- Open Access
ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets
© Frazee et al; licensee BioMed Central Ltd. 2011
Received: 18 August 2011
Accepted: 16 November 2011
Published: 16 November 2011
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.
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.
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.
RNA-seq, or short-read sequencing of mRNA, has emerged as a powerful and flexible tool for studying gene expression . As with other new technologies, the analysis of RNA-seq data requires the development of new statistical methods. Data from many RNA-seq experiments are publicly available, but processing raw data into a form suitable for statistical analysis remains challenging . This difficulty together with the high cost of using second-generation sequencing technology means that most computational scientists have only a limited number of samples to work with . However, replication is critical to understanding biological variation in RNA-sequencing .
The Gene Expression Omnibus  is a useful repository that contains both processed and raw microarray data, but there is no comparable resource for processed RNA-seq data. We have compiled a resource, called ReCount, consisting of aligned, preprocessed RNA-seq data from 475 samples in 18 different experiments. Our database makes it easier for statistical and bioinformatics researchers to analyze RNA-seq count data using standard tools such as R, Bioconductor , and MATLAB. The aligned and preprocessed data in ReCount can be directly analyzed, used to develop and compare new methods for analysis, or examined to identify cross-study effects. The ReCount database 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.
Construction and Content
Datasets available for download (truncated to 35 bp)
Number of bio reps
Number of reads
modENCODE - worm
modENCODE - fly
To construct count tables, we used the Amazon Elastic MapReduce version of Myrna 1.1.2 . As input, Myrna requires a manifest file listing URL locations for all sequencing read files for each sample. Myrna manifest files are available as part of ReCount; most URLs in these files refer to reads stored in the NCBI Sequence Read Archive (SRA) .
For studies consisting of paired-end sequencing data, only the first mate of each pair was considered. Many studies also included technical replicates, which were processed using Myrna's pool-tech-reps option. This option pools the reads from technical replicates prior to alignment and analysis. Other options passed to Myrna were bowtie-args = "-v 2 -m 1", gene-footprint = intersect, and from-middle. The gene-footprint = intersect parameter causes a "union intersection" gene model to be used. The bowtie-args parameters specify that no more than two mismatches are allowed for a read alignment to be valid and that reads with multiple alignments are discarded. The from-middle argument designates that the number of bases considered when overlapping a read's alignment with a gene footprint should be measured from the middle of the read (rather than the 3' or 5' end). Finally, we provide count tables and ExpressionSets created using Myrna's truncate = 35 option, which truncates reads longer than 35 bp to 35 bp. For using data from multiple studies at once, the truncation makes studies more comparable to each other; it also decreases the likelihood that a read will span a splice junction and therefore be discarded. However, for researchers who wish to utilize the full read length, we also provide count tables and ExpressionSets created without the truncate option.
Count tables presented in ReCount have not yet been normalized. During analysis, gene counts in each sample are commonly normalized by dividing by the 75th percentile of the distribution of non-zero gene counts in the sample, as suggested previously , but the data presented in ReCount allows researchers to develop, evaluate, and compare alternative normalization schemes.
Utility and Discussion
The ReCount website features an interactive version of Table 1. ExpressionSets, count tables, and phenotype tables are downloadable from the table. Manifest files used with Myrna, specific Myrna commands used, and R code used to create ExpressionSets are also available for download at the ReCount website. These scripts allow researchers to compare the effects of alternative normalization or alternative Myrna parameterization to the canonical versions of the datasets contained in the ReCount database. The site also contains further details about the contents of the downloadables as well as a set of R commands that may be useful when working with ExpressionSets.
Utility: Example Applications
ReCount facilitates studies that are not possible using only a small number of samples from a single study. Here we present two toy examples that illustrate the potential utility of the ReCount database. Both examples used datasets created with reads truncated to 35 bp.
Application 1: Comparison of normalization methods
Count tables presented in ReCount have not yet been normalized, which facilitates comparisons between normalization and preprocessing approaches. As an example, we compared 75th percentile normalization  with quantile normalization  using data collected on two different strains of mice (the bottomly dataset available in ReCount ). We analyzed 36,536 total genes, first removing genes with zero counts or that showed no variation across samples (23,697 genes). For both types of normalization, each gene was tested for differential expression between the two strains using an F-test. There were 696 genes that were differentially expressed at a false discovery rate of 5% (a Benjamini-Hochberg correction for multiple testing  was used) in both analyses, while 177 were only differentially expressed using quantile normalization and 35 were only differentially expressed using 75th percentile normalization. The set of differentially expressed genes for the quantile normalization scheme is a bit larger than the set for the 75th percentile normalization, but the overlap is still quite large. This simple analysis demonstrates a method for comparing normalization schemes; it also illustrates that results of a differential expression analysis differ very little based on which of these two well-established normalization schemes was used.
