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A framework for space-efficient variable-order Markov models

Fabio Cunial, Jarno Alanko, Djamal Belazzougui
doi: https://doi.org/10.1101/443101
Fabio Cunial
*Max Planck Institute for Molecular Cell Biology and Genetics (MPI-CBG), and Center for Systems Biology Dresden (CSBD), Dresden, 01307, Germany.
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  • For correspondence: cunial@mpi-cbg.de
Jarno Alanko
†Department of Computer Science, University of Helsinki, Helsinki, 00014, Finland
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Djamal Belazzougui
‡CAPA, DTISI, Centre de Récherche sur l’Information Scientifique et Technique, Algiers, Algeria
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Abstract

Motivation Markov models with contexts of variable length are widely used in bioinformatics for representing sets of sequences with similar biological properties. When models contain many long contexts, existing implementations are either unable to handle genome-scale training datasets within typical memory budgets, or they are optimized for specific model variants and are thus inflexible.

Results We provide practical, versatile representations of variable-order Markov models and of interpolated Markov models, that support a large number of context-selection criteria, scoring functions, probability smoothing methods, and interpolations, and that take up to 4 times less space than previous implementations based on the suffix array, regardless of the number and length of contexts, and up to 10 times less space than previous trie-based representations, or more, while matching the size of related, state-of-the-art data structures from Natural Language Processing. We describe how to further compress our indexes to a quantity related to the redundancy of the training data, saving up to 90% of their space on repetitive datasets, and making them become up to 60 times smaller than previous implementations based on the suffix array. Finally, we show how to exploit constraints on the length and frequency of contexts to further shrink our compressed indexes to half of their size or more, achieving data structures that are 100 times smaller than previous implementations based on the suffix array, or more. This allows variable-order Markov models to be trained on bigger datasets and with longer contexts on the same hardware, thus possibly enabling new applications.

Availability and implementation https://github.com/jnalanko/VOMM

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted October 14, 2018.
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A framework for space-efficient variable-order Markov models
Fabio Cunial, Jarno Alanko, Djamal Belazzougui
bioRxiv 443101; doi: https://doi.org/10.1101/443101
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A framework for space-efficient variable-order Markov models
Fabio Cunial, Jarno Alanko, Djamal Belazzougui
bioRxiv 443101; doi: https://doi.org/10.1101/443101

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