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Training in High-Throughput Sequencing: Common Guidelines to Enable Material Sharing, Dissemination, and Reusability
Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC). Department of Forest Genetics and Plant Physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, Umeå, Sweden.
2016 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 6, e1004937Article in journal (Refereed) Published
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Abstract [en]

The advancement of high-throughput sequencing (HTS) technologies and the rapid development of numerous analysis algorithms and pipelines in this field has resulted in an unprecedentedly high demand for training scientists in HTS data analysis. Embarking on developing new training materials is challenging for many reasons. Trainers often do not have prior experience in preparing or delivering such materials and struggle to keep them up to date. A repository of curated HTS training materials would support trainers in materials preparation, reduce the duplication of effort by increasing the usage of existing materials, and allow for the sharing of teaching experience among the HTS trainers' community. To achieve this, we have developed a strategy for materials' curation and dissemination. Standards for describing training materials have been proposed and applied to the curation of existing materials. A Git repository has been set up for sharing annotated materials that can now be reused, modified, or incorporated into new courses. This repository uses Git; hence, it is decentralized and self-managed by the community and can be forked/built-upon by all users. The repository is accessible at http://bioinformatics.upsc.se/htmr.

Place, publisher, year, edition, pages
2016. Vol. 12, no 6, e1004937
National Category
Bioinformatics (Computational Biology)
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URN: urn:nbn:se:umu:diva-124349DOI: 10.1371/journal.pcbi.1004937ISI: 000379349700027PubMedID: 27309738OAI: oai:DiVA.org:umu-124349DiVA: diva2:1034524
Available from: 2016-10-12 Created: 2016-08-04 Last updated: 2017-11-29Bibliographically approved

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