TREE2FASTA: a flexible Perl script for batch extraction of FASTA sequences from exploratory phylogenetic trees

BMC Research Notes. 2018;11(1):1-6 DOI 10.1186/s13104-018-3268-y

 

Journal Homepage

Journal Title: BMC Research Notes

ISSN: 1756-0500 (Online)

Publisher: BMC

LCC Subject Category: Medicine | Science: Biology (General)

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Thomas Sauvage (Department of Biology, University of Louisiana at Lafayette)
Sophie Plouviez (Department of Biology, University of Louisiana at Lafayette)
William E. Schmidt (Department of Biology, University of Louisiana at Lafayette)
Suzanne Fredericq (Department of Biology, University of Louisiana at Lafayette)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 15 weeks

 

Abstract | Full Text

Abstract Objective The body of DNA sequence data lacking taxonomically informative sequence headers is rapidly growing in user and public databases (e.g. sequences lacking identification and contaminants). In the context of systematics studies, sorting such sequence data for taxonomic curation and/or molecular diversity characterization (e.g. crypticism) often requires the building of exploratory phylogenetic trees with reference taxa. The subsequent step of segregating DNA sequences of interest based on observed topological relationships can represent a challenging task, especially for large datasets. Results We have written TREE2FASTA, a Perl script that enables and expedites the sorting of FASTA-formatted sequence data from exploratory phylogenetic trees. TREE2FASTA takes advantage of the interactive, rapid point-and-click color selection and/or annotations of tree leaves in the popular Java tree-viewer FigTree to segregate groups of FASTA sequences of interest to separate files. TREE2FASTA allows for both simple and nested segregation designs to facilitate the simultaneous preparation of multiple data sets that may overlap in sequence content.