Journal ArticleDOI
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
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TLDR
DGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples is presented, integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference.Abstract:
Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference. Availability: The R package and a quick-start vignette is available at http://bioinfo.au.tsinghua.edu.cn/software/degseq Contact: xwwang@tsinghua.edu.cn; zhangxg@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.read more
Citations
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Journal ArticleDOI
Differential expression analysis for sequence count data.
Simon Anders,Wolfgang Huber +1 more
TL;DR: A method based on the negative binomial distribution, with variance and mean linked by local regression, is proposed and an implementation, DESeq, as an R/Bioconductor package is presented.
Journal ArticleDOI
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
Cole Trapnell,Adam Roberts,Loyal A. Goff,Loyal A. Goff,Loyal A. Goff,Geo Pertea,Daehwan Kim,Daehwan Kim,David R. Kelley,David R. Kelley,Harold Pimentel,Steven L. Salzberg,John L. Rinn,John L. Rinn,Lior Pachter +14 more
TL;DR: This protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results, which takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
Journal ArticleDOI
Differential analysis of gene regulation at transcript resolution with RNA-seq
Cole Trapnell,David G. Hendrickson,David G. Hendrickson,Martin Sauvageau,Martin Sauvageau,Loyal A. Goff,Loyal A. Goff,John L. Rinn,John L. Rinn,Lior Pachter +9 more
TL;DR: Cuffdiff 2, an algorithm that estimates expression at transcript-level resolution and controls for variability evident across replicate libraries, robustly identifies differentially expressed transcripts and genes and reveals differential splicing and promoter-preference changes.
Journal ArticleDOI
A survey of best practices for RNA-seq data analysis
Ana Conesa,Pedro Madrigal,Pedro Madrigal,Sonia Tarazona,David Gomez-Cabrero,Alejandra Cervera,Andrew McPherson,Michał Wojciech Szcześniak,Daniel J. Gaffney,Laura L. Elo,Xuegong Zhang,Ali Mortazavi +11 more
TL;DR: All of the major steps in RNA-seq data analysis are reviewed, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.
Journal ArticleDOI
Circulating Tumor Cell Clusters Are Oligoclonal Precursors of Breast Cancer Metastasis
Nicola Aceto,Aditya Bardia,David T. Miyamoto,Maria C. Donaldson,Ben S. Wittner,Joel A. Spencer,Min Yu,Adam Pely,Amanda Engstrom,Huili Zhu,Brian W. Brannigan,Ravi Kapur,Shannon L. Stott,Toshi Shioda,Sridhar Ramaswamy,David T. Ting,Charles P. Lin,Mehmet Toner,Daniel A. Haber,Daniel A. Haber,Shyamala Maheswaran +20 more
TL;DR: Using mouse models with tagged mammary tumors, it is demonstrated that CTC clusters arise from oligoclonal tumor cell groupings and not from intravascular aggregation events, and though rare in the circulation, they greatly contribute to the metastatic spread of cancer.
References
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