Show simple item record

dc.contributor.authorAlexander, Harriet  Concept link
dc.contributor.authorJohnson, Lisa K  Concept link
dc.contributor.authorBrown, C. Titus  Concept link
dc.date.accessioned2019-04-15T19:09:34Z
dc.date.available2019-04-15T19:09:34Z
dc.date.issued2018-12-13
dc.identifier.citationAlexander, H., Johnson, L. K., & Brown, C. T. (2019). Keeping it light: (re)analyzing community-wide datasets without major infrastructure. Gigascience, 8(2), giy159.en_US
dc.identifier.urihttps://hdl.handle.net/1912/24014
dc.description© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Alexander, H., Johnson, L. K., & Brown, C. T.. Keeping it light: (re)analyzing community-wide datasets without major infrastructure. Gigascience, 8(2),(2019): giy159, doi:10.1093/gigascience/giy159.en_US
dc.description.abstractDNA sequencing technology has revolutionized the field of biology, shifting biology from a data-limited to data-rich state. Central to the interpretation of sequencing data are the computational tools and approaches that convert raw data into biologically meaningful information. Both the tools and the generation of data are actively evolving, yet the practice of re-analysis of previously generated data with new tools is not commonplace. Re-analysis of existing data provides an affordable means of generating new information and will likely become more routine within biology, yet necessitates a new set of considerations for best practices and resource development. Here, we discuss several practices that we believe to be broadly applicable when re-analyzing data, especially when done by small research groups.en_US
dc.description.sponsorshipFunding was provided by the Gordon and Betty Moore Foundation (award GBMF4551 to C.T.B.).en_US
dc.publisherOxford University Pressen_US
dc.relation.urihttps://doi.org/10.1093/gigascience/giy159
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectreproducibilityen_US
dc.subjectdata reuseen_US
dc.subjectopen dataen_US
dc.titleKeeping it light: (re)analyzing community-wide datasets without major infrastructureen_US
dc.typeArticleen_US
dc.identifier.doi10.1093/gigascience/giy159


Files in this item

Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International