publication . Article . Other literature type . Review . 2016

A survey of best practices for RNA-seq data analysis

Conesa A; Madrigal P; Tarazona S; Gomez-Cabrero D; Cervera A; McPherson A; Mw, Szcześniak; Daniel Gaffney; Ll, Elo; Zhang X; ...
English
  • Published: 26 Jan 2016
  • Publisher: BioMed Central
Abstract
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Erratum to this article has been published in Genome Biology 2016 17:181 [EN] RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experim...
Subjects
free text keywords: RNA-seq, Next-generation sequencing, transcriptomics, differential gene expression, guidelines, standards, RNA-seq analysis, ESTADISTICA E INVESTIGACION OPERATIVA, Review, DIFFERENTIAL EXPRESSION ANALYSIS, INTEGRATED ANALYSIS MMIA, DEEP SEQUENCING DATA, LARGE GENE LISTS, WEB-BASED TOOL, SINGLE-CELL, DNA-METHYLATION, PROSTATE-CANCER, SYSTEMS BIOLOGY, GENOME BROWSER, 318 Medical biotechnology, Erratum, Bioinformatics, Expression quantitative trait loci, Genetics, Genomics, Alternative splicing, Functional genomics, Computational biology, Gene expression profiling, Biology, Computational genomics, Evolutionary biology, Genome Biology, Best practice, Human genetics, Statistical genetics
Related Organizations
214 references, page 1 of 15

Mortazavi, A, Williams, BA, McCue, K, Schaeffer, L, Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008; 5: 1-8 [OpenAIRE] [PubMed] [DOI]

Levin, JZ, Yassour, M, Adiconis, X, Nusbaum, C, Thompson, DA, Friedman, N. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods. 2010; 7: 709-15 [OpenAIRE] [PubMed] [DOI]

Parkhomchuk, D, Borodina, T, Amstislavskiy, V, Banaru, M, Hallen, L, Krobitsch, S. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 2009; 37: e123 [OpenAIRE] [PubMed] [DOI]

Katz, Y, Wang, ET, Airoldi, EM, Burge, CB. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010; 7: 1009-15 [OpenAIRE] [PubMed] [DOI]

Garber, M, Grabherr, MG, Guttman, M, Trapnell, C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. 2011; 8: 469-77 [OpenAIRE] [PubMed] [DOI]

Łabaj, PP, Leparc, GG, Linggi, BE, Markillie, LM, Wiley, HS, Kreil, DP. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics. 2011; 27: i383-91 [OpenAIRE] [PubMed] [DOI]

Sims, D, Sudbery, I, Ilott, NE, Heger, A, Ponting, CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014; 15: 121-32 [OpenAIRE] [PubMed] [DOI]

Pollen, AA, Nowakowski, TJ, Shuga, J, Wang, X, Leyrat, AA, Lui, JH. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014; 32: 1053-8 [OpenAIRE] [PubMed] [DOI]

Jaitin, DA, Kenigsberg, E, Keren-Shaul, H, Elefant, N, Paul, F, Zaretsky, I. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014; 343: 776-9 [OpenAIRE] [PubMed] [DOI]

Tarazona, S, Garcia-Alcalde, F, Dopazo, J, Ferrer, A, Conesa, A. Differential expression in RNA-seq: a matter of depth. Genome Res. 2011; 21: 2213-23 [OpenAIRE] [PubMed] [DOI]

11.Andrews S. FASTQC. A quality control tool f or high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 29 September 2014.

Dai, M, Thompson, RC, Maher, C, Contreras-Galindo, R, Kaplan, MH, Markovitz, DM. NGSQC: cross-platform quality analysis pipeline for deep sequencing data. BMC Genomics. 2010; 11 (Suppl 4): S7 [OpenAIRE] [PubMed] [DOI]

13.FASTX-Toolkit. http://hannonlab.cshl.edu/fastx_toolkit/. Accessed 12 January 2016.

