Papers: 10.1371/journal.pone.0031630
https://doi.org/10.1371/journal.pone.0031630
Optimizing a Massive Parallel Sequencing Workflow for Quantitative miRNA Expression Analysis
Cited by: 33
Author(s): Francesca Cordero, Marco Beccuti, Maddalena Arigoni, Susanna Donatelli, Raffaele Calogero
Published: over 13 years ago
Software Mentions 8
bioconductor: baySeq
Empirical Bayesian analysis of patterns of differential expression in count dataPapers that mentioned: 139
Very Likely Science (100)
bioconductor: ChIPpeakAnno
Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome rangesPapers that mentioned: 145
Very Likely Science (76)
bioconductor: DEGseq
Identify Differentially Expressed Genes from RNA-seq dataPapers that mentioned: 697
Very Likely Science (100)
bioconductor: edgeR
Empirical Analysis of Digital Gene Expression Data in RPapers that mentioned: 6,568
Very Likely Science (100)
bioconductor: RankProd
Rank Product method for identifying differentially expressed genes with application in meta-analysisPapers that mentioned: 213
Very Likely Science (100)
Very Likely Science (90)
Likely Science (50)
Very Likely Science (85)