- -

A survey of best practices for RNA-seq data analysis

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

A survey of best practices for RNA-seq data analysis

Show full item record

Conesa, A.; Madrigal, P.; Tarazona Campos, S.; Gómez Cabrero, D.; Cervera, A.; Mcpherson, A.; Wojciech Szczesniak, M.... (2016). A survey of best practices for RNA-seq data analysis. Genome Biology. 17(13). https://doi.org/10.1186/s13059-016-0881-8

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/80137

Files in this item

Item Metadata

Title: A survey of best practices for RNA-seq data analysis
Author: Conesa, Ana Madrigal, Pedro Tarazona Campos, Sonia Gómez Cabrero, David Cervera, Alejandra McPherson, Andrew Wojciech Szczesniak, Michal Gaffney, Daniel J. Elo, Laura L. Zhang, Xuegong Mortazavi, Ali
UPV Unit: Universitat Politècnica de València. Facultad de Administración y Dirección de Empresas - Facultat d'Administració i Direcció d'Empreses
Issued date:
Abstract:
[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 experimental design, quality ...[+]
Subjects: RNA-seq analysis
Copyrigths: Reconocimiento (by)
Source:
Genome Biology. (issn: 1474-760X )
DOI: 10.1186/s13059-016-0881-8
Publisher:
BioMed Central
Publisher version: http://dx.doi.org/10.1186/s13059-016-0881-8
Project ID: info:eu-repo/grantAgreement/EC/FP7 STATegra/36000
Description: 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
Thanks:
The authors would like to thank Michael Love and Harold Pimentel for helpful suggestions on the initial draft of the manuscript. AC, ST, AM, DGC were supported by the FP7 STATegra project (grant 36000). Research in ...[+]
Type: Artículo

References

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

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

Parkhomchuk D, Borodina T, Amstislavskiy V, Banaru M, Hallen L, Krobitsch S, et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 2009;37:e123. [+]
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:1–8.

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

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

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.

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

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.

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.

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

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

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.

Andrews S. FASTQC. A quality control tool for 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, et al. NGSQC: cross-platform quality analysis pipeline for deep sequencing data. BMC Genomics. 2010;11 Suppl 4:S7.

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.

Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

Picard. http://picard.sourceforge.net/ . Accessed 12 January 2016.

Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28:2184–5.

García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28:2678–9.

Tarazona S, Furió-Tarí P, Turrà D, Pietro AD, Nueda MJ, Ferrer A, et al. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Res. 2015;43:e140.

Risso D, Schwartz K, Sherlock G, Dudoit S. GC-content normalization for RNA-seq data. BMC Bioinformatics. 2011;12:480.

Steijger T, Abril JF, Engström PG, Kokocinski F, Hubbard TJ, Guigó R, et al. Assessment of transcript reconstruction methods for RNA-seq. Nat Methods. 2013;10:1177–84.

Boley N, Stoiber MH, Booth BW, Wan KH, Hoskins RA, Bickel PJ, et al. Genome-guided transcript assembly by integrative analysis of RNA sequence data. Nat Biotechnol. 2014;32:341–6.

Roberts A, Pimentel H, Trapnell C, Pachter L. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics. 2011;27:2325–9.

Mezlini AM, Smith EJ, Fiume M, Buske O, Savich GL, Shah S, et al. iReckon: simultaneous isoform discovery and abundance estimation from RNA-seq data. Genome Res. 2013;23:519–29.

Li JJ, Jiang CR, Brown JB, Huang H, Bickel PJ. Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation. Proc Natl Acad Sci U S A. 2011;108:19867–72.

Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290–5.

Hiller D, Wong WH. Simultaneous isoform discovery and quantification from RNA-Seq. Stat Biosci. 2013;5:100–18.

Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34:W435–9.

Engström PG, Steijger T, Sipos B, Grant GR, Kahles A, Rätsch G, et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods. 2013;10:1185–91.

Xie Y, Wu G, Tang J, Luo R, Patterson J, Liu S, et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014;30:1660–6.

Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28:1086–92.

Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.

Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novotranscript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.

Patro R, Mount SM, Kingsford C. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol. 2014;32:462–4.

Anders S, Pyl PT, Huber W. HTSeq - a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.

Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.

UCSC Genome Bioinformatics: Frequently Asked Questions: Data File Formats. https://genome.ucsc.edu/FAQ/FAQformat.html#format4 . Accessed on 12 January 2016.

Pachter L. Models for transcript quantification from RNA-seq. arXiv.org. 2011. http://arxiv.org/abs/1104.3889 . Accessed 6 January 2016.

Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5.

Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.

Roberts A, Pachter L. Streaming fragment assignment for real-time analysis of sequencing experiments. Nat Methods. 2013;10:71–3.

