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When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data

Considerable advances in genomics over the past decade have resulted in vast amounts of data being generated and deposited in global archives. The growth of these archives exceeds our ability to process their content, leading to significant analysis bottlenecks. Sketching algorithms produce small, a...

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Detalles Bibliográficos
Autor principal: Rowe, Will P. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744645/
https://www.ncbi.nlm.nih.gov/pubmed/31519212
http://dx.doi.org/10.1186/s13059-019-1809-x
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author Rowe, Will P. M.
author_facet Rowe, Will P. M.
author_sort Rowe, Will P. M.
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description Considerable advances in genomics over the past decade have resulted in vast amounts of data being generated and deposited in global archives. The growth of these archives exceeds our ability to process their content, leading to significant analysis bottlenecks. Sketching algorithms produce small, approximate summaries of data and have shown great utility in tackling this flood of genomic data, while using minimal compute resources. This article reviews the current state of the field, focusing on how the algorithms work and how genomicists can utilize them effectively. References to interactive workbooks for explaining concepts and demonstrating workflows are included at https://github.com/will-rowe/genome-sketching.
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spelling pubmed-67446452019-09-18 When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data Rowe, Will P. M. Genome Biol Review Considerable advances in genomics over the past decade have resulted in vast amounts of data being generated and deposited in global archives. The growth of these archives exceeds our ability to process their content, leading to significant analysis bottlenecks. Sketching algorithms produce small, approximate summaries of data and have shown great utility in tackling this flood of genomic data, while using minimal compute resources. This article reviews the current state of the field, focusing on how the algorithms work and how genomicists can utilize them effectively. References to interactive workbooks for explaining concepts and demonstrating workflows are included at https://github.com/will-rowe/genome-sketching. BioMed Central 2019-09-13 /pmc/articles/PMC6744645/ /pubmed/31519212 http://dx.doi.org/10.1186/s13059-019-1809-x Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Review
Rowe, Will P. M.
When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data
title When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data
title_full When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data
title_fullStr When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data
title_full_unstemmed When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data
title_short When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data
title_sort when the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744645/
https://www.ncbi.nlm.nih.gov/pubmed/31519212
http://dx.doi.org/10.1186/s13059-019-1809-x
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