Bolger, AM, Lohse, M, Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30: 2114-20 [OpenAIRE] [PubMed] [DOI]

Dobin, A, Davis, CA, Schlesinger, F, Drenkow, J, Zaleski, C, Jha, S. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29: 15-21 [OpenAIRE] [PubMed] [DOI]

214 references, page 1 of 15
Abstract
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Erratum to this article has been published in Genome Biology 2016 17:181 [EN] RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experim...
Subjects
free text keywords: RNA-seq, Next-generation sequencing, transcriptomics, differential gene expression, guidelines, standards, RNA-seq analysis, ESTADISTICA E INVESTIGACION OPERATIVA, Review, DIFFERENTIAL EXPRESSION ANALYSIS, INTEGRATED ANALYSIS MMIA, DEEP SEQUENCING DATA, LARGE GENE LISTS, WEB-BASED TOOL, SINGLE-CELL, DNA-METHYLATION, PROSTATE-CANCER, SYSTEMS BIOLOGY, GENOME BROWSER, 318 Medical biotechnology, Erratum, Bioinformatics, Expression quantitative trait loci, Genetics, Genomics, Alternative splicing, Functional genomics, Computational biology, Gene expression profiling, Biology, Computational genomics, Evolutionary biology, Genome Biology, Best practice, Human genetics, Statistical genetics
Related Organizations
214 references, page 1 of 15

Mortazavi, A, Williams, BA, McCue, K, Schaeffer, L, Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008; 5: 1-8 [OpenAIRE] [PubMed] [DOI]

Levin, JZ, Yassour, M, Adiconis, X, Nusbaum, C, Thompson, DA, Friedman, N. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods. 2010; 7: 709-15 [OpenAIRE] [PubMed] [DOI]

Parkhomchuk, D, Borodina, T, Amstislavskiy, V, Banaru, M, Hallen, L, Krobitsch, S. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 2009; 37: e123 [OpenAIRE] [PubMed] [DOI]

Katz, Y, Wang, ET, Airoldi, EM, Burge, CB. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010; 7: 1009-15 [OpenAIRE] [PubMed] [DOI]

Garber, M, Grabherr, MG, Guttman, M, Trapnell, C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. 2011; 8: 469-77 [OpenAIRE] [PubMed] [DOI]

Łabaj, PP, Leparc, GG, Linggi, BE, Markillie, LM, Wiley, HS, Kreil, DP. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics. 2011; 27: i383-91 [OpenAIRE] [PubMed] [DOI]

Sims, D, Sudbery, I, Ilott, NE, Heger, A, Ponting, CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014; 15: 121-32 [OpenAIRE] [PubMed] [DOI]

Pollen, AA, Nowakowski, TJ, Shuga, J, Wang, X, Leyrat, AA, Lui, JH. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014; 32: 1053-8 [OpenAIRE] [PubMed] [DOI]

Jaitin, DA, Kenigsberg, E, Keren-Shaul, H, Elefant, N, Paul, F, Zaretsky, I. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014; 343: 776-9 [OpenAIRE] [PubMed] [DOI]

Tarazona, S, Garcia-Alcalde, F, Dopazo, J, Ferrer, A, Conesa, A. Differential expression in RNA-seq: a matter of depth. Genome Res. 2011; 21: 2213-23 [OpenAIRE] [PubMed] [DOI]

11.Andrews S. FASTQC. A quality control tool f or high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 29 September 2014.

Dai, M, Thompson, RC, Maher, C, Contreras-Galindo, R, Kaplan, MH, Markovitz, DM. NGSQC: cross-platform quality analysis pipeline for deep sequencing data. BMC Genomics. 2010; 11 (Suppl 4): S7 [OpenAIRE] [PubMed] [DOI]

13.FASTX-Toolkit. http://hannonlab.cshl.edu/fastx_toolkit/. Accessed 12 January 2016.

Bolger, AM, Lohse, M, Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30: 2114-20 [OpenAIRE] [PubMed] [DOI]

Dobin, A, Davis, CA, Schlesinger, F, Drenkow, J, Zaleski, C, Jha, S. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29: 15-21 [OpenAIRE] [PubMed] [DOI]

214 references, page 1 of 15
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publication . Article . Other literature type . Review . 2016

A survey of best practices for RNA-seq data analysis

Conesa A; Madrigal P; Tarazona S; Gomez-Cabrero D; Cervera A; McPherson A; Mw, Szcześniak; Daniel Gaffney; Ll, Elo; Zhang X; ...