E S K G Q L L L D G R N A G L A A A I G P T
I F V D V D P N A S L S R E E I F G P V L V V T R
F T S E E Q A L Q L A N D S Q Y G L G A A V W T R
D L S R A H R M S R R L K A G S V F V N N Y N D G
D M T V P F G G Y K Q S G N G R D K S L H A L E K
F T E L K T I W I
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BLASTN

• BLAST for nucleic acids
• K 11
• Exact match instead of neighborhood search.

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BLAST Variations
Program Query Target Type
BLASTP Protein Protein Gapped
BLASTN Nucleic acid Nucleic acid Gapped
BLASTX Nucleic acid Protein Gapped
TBLASTN Protein Nucleic acid Gapped
TBLASTX Protein Nucleic acid Gapped
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Even More Variations

• PsiBLAST (iterative)
• BLAT, BLASTZ, MegaBLAST
• FLASH, PatternHunter, SSAHA, SENSEI, WABA, GLASS
• Main differences are
• Seed choice (k, gapped seeds)
• Additional data structures

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Suffix Trees
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Suffix Tree

• Tree structure that contains all suffixes of the
  input sequence
• TGAGTGCGA
• GAGTGCGA
• AGTGCGA
• GTGCGA
• TGCGA
• GCGA
• CGA
• GA
• A

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Suffix Tree Example
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Suffix Tree Analysis

• O(n) space and construction time
• 10n to 70n space usage reported
• O(m) search time for m-letter sequence
• Good for
• Small data
• Exact matches

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Suffix Array

• 5 bytes per letter
• O(m log n) search time
• Better space usage
• Slower search

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Mummer
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Other Sequence Comparison Tools

• Reputer, MGA, AVID
• QUASAR (suffix array)