Results 1 to 10 of about 345,547 (215)
Statistical Modeling of RNA-Seq Data [PDF]
Statistical Science, 2011 Recently, ultra high-throughput sequencing of RNA (RNA-Seq) has been developed as an approach for analysis of gene expression. By obtaining tens or even hundreds of millions of reads of transcribed sequences, an RNA-Seq experiment can offer a comprehensive survey of the population of genes (transcripts) in any sample of interest.
Julia Salzman, Hui Jiang, Hui Jiang
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RNA-seq: technical variability and sampling [PDF]
BMC Genomics, 2011 RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences ...
Lauren M. McIntyre +6 more
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SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data [PDF]
Genome Biology, 2019 Single-cell RNA-seq data contain a large proportion of zeros for expressed genes. Such dropout events present a fundamental challenge for various types of data analyses. Here, we describe the SCRABBLE algorithm to address this problem. SCRABBLE leverages
Tao Peng, Qin Zhu, Penghang Yin, Kai Tan
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RNA-Seq: a revolutionary tool for transcriptomics [PDF]
Nature Reviews Genetics, 2009 (Uploaded by Plazi for the Bat Literature Project) RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes.
Zhong Wang +2 more
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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome [PDF]
BMC Bioinformatics, 2011 Bo Li, Colin N Dewey, Pierre Barr
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RNA-Seq: revelation of the messengers [PDF]
Trends in Plant Science, 2013 Next-generation RNA-sequencing (RNA-Seq) is rapidly outcompeting microarrays as the technology of choice for whole-transcriptome studies. However, the bioinformatics skills required for RNA-Seq data analysis often pose a significant hurdle for many biologists.
Van Verk +6 more
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IET Systems Biology, 2023 Hepatocellular carcinoma (HCC) remains a worldwide health problem. Mounting evidence indicates that exhausted T cells play a critical role in the progress and treatment of HCC.
Xiaolong Tang +5 more
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Cancer Medicine, 2023 Background Progestins are used as fertility‐sparing regimens for young patients with stage 1A endometrioid endometrial cancer (EEC) and atypical endometrial hyperplasia (AEH).
Lulu Wang +6 more
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Big Data Analytics in RNA-sequencing
Korean Journal of Clinical Laboratory Science, 2023 As next-generation sequencing has been developed and used widely, RNA-sequencing (RNA-seq) has rapidly emerged as the first choice of tools to validate global transcriptome profiling.
Sung-Hun WOO, Byung Chul JUNG
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Bioinformatics, 2011 Abstract Summary: RNA-Seq is an exciting methodology that leverages the power of high-throughput sequencing to measure RNA transcript counts at an unprecedented accuracy. However, the data generated from this process are extremely large and biologist-friendly tools with which to analyze it are sorely lacking. MultiExperiment Viewer (MeV)
Daniel Schlauch +3 more
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