The Treeterbi and Parallel Treeterbi algorithms: efficient, optimal decoding for ordinary, generalized and pair HMMs

Motivation: Hidden Markov models (HMMs) and generalized HMMs been successfully applied to many problems, but the standard Viterbi algorithm for computing the most probable interpretation of an input sequence (known as decoding) requires memory proportional to the length of the sequence, which can be...

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Vydáno v:Bioinformatics Ročník 23; číslo 5; s. 545 - 554
Hlavní autoři: Keibler, Evan, Arumugam, Manimozhiyan, Brent, Michael R.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Oxford University Press 01.03.2007
Oxford Publishing Limited (England)
Témata:
Algorithms
Biological and medical sciences
Chromosomes
Chromosomes, Human
Computational Biology
DNA, Complementary - chemistry
Fundamental and applied biological sciences. Psychology
General aspects
Genes
Genomics - methods
Humans
Markov Chains
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Programming Languages
Original document
Parallel algorithm
Gene
Memory
Prediction
Hidden Markov model
Software
Web site
Bioinformatics
Viterbi decoding
Comparative study
Optimization
ISSN:1367-4803, 1367-4811, 1367-4811, 1460-2059
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Shrnutí:Motivation: Hidden Markov models (HMMs) and generalized HMMs been successfully applied to many problems, but the standard Viterbi algorithm for computing the most probable interpretation of an input sequence (known as decoding) requires memory proportional to the length of the sequence, which can be prohibitive. Existing approaches to reducing memory usage either sacrifice optimality or trade increased running time for reduced memory. Results: We developed two novel decoding algorithms, Treeterbi and Parallel Treeterbi, and implemented them in the TWINSCAN/N-SCAN gene-prediction system. The worst case asymptotic space and time are the same as for standard Viterbi, but in practice, Treeterbi optimally decodes arbitrarily long sequences with generalized HMMs in bounded memory without increasing running time. Parallel Treeterbi uses the same ideas to split optimal decoding across processors, dividing latency to completion by approximately the number of available processors with constant average overhead per processor. Using these algorithms, we were able to optimally decode all human chromosomes with N-SCAN, which increased its accuracy relative to heuristic solutions. We also implemented Treeterbi for Pairagon, our pair HMM based cDNA-to-genome aligner. Availability: The TWINSCAN/N-SCAN/PAIRAGON open source software package is available from http://genes.cse.wustl.edu. Contact: brent@cse.wustl.edu
Bibliografie:istex:8AF55F7C783920AEB3C030C5D56A7DCDA7A930DF
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ISSN:1367-4803
1367-4811
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btl659