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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...
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| 出版年: | BMC Bioinformatics |
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| 主要な著者: | , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
BioMed Central
2018
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| 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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