Efficient Updating of Biological Sequence Analyses

We present a novel approach for reducing the computational complexity of updating homologies produced by a wide class of popular state-of-the-art algorithms in comparative computational biology. The algorithms that we consider use hidden Markov models (HMMs) and a Viterbi recursion to evaluate match...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing Vol. 2; no. 3; pp. 365 - 377
Main Authors: Changjin Hong, Tewfik, A.H.
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2008
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Biological
Biological system modeling
Computation
Computational biology
Computational complexity
Dynamic programming
Error correction
Evolution (biology)
Genetic mutations
Hidden Markov models
hidden Markov models (HMMs)
Matching
Mathematical models
minimum spanning tree
Optimization
Proteins
Recursion
sensitivity analysis
Sequences
shortest path
Studies
Tolerances
Viterbi algorithm
Viterbi decoding algorithm
ISSN:1932-4553, 1941-0484
Online Access:Get full text
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Summary:We present a novel approach for reducing the computational complexity of updating homologies produced by a wide class of popular state-of-the-art algorithms in comparative computational biology. The algorithms that we consider use hidden Markov models (HMMs) and a Viterbi recursion to evaluate matches between sequences, or between a sequence and models. Such updates occur frequently in practice as researchers discover errors in biological sequences or analyze multiple nearly similar sequences, e.g., in a family of proteins that underwent mutations during evolution. The proposed algorithm interprets the Viterbi recursion as an update of an optimal minimum spanning tree in a shortest path problem. We propose the novel concept of a relative node tolerance bound and show how it can be used to guarantee that one or more partial subtrees of a minimum spanning tree obtained before encountering the perturbations remain optimal. We also describe how to compute and use in real-time the relative node tolerance bounds to skip most unperturbed parts of a sequence while computing the new optimal solution. To further reduce the computational overhead associated with the tolerance bound evaluation, we present and exploit a statistical analysis of the matching procedure that estimates how many columns in the dynamic program that corresponds to the matching problem are affected by a change in a preceding column. The resulting "reusable" Viterbi decoding algorithm can update a matching result in less than a third to a fifth of the time required to compute a new match by performing a normal matching procedure, i.e., running a Viterbi algorithm with updated sequences against a base hidden Markov model.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2008.924382