Browsing by Author "Love, Michael I."

Now showing items 1-5 of 5

    • A benchmark for RNA-seq quantification pipelines 

      Teng, Mingxiang; Love, Michael I.; Davis, Carrie A.; Djebali, Sarah; Dobin, Alexander; Graveley, Brenton R.; Li, Sheng; Mason, Christopher E.; Olson, Sara; Pervouchine, Dmitri; Sloan, Cricket A.; Wei, Xintao; Zhan, Lijun; Irizarry, Rafael A. (BioMed Central, 2016)
      Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, ...

    • Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences 

      Soneson, Charlotte; Love, Michael I.; Robinson, Mark D. (F1000Research, 2015)
      High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between ...

    • MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens 

      Li, Wei; Xu, Han; Xiao, Tengfei; Cong, Le; Love, Michael I; Zhang, Feng; Irizarry, Rafael A; Liu, Jun S; Brown, Myles; Liu, X Shirley (BioMed Central, 2014)
      We propose the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK demonstrates better ...

    • Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 

      Love, Michael I; Huber, Wolfgang; Anders, Simon (BioMed Central, 2014)
      In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate ...

    • RNA-Seq workflow: gene-level exploratory analysis and differential expression 

      Love, Michael I.; Anders, Simon; Kim, Vladislav; Huber, Wolfgang (F1000Research, 2015)
      Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a ...