Bray N, Pimentel H, Melsted P, Pachter L. Near-optimal RNA-Seq quantification with kallisto. https://liorpachter.wordpress.com/2015/05/10/near-optimal-rna-seq-quantification-with-kallisto/ . Accessed 6 January 2016.

Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-seq expression estimates by correcting for fragment bias. Genome Biol. 2011;12:R22.

Ma X, Zhang X. NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data. BMC Bioinformatics. 2013;14:220.

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.

Hansen K, Brenner S, Dudoit S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 2010;38:e131.

Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.

Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.

Li J, Witten DM, Johnstone IM, Tibshirani R. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics. 2012;13:523–38.

Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2012;31:46–53.

Auer PL, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics. 2010;185:405–16.

Johnson WE, Rabinovic A, Li C. Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics. 2007;8:118–27.

Nueda MJ, Ferrer A, Conesa A. ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics. 2012;13:553–66.

Robinson MD, Smyth GK. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics. 2007;23:2881–7.

Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:R29.

Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics. 2013;14:91.

Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics. 2010;11:422.

Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BM, et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 2013;29:1035–43.

Li J, Tibshirani R. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat Methods Med Res. 2013;22:519–36.

Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26:136–8.

Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 2013;14:R95.

Seyednasrollah F, Laiho A, Elo LL. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform. 2015;16:59–70.

Zheng S, Chen L. A hierarchical Bayesian model for comparing transcriptomes at the individual transcript isoform level. Nucleic Acids Res. 2009;37:e75.

Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7:562–78.

Singh D, Orellana CF, Hu Y, Jones CD, Liu Y, Chiang DY, et al. FDM: a graph-based statistical method to detect differential transcription using RNA-seq data. Bioinformatics. 2011;27:2633–40.

Shi Y, Jiang H. rSeqDiff: detecting differential isoform expression from RNA-Seq data using hierarchical likelihood ratio test. PLoS One. 2013;8:e79448.

Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res. 2012;22:2008–17.

Wang W, Qin Z, Feng Z, Wang X, Zhang X. Identifying differentially spliced genes from two groups of RNA-seq samples. Gene. 2013;518:164–70.

Shen S, Park JW, Lu ZX, Lin L, Henry MD, Wu YN, et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc Natl Acad Sci U S A. 2014;111:E5593–601.

Drewe P, Stegle O, Hartmann L, Kahles A, Bohnert R, Wachter A, et al. Accurate detection of differential RNA processing. Nucleic Acids Res. 2013;41:5189–98.

Hu Y, Huang Y, Du Y, Orellana CF, Singh D, Johnson AR, et al. DissSplice: the genome-wide detection of differential splicing events with RNA-seq. Nucleic Acids Res. 2013;41:e39.

Hilker R, Stadermann KB, Doppmeier D, Kalinowski J, Stoye J, Straube J, et al. ReadXplorer - visualization and analysis of mapped sequences. Bioinformatics. 2014;30:2247–54.

Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The Human Genome Browser at UCSC. Genome Res. 2002;12:996–1006.

Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high- performance genomics data visualization and exploration. Brief Bioinformatics. 2012;14:178–92.

Medina I, Salavert F, Sanchez R, de Maria A, Alonso R, Escobar P, et al. Genome Maps, a new generation genome browser. Nucleic Acids Res. 2013;41(Web Server issue):W41–6.

Fiume M, Williams V, Brook A, Brudno M. Savant: genome browser for high-throughput sequencing data. Bioinformatics. 2010;26:1938–44.

Rogé X, Zhang X. RNAseqViewer: visualization tool for RNA-Seq data. Bioinformatics. 2013;30:891–2.

Katz Y, Wang ET, Silterra J, Schwartz S, Wong B, Thorvaldsdóttir H, et al. Quantitative visualization of alternative exon expression from RNA-seq data. Bioinformatics. 2015;31:2400–2.

Wu E, Nance T, Montgomery SB. SplicePlot: a utility for visualizing splicing quantitative trait loci. Bioinformatics. 2014;30:1025–6.

Ryan MC, Cleland J, Kim R, Wong WC, Weinstein JN. SpliceSeq: a resource for analysis and visualization of RNA-Seq data on alternative splicing and its functional impacts. Bioinformatics. 2012;28:2385–7.

Liu Q, Chen C, Shen E, Zhao F, Sun Z, Wu J. Detection, annotation and visualization of alternative splicing from RNA-Seq data with SplicingViewer. Genomics. 2012;99:178–82.

Dietrich S, Wiegand S, Liesegang H. TraV: a genome context sensitive transcriptome browser. PLoS One. 2014;9:e93677.

