1. Sequence alignment, Part 2
BI420 – Introduction to Bioinformatics
Sequence alignment, Part 2
BI420 Spring 2012
Department of Biology, Boston College

2. Similar algorithms can be used for multiple alignment
The multiple alignment of 24 hexokinase protein sequences from various species.
However, real multiple alignment programs (e.g. clustalw) are usually heuristic, rather than exact

3. Applications of Alignment

4. Alignment is used for mapping sequence reads to the genome

5. Alignment is used in similarity search
Alignment: determining how sequences have descended from a common ancestor
Similarity search: determining which sequences are related to one another. Requires scoring of each alignment.
query
database

6. Alignment Exercises

7. Visualizing pair-wise alignments
Visit a web server running a dot-plotter:
Upload hba_human and hbb_human, and create dot-plot:

8. MATLAB example MATLAB bioinformatics toolbox sequence analysis demo:
Aligning pairs of sequences

9. BLAST Basic Local Alignment Search Tool

10. Purpose of BLAST
Exact alignment algorithms, such as Needleman-Wunsch for global and Smith-Waterman for local, are slow: O(length1 * length2) .
Alignment speed can be increased by using statistical properties of sequences to estimate alignment quality. This requires pre-processing of the sequence database.

11. The BLAST algorithms Program Database Query Typical Uses BLASTN
Nucleotide
Mapping oligonucleotides, amplimers, ESTs, and repeats to a genome. Identifying related transcripts.
BLASTP
Protein
Identifying common regions between proteins. Collecting related proteins for phylogenetic analysis.
BLASTX
Finding protein-coding genes in genomic DNA.
TBLASTN
Identifying transcripts similar to a known protein (finding proteins not yet in GenBank). Mapping a protein to genomic DNA.
TBLASTX
Cross-species gene prediction. Searching for genes missed by traditional methods.

12. BLAST report BLAST example with hba_Human
BLAST example with hba_Human

13. BLAST report

14. The BLAST algorithm
Sequence alignment takes place in a 2d space where diagonal lines represent regions of similarity. Gaps break up the diagonals.
The search space can be considered as seq1 vs seq2, or as seq1 vs a database of a sequences.
Global alignment vs. local alignment
BLAST is local
Maximum scoring pair (MSP) vs. High-scoring pair (HSP)
BLAST finds HSPs (usually the MSP too)
Gapped vs. ungapped
BLAST can do both

15. The BLAST algorithm Alignments require word (segment pair) hits
Database is preprocessed for word content of each sequence. This speeds up later calculations.

16. BLOSUM62 neighborhood of RGD
The BLAST algorithm
BLOSUM62 neighborhood of RGD
RGD 17
KGD 14
QGD 13
RGE 13
EGD 12
HGD 12
NGD 12
RGN 12
AGD 11
MGD 11
RAD 11
RGQ 11
RGS 11
RND 11
RSD 11
SGD 11
TGD 11
For a given word, assign a score to neighborhood words based on scoring matrix.
W (word length) and T (threshold for a word match) modulate speed and sensitivity
T=12

17. Word length
As the threshold score for a word match is increased, there are fewer matches.
This makes the search more specific, but less sensitive.

18. 2-hit seeding Alignments often have multiple word hits in clusters.
Isolated word hits are frequently false leads.
Most alignments have large ungapped regions.
Requiring 2 word hits on the same diagonal greatly increases speed at a slight cost in sensitivity. Similar to paired-end read mapping concept.

19. Extension of the seed alignments
Alignments are extended from seeds in each direction.
Extension is terminated when the maximum score drops below X.
Example
The quick brown fox jumps over the lazy dog.
The quiet brown cat purrs when she sees him.

20. BLAST statistics How significant is this similarity?
>gi| |ref|NP_ | (NC_004193) 3-oxoacyl-(acyl carrier protein) reductase [Oceanobacillus iheyensis] Length = Score = 38.9 bits (89), Expect = 3e-05 Identities = 17/40 (42%), Positives = 26/40 (64%) Frame = -1 Query: 4146 VTGAGHGLGRAISLELAKKGCHIAVVDINVSGAEDTVKQI VTGA G+G+AI+ A +G + V D+N GA+ V++I Sbjct: 10 VTGAASGMGKAIATLYASEGAKVIVADLNEEGAQSVVEEI 49
How significant is this similarity?

21. Scoring the alignment S (score) 4 -1 4
Query: 4146 VTGAGHGLGRAISLELAKKGCHIAVVDINVSGAEDTVKQI VTGA G+G+AI+ A +G + V D+N GA+ V++I Sbjct: 10 VTGAASGMGKAIATLYASEGAKVIVADLNEEGAQSVVEEI 49 4 -1 4
S (score)

22. Evaluating an alignment
How many alignments of a given score would be expected by chance, i.e. without common evolutionary history?
We expect more chance hits when the search database or when the query sequence(s) is longer.
For a higher score threshold, we expect fewer chance hits.

23. The Karlin-Altschul equation
The “Expect” or “E-value”
A minor constant
Scaling factor
Normalized score
Expected number of alignments
Raw score
Length of query
Length of database
Search space
The “P-value”

24. The sum-statistics
Multiple aligning regions from a single sequence should increase our belief that the sequence is evolutionarily related to our query.
Sum statistics merge the significance (decrease the E-value) for groups of consistent alignments.

25. The sum-statistics
The sum score is not
reported by BLAST!