HMM Sampling and Applications toGene Finding and Alignment

European Conference on Computational Biology 2003

Simon Cawley* and Lior Pachter+

and thanks to Eli Rusman

* Affymetrix+ UC Berkeley Mathematics Dept

Conservation of alternative splicing between human and

mouse• Modrek and Lee: 40-60% of human genes

have alternative splice forms. Nature Genetics 2002.

• Nurtdinov et al. 75% of human alternative splice forms are conserved in mouse.

Human Molecular Genetics 2003.

Can we develop ab-initio methods for detecting conserved alternative splice sites?

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AA G A T T A C C A C A

Sequence Alignment

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max

Finding the optimal alignment

ai,j = w ai-1,j + w ai,j-1 + si,j ai-1,j-1

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Alignment forward variables for positions [1,i] and [1,j]

in each sequence

Match/mismatch probabilities forpositions i,j in each sequence

gap probabilities

Sampling to find alternative alignments

Linear Space Sampling

Sequences length T,U

To obtain k samples

Time complexity: O(TU+k(T+U))

Memory requirements: O(T+U)

Hirschberg’s divide and conquer algorithm

Time complexity: O(TU)

Memory requirements: O(T+U)

Alternative Splicing in Mammalian Genomes

pre-mRNA

TRANSLATION

SPLICING

Protein I

ALTERNATIVE SPLICING

Protein II

TRANSLATION

M. Alexandersson, S. Cawley, L. Pachter, SLAM- Cross-species gene finding and alignment with a

generalized pair hidden Markov model, Genome Research, 13 (2003) p 496-502

Cross-species simultaneous gene finding

and alignment

Modeling gene features

5’ 3’

Exon 1 Exon 2 Exon 3Intron 1 Intron 2

CNS CNS CNS

[human]

[mouse]

The SLAM hidden Markov model

SLAM components• Splice site detector

– VLMM

• Intron and intergenic regions– 2nd order Markov chain

– independent geometric lengths

• Coding sequence– PHMM on protein level

– generalized length distribution

• Conserved non-coding sequence– PHMM on DNA level

SLAM input and output

• Input:– Pair of homologous sequences.

• Output:– CDS and CNS predictions in both sequences.– Protein predictions.– Protein and CNS alignment.

http://bio.math.berkeley.edu/slam/

Input:

Output:

Methodology for identifying alternative splice sites

• Compiled SLAM gene predictions for the human, mouse and rat genomes.

• Identified a set of 3400 human/mouse/rat gene triples with consistent predictions from hs/mm and hs/rn analyses.

• For each triple, sampled sub-optimal parses from hs/mm and hs/rn runs

• Collected alternative exons (non-Viterbi exons) that appeared in both the hs/mm and hs/rn runs

• Examined overlap with RefSeq genes, mRNAs and ESTs

SLAM whole genome predictions

• Built a whole genome homology map (Colin Dewey)http://baboon.math.berkeley.edu/~cdewey/homologyMaps/

• Pre-aligned the homologous blocks to reduce the SLAM search space (Nicolas Bray using AVID)

http://baboon.math.berkeley.edu/mavid/http://hanuman.math.berkeley.edu/kbrowser/

• Ran SLAM on the resulting blockshttp://bio.math.berkeley.edu/slam/mouse/http://bio.math.berkeley.edu/slam/rat/

[human]

[mouse]

[rat]

Comparing predicted alternative exons to ESTs and

mRNAshuman/mouse/rat alternative

exonshuman/mouse alternative

exons

EST/mRNANo

EST/mRNA EST/mRNANo

EST/mRNA

Gene count 29 344 461 3296

Alt. Exon count 29 441 557 7240

Shifties 28 209 262 2227

Newbies 1 232 295 5013

Conclusions

• Sampling is memory efficient, fast, and should be used routinely for alignment applications.

• Conserved alternative splice forms can be detected ab-initio.

• The extent of alternative splicing conservation is currently unclear. Sampling provides an alternative approach for investigating this problem- one that is not sensitive to biases in EST data.

• Problem: design effective and scalable validation strategies for alternative splice sites.