Application 2: Analysis using data from multiple studies
ReCount's preproccessed, freely-available data compatible with common statistical software will encourage statisticians interested in methods development to tackle problems arising in RNA-seq data analysis. By providing a large amount of RNA-seq data in a central, accessible location, ReCount facilitates analyses like those above and several others. For example, another interesting application may be to attempt to replicate findings from other studies (e.g., percentage of differentially expressed genes). Additionally, all commands and manifest files used in preprocessing are available on the website, so users can create their own count tables should they desire alternative parameterizations: e.g., alternative alignment parameters can be passed to Bowtie, the truncation length can be changed, or the pool-tech-reps option can be removed.
ReCount addresses two key issues for statistical researchers interested in RNA sequencing: (1) small sample sizes in many available studies and (2) computational difficulties in developing analysis-ready RNA-sequencing data. By providing Myrna manifest files and R scripts that reproduce the count tables in ReCount, our database also allows for flexible exploration of a large number of organized RNA-sequencing datasets. We anticipate that ReCount will be useful to both the statistical and bioinformatics community as a resource for readily analyzable RNA-sequencing data.
Availability and Requirements
ReCount is publicly accessible at http://bowtie-bio.sf.net/recount.
Acknowledgements and Funding
The authors would like to acknowledge discussions with Kasper Hansen and Rafael Irizarry. Support for this research was provided by an Amazon Web Services in Education Research Grant and a Bloomberg JHSPH Faculty Innovation Award to JL. BL was supported by NIH Grants P41-HG004059 and R01-HG005220. AF was supported by NIH Grant T32GM074906.
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10: 57–63. 10.1038/nrg2484PubMed CentralView ArticlePubMedGoogle Scholar
- Stein LD: The case for cloud computing in genome informatics. Genome Biol 2010, 11: 207. 10.1186/gb-2010-11-5-207PubMed CentralView ArticlePubMedGoogle Scholar
- Hansen KD, Wu Z, Irizarry RA, Leek JT: Sequencing technology does not eliminate biological variability. Nat Biotechnol 2011, 29(7):572–573. 10.1038/nbt.1910PubMed CentralView ArticlePubMedGoogle Scholar
- Auer PL, Doerge RW: Statistical design and analysis of RNA sequencing data. Genetics 2010, 185: 405–416. 10.1534/genetics.110.114983PubMed CentralView ArticlePubMedGoogle Scholar
- Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Holko M, Ayanbule O, Yefanov A, Sobolera A: NCBI GEO: archive for functional genomics data sets - 10 years on. Nucleic Acids Res 2011, 39(suppl 1):D1005-D1010.PubMed CentralView ArticlePubMedGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY, Zhang J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004, 5(10):R80. 10.1186/gb-2004-5-10-r80PubMed CentralView ArticlePubMedGoogle Scholar
- Human BodyMap 2.0 data from Illumina2011. [http://www.ensembl.info/blog/2011/05/24/human-bodymap-2–0-data-from-illumina/]
- Cheung VG, Nayak RR, Wang IX, Elwyn S, Cousins SM, Morley M, Spielman RS: Polymorphic cis- and trans-regulation of human gene expression. PLoS Biol 2010, 8(9):e1000480. 10.1371/journal.pbio.1000480PubMed CentralView ArticlePubMedGoogle Scholar
- Core LJ, Waterfall JJ, Lis JT: Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 2008, 322(5909):1845–1848. 10.1126/science.1162228PubMed CentralView ArticlePubMedGoogle Scholar
- Blekhman R, Marioni JC, Zumbo P, Gilad Y: Sex-specific and lineage-specific alternative splicing in primates. Genome Res 2010, 20(2):180–189. 10.1101/gr.099226.109PubMed CentralView ArticlePubMedGoogle Scholar
- Bullard JH, Purdom E, Hansen KD, Dudoit S: Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC bioinformatics 2010, 11: 94. 10.1186/1471-2105-11-94PubMed CentralView ArticlePubMedGoogle Scholar
- Montgomery SB, Sammeth M, Gutierrez-Arcelus M, Lach RP, Ingle C, Nisbett J, Guigo R, Dermitzakis ET: Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 2010, 464(7289):773–777. 10.1038/nature08903View ArticlePubMedGoogle Scholar
- Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, Veyrieras JB, Stephens M, Gilad Y, Pritchard JK: Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 2010, 464(7289):768–772. 10.1038/nature08872PubMed CentralView ArticlePubMedGoogle Scholar
- Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, Schmidt D, O'Keefe S, Haas S, Vingron M, Lehrach H, Yaspo ML: A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008, 321(5891):956–960. 10.1126/science.1160342View ArticlePubMedGoogle Scholar
- Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB: Alternative isoform regulation in human tissue transcriptomes. Nature 2008, 456(7221):470–476. 10.1038/nature07509PubMed CentralView ArticlePubMedGoogle Scholar
- Katz Y, Wang ET, Airoldi EM, Burge CB: Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods 2010, 7(12):1009–1015. 10.1038/nmeth.1528PubMed CentralView ArticlePubMedGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008, 5(7):621–628. 10.1038/nmeth.1226View ArticlePubMedGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010, 28(5):511–515. 10.1038/nbt.1621PubMed CentralView ArticlePubMedGoogle Scholar
- Yang F, Babak T, Shendure J, Disteche CM: Global survey of escape from X inactivation by RNA-sequencing in mouse. Genome Res 2010, 20(5):614–622. 10.1101/gr.103200.109PubMed CentralView ArticlePubMedGoogle Scholar
- Bottomly P, Walter NA, Hunter JE, Darakijian P, Kawane S, Buck KS, Searles RP, Mooney M, McWeeney SK, Hitzemann R: Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays. PLoS One 2011, 6(3):e17820. 10.1371/journal.pone.0017820PubMed CentralView ArticlePubMedGoogle Scholar
- Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M: The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008, 320(5881):1344–1349. 10.1126/science.1158441PubMed CentralView ArticlePubMedGoogle Scholar
- Hammer P, Banck MS, Amberg R, Wang C, Petznick G, Luo S, Khrebtukova I, Schroth GP, Beyerlein P, Beutler AS: mRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain. Genome Res 2010, 20(6):847–860. 10.1101/gr.101204.109PubMed CentralView ArticlePubMedGoogle Scholar
- Hillier LW, Reinke V, Green P, Hirst M, Marra MA, Waterston RH: Massively parallel sequencing of the polyadenylated transcriptome of C. elegans. Genome Res 2009, 19(4):657–666. 10.1101/gr.088112.108PubMed CentralView ArticlePubMedGoogle Scholar
- Graveley BR, Brooks AN, Carlson JW, Duff MO, Landolin JM, Yang L, Artieri CG, van Baren MJ, Boley N, Booth BW, Brown JB, Cherbas L, Davis CA, Dobin A, Li R, Lin W, Malone JH, Mattiuzzo NR, Miller D, Sturgill D, Tuch BB, Zaleski C, Zhang D, Blanchette , Dudoit S, Eads B, Green RE, Hammonds A, Jiang L, Kapranov P, Langton L, Perrimon N, Sandler JE, Wan KE, Willingham A, Zhang Y, Zou Y, Andrews J, Bickel PJ, Brenner SE, Brent MR, Cherbas P, Ginergas TR, Hoskins RA, Kaufman TC, Oliver B, Celniker SE: The developmental transcriptome of Drosophila melanogaster. Nature 2011, 471(7339):473–479. 10.1038/nature09715PubMed CentralView ArticlePubMedGoogle Scholar
- Flicek P, Amode MR, Barrell D, Beal K, Brent S, Chen Y, Clapham P, Coates G, Fairley S, Fitzgerald S, Gordon L, Hendrix M, Hourlier T, Johnson N, Kahari A, Keefe D, Keenan S, Kinsella R, Kokocinski F, Kulesha E, Larsson P, Longden I, McLaren W, Overduin B, Pritchard B, Riat HS, Rios D, Ritchie GR, Ruffier M, Schuster M, Sobral D, Spudich D, Tang YA, Trevanion S, Vandrovcova J, Vilella AJ, White S, Wilder SP, Zadissa A, Zamora J, Aken BL, Birney E, Cunningham F, Dunham I, Durbin R, Fernandez-Suarez XM, Herrero J, Hubbard TJ, Parker A, Proctor G, Vogel J, Searle SM: Ensembl 2011. Nucleic Acids Res 2011, 39(suppl 1):D800.PubMed CentralView ArticlePubMedGoogle Scholar
- Langmead B, Hansen KD, Leek JT: Cloud-scale RNA-sequencing differential expression analysis with Myrna. Genome Biol 2010, 11(8):R83. 10.1186/gb-2010-11-8-r83PubMed CentralView ArticlePubMedGoogle Scholar
- Leinonen R, Sugawara H, Shumway M: The sequence read archive. Nucleic Acids Res 2011, 39(suppl 1):D19-D21.PubMed CentralView ArticlePubMedGoogle Scholar
- Bolstad B, Irizarry R, Åstrand M, Speed T: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19(2):185–193. 10.1093/bioinformatics/19.2.185View ArticlePubMedGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 1995, 289–300.Google Scholar
- Storey JD, Madeoy J, Strout JL, Wurfel M, Ronald J, Akey JM: Gene-expression variation within and among human populations. Am J Hum Genet 2007, 80(3):502–509. 10.1086/512017PubMed CentralView ArticlePubMedGoogle Scholar
- Spielman RS, Bastone LA, Burdick JT, Morley M, Ewens WJ, Cheung VG: Common genetic variants account for differences in gene expression among ethnic groups. Nat Genet 2007, 39(2):226–231. 10.1038/ng1955PubMed CentralView ArticlePubMedGoogle Scholar
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