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Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance

BACKGROUND: RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for each step of one common workflow, differential expression analysis, which includes read alignment, expression modeling, and differentially expressed gene identification, has a dramatic impact on performance cha...

詳細記述

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書誌詳細
出版年:BMC Bioinformatics
主要な著者: Baccarella, Alyssa, Williams, Claire R., Parrish, Jay Z., Kim, Charles C.
フォーマット: Artigo
言語:Inglês
出版事項: BioMed Central 2018
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC6234607/
https://ncbi.nlm.nih.gov/pubmed/30428853
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-018-2445-2
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