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Papers: 10.1186/s13059-016-0940-1

https://doi.org/10.1186/s13059-016-0940-1

A benchmark for RNA-seq quantification pipelines

Cited by: 146
Author(s): Mingxiang Teng, Michael I. Love, Carrie A. Davis, Sarah Djebali, Alexander Dobin, Brenton R. Graveley, Sheng Li, Christopher E. Mason, Sara Olson, Dmitri D. Pervouchine, Cricket A. Sloan, Xintao Wei, Lijun Zhan, Rafael A. Irizarry
Published: over 9 years ago

Software Mentions 7

bioconductor: alpine
alpine
Papers that mentioned: 2
Very Likely Science (90)
bioconductor: DESeq2
Differential gene expression analysis based on the negative binomial distribution
Papers that mentioned: 9,583
Very Likely Science (100)
bioconductor: edgeR
Empirical Analysis of Digital Gene Expression Data in R
Papers that mentioned: 6,568
Very Likely Science (100)
bioconductor: rnaseqcomp
Benchmarks for RNA-seq Quantification Pipelines
Papers that mentioned: 2
Very Likely Science (100)
cran: STAR
Spike Train Analysis with R
Papers that mentioned: 5,759
Very Likely Science (100)
pypi: alpine
Alpine Web API Client
Papers that mentioned: 2
Very Likely Science (80)
pypi: kallisto
The Kallisto software enables the efficient calculation of atomic features that can be used within a quantitative structure-activity relationship (QSAR) approach. Furthermore, several modelling helpers are implemented.
Papers that mentioned: 307
Very Likely Science (100)