MEvoLib v1.0: The first molecular evolution library for Python

Álvarez-Jarreta, J. (Universidad de Zaragoza) ; Ruiz-Pesini, E. (Universidad de Zaragoza)

Resumen: Background: Molecular evolution studies involve many different hard computational problems solved, in most cases, with heuristic algorithms that provide a nearly optimal solution. Hence, diverse software tools exist for the different stages involved in a molecular evolution workflow. Results: We present MEvoLib, the first molecular evolution library for Python, providing a framework to work with different tools and methods involved in the common tasks of molecular evolution workflows. In contrast with already existing bioinformatics libraries, MEvoLib is focused on the stages involved in molecular evolution studies, enclosing the set of tools with a common purpose in a single high-level interface with fast access to their frequent parameterizations. The gene clustering from partial or complete sequences has been improved with a new method that integrates accessible external information (e.g. GenBank''s features data). Moreover, MEvoLib adjusts the fetching process from NCBI databases to optimize the download bandwidth usage. In addition, it has been implemented using parallelization techniques to cope with even large-case scenarios. Conclusions: MEvoLib is the first library for Python designed to facilitate molecular evolution researches both for expert and novel users. Its unique interface for each common task comprises several tools with their most used parameterizations. It has also included a method to take advantage of biological knowledge to improve the gene partition of sequence datasets. Additionally, its implementation incorporates parallelization techniques to enhance computational costs when handling very large input datasets.

Idioma: Inglés

DOI: 10.1186/s12859-016-1303-3

Año: 2016

Publicado en: BMC BIOINFORMATICS 17, 436 (2016), [8 pp.]
ISSN: 1471-2105

Factor impacto JCR: 2.448 (2016)

Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 10 / 57 = 0.175 (2016) - Q1 - T1
Categ. JCR: BIOCHEMICAL RESEARCH METHODS rank: 38 / 77 = 0.494 (2016) - Q2 - T2
Categ. JCR: BIOTECHNOLOGY & APPLIED MICROBIOLOGY rank: 67 / 160 = 0.419 (2016) - Q2 - T2

Factor impacto SCIMAGO: 1.581 - Applied Mathematics (Q1) - Biochemistry (Q1) - Computer Science Applications (Q1) - Molecular Biology (Q2) - Structural Biology (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA/B33
Financiación: info:eu-repo/grantAgreement/ES/DGA/T27
Financiación: info:eu-repo/grantAgreement/ES/FIS/PI14-00070
Financiación: info:eu-repo/grantAgreement/ES/MEC/FPU-AP2010-1058
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2011-27479-C04-01

Tipo y forma: Article (Published version)

Área (Departamento): Área Bioquímica y Biolog.Mole. (Dpto. Bioq.Biolog.Mol. Celular)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)