Carrara M, Beccuti M, Lazzarato F, Cavallo F, Cordero F, Donatelli S, et al. State-of-the-art fusion-finder algorithms sensitivity and specificity. BioMed Res Int. 2013;15:340620.

Maher CA, Palanisamy N, Brenner JC, Cao X, Kalyana-Sundaram S, Luo S, et al. Chimeric transcript discovery by paired-end transcriptome sequencing. Proc Natl Acad Sci U S A. 2009;106:12353–8.

Yoshihara K, Wang Q, Torres-Garcia W, Zheng S, Vegesna R, Kim H, et al. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene. 2015;34:4845–54.

McPherson A, Hormozdiari F, Zayed A, Giuliany R, Ha G, Sun MG, et al. deFuse: an algorithm for gene fusion discovery in tumor RNA-seq data. PLoS Comput Biol. 2011;7:e1001138.

Wu C, Wyatt AW, McPherson A, Lin D, McConeghy BJ, Mo F, et al. Poly-gene fusion transcripts and chromothripsis in prostate cancer. Gene Chromosomes Cancer. 2012;51:1144–53.

Wyatt AW, Mo F, Wang K, McConeghy B, Brahmbhatt S, Jong L, et al. Heterogeneity in the inter-tumor transcriptome of high risk prostate cancer. Genome Biol. 2014;15:426.

Stransky N, Cerami E, Schalm S, Kim JL, Lengauer C. The landscape of kinase fusions in cancer. Nat Commun. 2014;5:4846.

Rabbitts TH. Commonality but diversity in cancer gene fusions. Cell. 2009;137:391–5.

McPherson A, Wu C, Hajirasouliha I, Hormozdiari F, Hach F, Lapuk A, et al. Comrad: detection of expressed rearrangements by integrated analysis of RNA-Seq and low coverage genome sequence data. Bioinformatics. 2011;27:1481–8.

Iyer MK, Chinnaiyan AM, Maher CA. ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics. 2011;27:2903–4.

Pflueger D, Terry S, Sboner A, Habegger L, Esgueva R, Lin PC, et al. Discovery of non-ETS gene fusions in human prostate cancer using next-generation RNA sequencing. Genome Res. 2011;21:56–67.

Wu J, Liu Q, Wang X, Zheng J, Wang T, You M, et al. mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA Biol. 2013;10:1087–92.

Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.

Prüfer K, Stenzel U, Dannemann M, Green RE, Lachmann M, Kelso J. PatMaN: rapid alignment of short sequences to large databases. Bioinformatics. 2008;24:1530–1.

Emde AK, Grunert M, Weese D, Reinert K, Sperling SR. MicroRazerS: rapid alignment of small RNA reads. Bioinformatics. 2010;26:123–4.

An J, Lai J, Lehman ML, Nelson CC. miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res. 2013;41:727–37.

Yang X, Li L. miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics. 2011;27:2614–5.

Stocks MB, Moxon S, Mapleson D, Woolfenden HC, Mohorianu I, Folkes L, et al. The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets. Bioinformatics. 2012;28:2059–61.

Axtell MJ. ShortStack: comprehensive annotation and quantification of small RNA genes. RNA. 2013;19:740–51.

Giurato G, De Filippo MR, Rinaldi A, Hashim A, Nassa G, Ravo M, et al. iMir: an integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq. BMC Bioinformatics. 2013;14:362.

Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:1–12.

Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14:7.

Wang X, Cairns MJ. Gene set enrichment analysis of RNA-Seq data: integrating differential expression and splicing. BMC Bioinformatics. 2013;14 Suppl 5:S16.

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9.

Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12:115–21.

Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protocols. 2009;4:44–57.

Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13.

Medina I, Carbonell J, Pulido L, Madeira SC, Goetz S, Conesa A, et al. Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res. 2010;38 suppl 2:W210–3.

Bairoch A, Boeckmann B, Ferro S, Gasteiger E. Swiss-Prot: juggling between evolution and stability. Brief Bioinformatics. 2004;5:39–55.

Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. The Pfam protein families database. Nucleic Acids Res. 2014;42(Database issue):D222–30.

Hunter S, Jones P, Mitchell A, Apweiler R, Attwood TK, Bateman A, et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 2011;40(Database issue):D306–12.

Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21:3674–6.

Gardner PP, Daub J, Tate JG, Nawrocki EP, Kolbe DL, Lindgreen S, et al. Rfam: updates to the RNA families database. Nucleic Acids Res. 2009;37 suppl 1:D136–40.

Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42(Database issue):D68–73.

Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS. MicroRNA targets in Drosophila. Genome Biol. 2003;5:R1.

Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383.

