Journal article
Ten simple rules for providing effective bioinformatics research support
PLoS computational biology, Vol.16(3), pp.e1007531-e1007531
03/2020
DOI: 10.1371/journal.pcbi.1007531
PMCID: PMC7098546
PMID: 32214318
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.
Details
- Title: Subtitle
- Ten simple rules for providing effective bioinformatics research support
- Creators
- Judit Kumuthini - H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South AfricaMichael Chimenti - Iowa Institute of Human Genetics, Bioinformatics Division, Carver College of Medicine, University of Iowa, Iowa City, United States of AmericaSven Nahnsen - Quantitative Biology Centre, Eberhard Karls University of Tübingen, Tübingen, Baden-Württemberg, GermanyAlexander Peltzer - Quantitative Biology Centre, Eberhard Karls University of Tübingen, Tübingen, Baden-Württemberg, GermanyRebone Meraba - H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South AfricaRoss McFadyen - H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South AfricaGordon Wells - H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South AfricaDeanne Taylor - Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of AmericaMark Maienschein-Cline - Research Informatics Core, University of Illinois at Chicago, Chicago, Illinois, United States of AmericaJian-Liang Li - Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences, Durham, North Carolina, United States of AmericaJyothi Thimmapuram - Bioinformatics Core, Purdue University, West Lafayette, Indiana, United States of AmericaRadha Murthy-Karuturi - Department of Computational Sciences, The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of AmericaLyndon Zass - H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South Africa
- Resource Type
- Journal article
- Publication Details
- PLoS computational biology, Vol.16(3), pp.e1007531-e1007531
- DOI
- 10.1371/journal.pcbi.1007531
- PMID
- 32214318
- PMCID
- PMC7098546
- NLM abbreviation
- PLoS Comput Biol
- ISSN
- 1553-734X
- eISSN
- 1553-7358
- Publisher
- United States
- Language
- English
- Date published
- 03/2020
- Academic Unit
- Iowa Institute of Human Genetics
- Record Identifier
- 9984066345202771
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