MLgsc: A Maximum-Likelihood General Sequence Classifier
Author(s) -
Thomas Junier,
Vincent Hervé,
Tina Wunderlin,
Pilar Junier
Publication year - 2015
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0129384
Subject(s) - computer science , software , data mining , multiple sequence alignment , unix , classifier (uml) , sequence alignment , gene prediction , phylogenetic tree , bioinformatics , computational biology , pattern recognition (psychology) , artificial intelligence , biology , gene , genetics , programming language , genome , peptide sequence
We present software package for classifying protein or nucleotide sequences to user-specified sets of reference sequences. The software trains a model using a multiple sequence alignment and a phylogenetic tree, both supplied by the user. The latter is used to guide model construction and as a decision tree to speed up the classification process. The software was evaluated on all the 16S rRNA gene sequences of the reference dataset found in the GreenGenes database. On this dataset, the software was shown to achieve an error rate of around 1% at genus level. Examples of applications based on the nitrogenase subunit NifH gene and a protein-coding gene found in endospore-forming Firmicutes is also presented. The programs in the package have a simple, straightforward command-line interface for the Unix shell, and are free and open-source. The package has minimal dependencies and thus can be easily integrated in command-line based classification pipelines.
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