This document discusses various methods for identifying similar biological sequences, including: - BLAST, which uses fast heuristic algorithms to identify near-identical sub-sequences for alignment. - Profile-based searches like HMMER that use hidden Markov models built from multiple sequence alignments, providing more accurate results than single-sequence methods. - Covariance models for RNA sequence searches, which incorporate information about base pairing into the models. - Benchmarking of tools showed profile methods like HMMER and covariance models are more accurate than BLAST for detecting remote homology. Resources like Pfam and Rfam provide profile databases for protein and RNA families.

1. Homology Search
Paul Gardner
March 24, 2015
Paul Gardner Homology Search

2. News & Views reminder (20% of your course grade, due
March 26, Reviewed April 2 (5/20), Revisions April 28
(15/20))
Meredith et al. (2014) Evidence for a single loss of
mineralized teeth in the common avian ancestor. Science
Nunez et al. (2015) Integrase-mediated spacer acquisition
during CRISPR-Cas adaptive immunity. Nature
Paul Gardner Homology Search

3. Homology search
In a huge collection of biological
sequences how can you locate
similar sequences?
by using heuristic, super fast,
sequence alignment methods
Paul Gardner Homology Search

4. BLAST
Paul Gardner Homology Search

5. BLAST
Identify all ’hits’ of at least W long
Find any hits on the same diagonal of an alignment matrix
Trigger a full alignment in that region
Basic idea: identify near-identical sub-sequences first → align any
hits in full
Paul Gardner Homology Search

6. What does that E-value (Expect) mean?
>gb|CP001191.1| Rhizobium leguminosarum bv. trifolii WSM2304, complete genome
Length=4537948
Features in this part of subject sequence:
cold-shock DNA-binding domain protein
Score = 57.2 bits (62), Expect = 2e-05
Identities = 78/106 (74%), Gaps = 6/106 (6%)
Strand=Plus/Plus
Query 1 CTTCGTCAGATTTCCTCTCAATATCGATCATACCGGACTGATATTCGTCCGG----GAAC
|| |||||||| ||||||||| |||||| | | | || |||| |||| ||||
Sbjct 828507 CTCCGTCAGATATCCTCTCAACATCGATACGGCTTGTCGGACATTCTTCCGCAGGCGAAC
Query 57 TCTAGCGATTGAAA-GGAAATCGTTATGAACTCAGGCACCGTAAAG
| | || |||||| ||| ||||||||||| |||||| ||| |||
Sbjct 828567 ACAA-CGGTTGAAAAGGAGATCGTTATGAATTCAGGCGTCGTCAAG
Paul Gardner Homology Search

7. How can we evaluate the significance of a score?
Note that a bit-score of 57.2 by itself is not that useful.
It depends on the sequence & database size & composition.
To counter this we can compute an Expect-value (E-value).
This is the expected number of hits with the observed score for
the given query and database sizes.
P-values can also be used
0 100 200 300 400 500 600 700
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Separating true from false hits
score (bits)
Num.matches
Random sequences/Negative controls
True homologs/Positive controls
Threshold
False negatives
True positives
False positives
True negatives
Paul Gardner Homology Search

8. How can we evaluate the significance of a score?
0 100 200 300 400 500 600 700
0
2000
4000
6000
8000
10000
Separating true from false hits
score (bits)
Num.matches
Random sequences/Negative controls
True homologs/Positive controls
Threshold
False negatives
True positives
False positives
True negatives
E = κMN2−λx
E: E-value
M&N: query &
database size
κ&λ: fitting
parameters
Paul Gardner Homology Search

9. BLAST is not the only, or best tool for the job!
Paul Gardner Homology Search

10. Profile-based homology search
Krogh, A. et al. (1994) Hidden Markov models in computational biology. Applications to protein modeling. J Mol
Biol.
Image provided by Eric Nawrocki.
Paul Gardner Homology Search

11. Profile-based homology search – scoring sequences
Image provided by Eric Nawrocki.
Paul Gardner Homology Search

12. Profile HMM are slightly more complicated
A tree-weighting scheme takes care of unbalanced
alignments
Dirichlet-mixture priors are used to incorporate information
about amino-acid biochemistry
Effective sequence number is used to down-weight priors
when many sequences are available
Transition probabilities to Insert & Delete states are estimated
from the alignment
Paul Gardner Homology Search

13. Why not just use BLAST?
ACCURACY!
Every benchmark of homology search tools has shown that
profile methods are more accurate than single-sequence
methods.
Eddy (2011) Accelerated Profile HMM Searches. PLoS
Computational Biology.
Paul Gardner Homology Search

14. Why not just use BLAST?
SPEED! To search a single query vs a database of all proteins:
BLAST: searches 42 million UniProt sequences
HMMER: searches 15,000 Pfam profiles
The search space is ∼ 3, 000x smaller for profiles
Save Planet Earth, use HMMER3
Eddy (2011) Accelerated Profile HMM Searches. PLoS
Computational Biology.
Paul Gardner Homology Search

15. Pfam
What is a Pfam-A Entry?
hmmsearch
hmmbuild
hmmalign
SEED
HMM
OUTOUT
ALIGNDESC
Slide borrowed from Rob Finn.
Paul Gardner Homology Search

16. But, what about RNA?
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Paul Gardner Homology Search

17. Covariance models
Nawrocki & Eddy (2007) Query-Dependent Banding (QDB) for Faster RNA Similarity Searches. PLOS
computational biology.
Paul Gardner Homology Search

18. Benchmark
Freyhult, Bollback & Gardner (2007) Exploring genomic dark matter: A critical assessment of the performance of
homology search methods on noncoding RNA. Genome Research.
Paul Gardner Homology Search

19. Rfam
Paul Gardner Homology Search

20. Relevant reading
Reviews:
Eddy SR (2004) What is a hidden Markov model? Nature
Biotechnology.
Methods:
Altschul SF et al. (1997) Gapped BLAST and PSI-BLAST: a
new generation of protein database search programs. Nucleic
acids research.
Eddy (2011) Accelerated Profile HMM Searches. PLoS
Computational Biology.
Paul Gardner Homology Search

21. The End
Paul Gardner Homology Search