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We began analyzing https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-449, but it redirected us to https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-449. The analysis below is for the second page.

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

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

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