W
Wei Shi
Researcher at National Institutes of Health
Publications - 224
Citations - 62350
Wei Shi is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Antibody & Medicine. The author has an hindex of 59, co-authored 179 publications receiving 40542 citations. Previous affiliations of Wei Shi include University of Melbourne & La Trobe University.
Papers
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Journal ArticleDOI
limma powers differential expression analyses for RNA-sequencing and microarray studies
Matthew E. Ritchie,Belinda Phipson,Di Wu,Yifang Hu,Charity W. Law,Wei Shi,Gordon K. Smyth,Gordon K. Smyth +7 more
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features
TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
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voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Charity W. Law,Charity W. Law,Yunshun Chen,Yunshun Chen,Wei Shi,Wei Shi,Gordon K. Smyth,Gordon K. Smyth +7 more
TL;DR: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments, and the voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline.
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The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote
TL;DR: This article proposes an elegantly simple multi-seed strategy, called seed-and-vote, for mapping reads to a reference genome, which uses a relatively large number of short seeds extracted from each read and allows all the seeds to vote on the optimal location.
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The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads.
TL;DR: Rsubread is presented, a Bioconductor software package that provides high-performance alignment and read counting functions for RNA-seq reads that integrates read mapping and quantification in a single package and has no software dependencies other than R itself.