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Title:
ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets | BMC Bioinformatics | Full Text
Description:
1 Background RNA sequencing is a flexible and powerful new approach for measuring gene, exon, or isoform expression. To maximize the utility of RNA sequencing data, new statistical methods are needed for clustering, differential expression, and other analyses. A major barrier to the development of new statistical methods is the lack of RNA sequencing datasets that can be easily obtained and analyzed in common statistical software packages such as R. To speed up the development process, we have created a resource of analysis-ready RNA-sequencing datasets. 2 Description ReCount is an online resource of RNA-seq gene count tables and auxilliary data. Tables were built from raw RNA sequencing data from 18 different published studies comprising 475 samples and over 8 billion reads. Using the Myrna package, reads were aligned, overlapped with gene models and tabulated into gene-by-sample count tables that are ready for statistical analysis. Count tables and phenotype data were combined into Bioconductor ExpressionSet objects for ease of analysis. ReCount also contains the Myrna manifest files and R source code used to process the samples, allowing statistical and computational scientists to consider alternative parameter values. 3 Conclusions By combining datasets from many studies and providing data that has already been processed from. fastq format into ready-to-use. RData and. txt files, ReCount facilitates analysis and methods development for RNA-seq count data. We anticipate that ReCount will also be useful for investigators who wish to consider cross-study comparisons and alternative normalization strategies for RNA-seq.
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25 years and 11 months (reg. 1999-08-06).

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Keywords {🔍}

pubmed, data, article, recount, google, scholar, central, analysis, gene, expression, cas, studies, genes, rnaseq, sequencing, normalization, count, statistical, authors, differential, tables, alternative, methods, myrna, bmc, datasets, files, differentially, expressed, bioinformatics, rna, samples, genome, reads, read, multiple, counts, figure, nature, table, montgomery, original, database, resource, rnasequencing, manifest, sample, file, technical, res,

Topics {✒️}

info/blog/2011/05/24/human-bodymap-2–0-data quantile normalization scheme analysis-ready rna-sequencing datasets open access article nih grants p41-hg004059 organized rna-sequencing datasets rna-seq count data pool-tech-reps option background rna-seq dba/2j mouse striatum flexible exploration false-discovery-rate cutoff lineage-specific alternative splicing rna-seq data requires preprocessed rna-seq data benjamini-hochberg-corrected p rna-seq data analysis paired-end sequencing data parametric paired t-test bmc bioinformatics 2010 bmc bioinformatics 12 privacy choices/manage cookies authors scientific editing processed rna-seq data full size image alternative normalization strategies identify cross-study effects rna sequencing datasets 75th percentile normalization comparing normalization schemes established normalization schemes understanding biological variation spielman rs authors’ original file 75th-percentile normalized counts rna-seq data rna sequencing data quantile normalization [28] quantile normalization count tables presented human gene expression human tissue transcriptomes false discovery rate construct count tables discussion user interface alternative parameter values biomed central desire alternative parameterizations paired t-tests provide count tables

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