ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

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 n...
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Autor*in:	Frazee, Alyssa C [verfasserIn] Langmead, Ben Leek, Jeffrey T

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Normalization Scheme Quantile Normalization Alternative Normalization Flexible Exploration Count Table

Anmerkung:	© Frazee et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 12(2011), 1 vom: 16. Nov.
Übergeordnetes Werk:	volume:12 ; year:2011 ; number:1 ; day:16 ; month:11

Links:	Volltext

DOI / URN:	10.1186/1471-2105-12-449

Katalog-ID:	SPR026872218

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allfields	10.1186/1471-2105-12-449 doi (DE-627)SPR026872218 (SPR)1471-2105-12-449-e DE-627 ger DE-627 rakwb eng Frazee, Alyssa C verfasserin aut ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Frazee et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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. Normalization Scheme (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Alternative Normalization (dpeaa)DE-He213 Flexible Exploration (dpeaa)DE-He213 Count Table (dpeaa)DE-He213 Langmead, Ben aut Leek, Jeffrey T aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-12-449 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1 16 11
spelling	10.1186/1471-2105-12-449 doi (DE-627)SPR026872218 (SPR)1471-2105-12-449-e DE-627 ger DE-627 rakwb eng Frazee, Alyssa C verfasserin aut ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Frazee et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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. Normalization Scheme (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Alternative Normalization (dpeaa)DE-He213 Flexible Exploration (dpeaa)DE-He213 Count Table (dpeaa)DE-He213 Langmead, Ben aut Leek, Jeffrey T aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-12-449 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1 16 11
allfields_unstemmed	10.1186/1471-2105-12-449 doi (DE-627)SPR026872218 (SPR)1471-2105-12-449-e DE-627 ger DE-627 rakwb eng Frazee, Alyssa C verfasserin aut ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Frazee et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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. Normalization Scheme (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Alternative Normalization (dpeaa)DE-He213 Flexible Exploration (dpeaa)DE-He213 Count Table (dpeaa)DE-He213 Langmead, Ben aut Leek, Jeffrey T aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-12-449 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1 16 11
allfieldsGer	10.1186/1471-2105-12-449 doi (DE-627)SPR026872218 (SPR)1471-2105-12-449-e DE-627 ger DE-627 rakwb eng Frazee, Alyssa C verfasserin aut ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Frazee et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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. Normalization Scheme (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Alternative Normalization (dpeaa)DE-He213 Flexible Exploration (dpeaa)DE-He213 Count Table (dpeaa)DE-He213 Langmead, Ben aut Leek, Jeffrey T aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-12-449 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1 16 11
allfieldsSound	10.1186/1471-2105-12-449 doi (DE-627)SPR026872218 (SPR)1471-2105-12-449-e DE-627 ger DE-627 rakwb eng Frazee, Alyssa C verfasserin aut ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Frazee et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( 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. Normalization Scheme (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Alternative Normalization (dpeaa)DE-He213 Flexible Exploration (dpeaa)DE-He213 Count Table (dpeaa)DE-He213 Langmead, Ben aut Leek, Jeffrey T aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-12-449 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1 16 11
language	English
source	Enthalten in BMC bioinformatics 12(2011), 1 vom: 16. Nov. volume:12 year:2011 number:1 day:16 month:11
sourceStr	Enthalten in BMC bioinformatics 12(2011), 1 vom: 16. Nov. volume:12 year:2011 number:1 day:16 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Normalization Scheme Quantile Normalization Alternative Normalization Flexible Exploration Count Table
isfreeaccess_bool	false
container_title	BMC bioinformatics
authorswithroles_txt_mv	Frazee, Alyssa C @@aut@@ Langmead, Ben @@aut@@ Leek, Jeffrey T @@aut@@
publishDateDaySort_date	2011-11-16T00:00:00Z
hierarchy_top_id	326644814
id	SPR026872218
language_de	englisch
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topic_title	ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets Normalization Scheme (dpeaa)DE-He213 Quantile Normalization (dpeaa)DE-He213 Alternative Normalization (dpeaa)DE-He213 Flexible Exploration (dpeaa)DE-He213 Count Table (dpeaa)DE-He213
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title	ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets
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title_sort	recount: a multi-experiment resource of analysis-ready rna-seq gene count datasets
title_auth	ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets
abstract	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. © Frazee et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	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. © Frazee et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	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. © Frazee et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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score	7.4007797

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ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

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