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MNHN-Tree-Tools: a toolbox for tree inference using multi-scale clustering of a set of sequences
Author(s) -
Thomas Haschka,
Loı̈c Ponger,
Christophe Escudé,
Julien Mozziconacci
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab430
Subject(s) - computer science , tree (set theory) , toolbox , set (abstract data type) , cluster analysis , inference , data mining , artificial intelligence , computational biology , biology , programming language , mathematics , mathematical analysis
Genomic sequences are widely used to infer the evolutionary history of a given group of individuals. Many methods have been developed for sequence clustering and tree building. In the early days of genome sequencing, these were often limited to hundreds of sequences but due to the surge of high throughput sequencing, it is now common to have millions of sampled sequences at hand. We introduce MNHN-Tree-Tools, a high performance set of algorithms that builds multi-scale, nested clusters of sequences found in a FASTA file. MNHN-Tree-Tools does not rely on multiple sequence alignment and can thus be used on large datasets to infer a sequence tree. Herein, we outline two applications: a human alpha-satellite repeats classification and a tree of life derivation from 16S/18S rDNA sequences.

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