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Ten simple rules for providing effective bioinformatics research support.

Authors :
Judit Kumuthini
Michael Chimenti
Sven Nahnsen
Alexander Peltzer
Rebone Meraba
Ross McFadyen
Gordon Wells
Deanne Taylor
Mark Maienschein-Cline
Jian-Liang Li
Jyothi Thimmapuram
Radha Murthy-Karuturi
Lyndon Zass
Source :
PLoS Computational Biology, Vol 16, Iss 3, p e1007531 (2020)
Publication Year :
2020
Publisher :
Public Library of Science (PLoS), 2020.

Abstract

Life scientists are increasingly turning to high-throughput sequencing technologies in their research programs, owing to the enormous potential of these methods. In a parallel manner, the number of core facilities that provide bioinformatics support are also increasing. Notably, the generation of complex large datasets has necessitated the development of bioinformatics support core facilities that aid laboratory scientists with cost-effective and efficient data management, analysis, and interpretation. In this article, we address the challenges-related to communication, good laboratory practice, and data handling-that may be encountered in core support facilities when providing bioinformatics support, drawing on our own experiences working as support bioinformaticians on multidisciplinary research projects. Most importantly, the article proposes a list of guidelines that outline how these challenges can be preemptively avoided and effectively managed to increase the value of outputs to the end user, covering the entire research project lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage). In addition, we highlight the importance of clear and transparent communication, comprehensive preparation, appropriate handling of samples and data using monitoring systems, and the employment of appropriate tools and standard operating procedures to provide effective bioinformatics support.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X and 15537358
Volume :
16
Issue :
3
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.fc8a7c5cb44b4f83acfa3fde4ccf4a8a
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1007531