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ABSTRACT: Motivation
Many software libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone.Results
We present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line 'recipes' for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage and nanopore sequence analysis.Availability and implementation
Machine Boss is released under the BSD-3 open source license and is available from http://machineboss.org/.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Silvestre-Ryan J
PROVIDER: S-EPMC8034524 | biostudies-literature | 2021 Apr
REPOSITORIES: biostudies-literature
Silvestre-Ryan Jordi J Wang Yujie Y Sharma Mehak M Lin Stephen S Shen Yolanda Y Dider Shihab S Holmes Ian I
Bioinformatics (Oxford, England) 20210401 1
<h4>Motivation</h4>Many software libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone.<h4>Results</h4>We present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining aut ...[more]