A framework for space-efficient variable-order Markov models

dc.contributor.authorCunial, Fabio
dc.contributor.authorAlanko, Jarno
dc.contributor.authorBelazzougui, Djamal
dc.description.abstractMotivation: 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 four times less space than previous implementations based on the suffix array, regardless of the number and length of contexts, and up to ten 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 very repetitive datasets, and making them become up to sixty 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 a hundred times smaller than previous implementations based on the suffix array, or more. This allows variable-order Markov models to be used with bigger datasets and with longer contexts on the same hardware, thus possibly enabling new applications. Availability and implementation: https://github.com/jnalanko/VOMM en_US
dc.publisherOxford University Pressen_US
dc.relation.ispartofseriesBioinformatics;Volume 35, Issue 22
dc.structureCalcul pervasif et mobile (Pervasive and Mobile Computing group)en_US
dc.subjectVariable-order Markov modelsen_US
dc.subjectTruncated suffix triesen_US
dc.subjectSuccinct data structuresen_US
dc.subjectBurrows-Wheeler transformen_US
dc.subjectBiological sequence analysisen_US
dc.subjectProtein familiesen_US
dc.titleA framework for space-efficient variable-order Markov modelsen_US