Year: 2020
Type: article
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.
Source: Bioinformatics
Institution: University of California, Berkeley
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,989
Awards: Developing the JBrowse Genome Browser to Visualize Structural Variants and Cancer Genomics Data, Enhancements to the GMOD Suite of Genome Annotation and Visualization Tools, Berkeley Training Program in Genomics and Computational Biology, Developing the Apollo software for high-throughput annotation of multiple genomes
OpenAlex ID: W3042865765