000057763 001__ 57763 000057763 005__ 20200221144223.0 000057763 0247_ $$2doi$$a10.1186/s12859-016-1303-3 000057763 0248_ $$2sideral$$a96984 000057763 037__ $$aART-2016-96984 000057763 041__ $$aeng 000057763 100__ $$0(orcid)0000-0002-0946-0957$$aÁlvarez-Jarreta, J.$$uUniversidad de Zaragoza 000057763 245__ $$aMEvoLib v1.0: The first molecular evolution library for Python 000057763 260__ $$c2016 000057763 5060_ $$aAccess copy available to the general public$$fUnrestricted 000057763 5203_ $$aBackground: 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. 000057763 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TIN2011-27479-C04-01$$9info:eu-repo/grantAgreement/ES/MEC/FPU-AP2010-1058$$9info:eu-repo/grantAgreement/ES/FIS/PI14-00070$$9info:eu-repo/grantAgreement/ES/DGA/T27$$9info:eu-repo/grantAgreement/ES/DGA/B33 000057763 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000057763 590__ $$a2.448$$b2016 000057763 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b10 / 57 = 0.175$$c2016$$dQ1$$eT1 000057763 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b38 / 77 = 0.494$$c2016$$dQ2$$eT2 000057763 591__ $$aBIOTECHNOLOGY & APPLIED MICROBIOLOGY$$b67 / 160 = 0.419$$c2016$$dQ2$$eT2 000057763 592__ $$a1.581$$b2016 000057763 593__ $$aApplied Mathematics$$c2016$$dQ1 000057763 593__ $$aBiochemistry$$c2016$$dQ1 000057763 593__ $$aComputer Science Applications$$c2016$$dQ1 000057763 593__ $$aMolecular Biology$$c2016$$dQ2 000057763 593__ $$aStructural Biology$$c2016$$dQ2 000057763 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000057763 700__ $$0(orcid)0000-0002-0269-7337$$aRuiz-Pesini, E.$$uUniversidad de Zaragoza 000057763 7102_ $$11002$$2060$$aUniversidad de Zaragoza$$bDpto. Bioq.Biolog.Mol. Celular$$cÁrea Bioquímica y Biolog.Mole. 000057763 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000057763 773__ $$g17, 436 (2016), [8 pp.]$$pBMC bioinformatics$$tBMC BIOINFORMATICS$$x1471-2105 000057763 8564_ $$s540528$$uhttps://zaguan.unizar.es/record/57763/files/texto_completo.pdf$$yVersión publicada 000057763 8564_ $$s11082$$uhttps://zaguan.unizar.es/record/57763/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000057763 909CO $$ooai:zaguan.unizar.es:57763$$particulos$$pdriver 000057763 951__ $$a2020-02-21-13:16:29 000057763 980__ $$aARTICLE