SWIMmeR: an R-based software to unveiling crucial nodes in complex biological networks

Author:

Paci Paola12ORCID,Fiscon Giulia13ORCID

Affiliation:

1. Institute for Systems Analysis and Computer Science “Antonio Ruberti”, Dipartimento di Ingegneria, ICT e tecnologie per l'energia e i trasporti, National Research Council, Via dei Taurini 19 00185, Rome, Italy

2. Dipartimento di Ingegneria Informatica, Automatica e Gestionale (DIAG) "A. Ruberti", Sapienza Università di Roma Via Ariosto, 25 00185 Roma, Italia

3. Fondazione per la Medicina Personalizzata, Via Goffredo Mameli, 3/116122 Genova, Italy

Abstract

Abstract Summary We present SWIMmeR, an open-source version of its predecessor SWIM (SWitchMiner) that is a network-based tool for mining key (switch) genes that are associated with intriguing patterns of molecular co-abundance and may play a crucial role in phenotypic transitions in various biological settings. SWIM was originally written in MATLAB®, a proprietary programming language that requires the purchase of a license to install, manipulate, operate and run the software. Over the last years, SWIM has sparked a widespread interest within the scientific community thanks to the promising results obtained through its application in a broad range of phenotype-specific scenarios, spanning from complex diseases to grapevine berry maturation. This success has created the call for it to be distributed in a freely accessible, open-source, runtime environment, such as R, aimed at a general audience of non-expert users that cannot afford the leading proprietary solution. SWIMmeR is provided as a comprehensive collection of R functions and it also includes several additional features that make it less intensive in terms of computer time and more efficient in terms of usability and further implementation and extension. Availability and implementation The SWIMmeR source code is freely available at https://github.com/sportingCode/SWIMmeR.git, along with a practical user guide, including a usage example of its application on breast cancer dataset. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

PRIN 2017—Settore ERC LS2—Codice

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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