Moffatt MF, Kabesch M, Liang L, Dixon AL, Strachan D, Heath S, et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature. 2007;448:470–3.

Gilad Y, Rifkin S, Pritchard J. Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet. 2008;24:408–15.

Gaffney D. Global properties and functional complexity of human gene regulatory variation. PLoS Genet. 2013;9:e1003501.

Montgomery S, Sammeth M, Gutierrez-Arcelus M, Lach RP, Ingle C, Nisbett J, et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature. 2010;464:773–7.

Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010;464:768–72.

Lappalainen T, Sammeth M, Friedlander M, ‘t Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506–11.

Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, Shi J, et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 2014;24:14–24.

Pastinen T. Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet. 2010;11:533–8.

Sun W. A statistical framework for eQTL mapping using RNA-seq data. Biometrics. 2012;68:1–11.

van de Geijn B, McVicker G, Gilad Y, Pritchard JK. WASP: allele-specific for robust molecular quantitative trait locus discovery. Nat Methods. 2015;12:1061–3.

Kumasaka N, Knights AJ, Gaffney DJ. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat Genet. 2015. doi: 10.1038/ng.3467 .

Storey JD, Tibshirani R. Statistical significance for genome-wide studies. Proc Natl Acad Sci U S A. 2003;100:9440–5.

Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, et al. Genome-wide associations of gene expression variation in humans. PLoS Genet. 2005;1:e78.

Raj T, Rothamel K, Mostafavi S, Ye C, Lee MN, Replogle JM, et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science. 2014;344:519–23.

Shabalin A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28:1353–8.

Louhimo R, Lepikhova T, Monni O, Hautaniemi S. Comparative analysis of algorithms for integration of copy number and expression data. Nat Methods. 2012;9:351–5.

Kim JH, Dhanasekaran SM, Prensner JR, Cao X, Robinson D, Kalyana-Sundaram S, et al. Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer. Genome Res. 2011;21:1028–41.

Li JL, Mazar J, Zhong C, Faulkner GJ, Govindarajan SS, Zhang Z, et al. Genome-wide methylated CpG island profiles of melanoma cells reveal a melanoma coregulation network. Sci Rep. 2013;3:2962.

Xie L, Weichel B, Ohm JE, Zhang K. An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver. BMC Syst Biol. 2011;5 Suppl 3:S4.

Van Eijk KR, de Jong S, Boks MP, Langeveld T, Colas F, Veldink JH, et al. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics. 2012;13:636.

Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31:142–7.

Yeang C-H. An integrated analysis of molecular aberrations in NCI-60 cell lines. BMC Bioinformatics. 2010;11:495.

Jeong J, Li L, Liu Y, Nephew KP, Huang YHM, Shen C. An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer. BMC Med Genomics. 2010;3:55.

Jiao Y, Widschwendter M, Teschendorff AE. A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics. 2014;30:2360–6.

Wang S, Sun H, Ma J, Zang C, Wang C, Wang J, et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat Protoc. 2013;8:2502–15.

Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30.

Madrigal P, Krajewski P. Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform. BioData Min. 2015;8:20.

Angelini C, Costa V. Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems. Front Cell Dev Biol. 2014;2:51.

Neph S, Stergachis AB, Reynolds A, Sandstrom R, Borenstein E, Stamatoyannopoulos JA. Circuitry and dynamics of human transcription factor regulatory networks. Cell. 2012;150:1274–86.

Dweep H, Sticht C, Pandey P, Gretz N. miRWalk - database: prediction of possible miRNA binding sites by ‘walking’ the genes of 3 genomes. J Biomed Inform. 2011;44:839–47.

Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008;36:D154–8.

Wu X, Watson M. CORNA: testing gene lists for regulation by microRNAs. Bioinformatics. 2009;25:832–3.

Lee H, Yang Y, Chae H, Nam S, Choi D, Tangchaisin P, et al. BioVLAB-MMIA: a cloud environment for microRNA and mRNA integrated analysis (MMIA) on Amazon EC2. IEEE Trans Nanobiosci. 2012;11:266–72.

Nam S, Li M, Choi K, Balch C, Kim S, Nephew KP. MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression. Nucleic Acids Res. 2009;37:W356–62.

Sales G, Coppe A, Bisognin A, Bortoluzzi S, Romualdi C. MAGIA, a web-based tool for miRNA and Genes Integrated Analysis. Nucleic Acids Res. 2010;38:W352–9.

Icay K, Chen P, Cervera C, Lehtonen R, Hautaniemi S. SePIA: RNA and smallRNA-sequence processing, integration, and analysis. 2015. http://anduril.org/sepia . Accessed 6 Jan 2016.

de Sousa AR, Penalva LO, Marcotte EM, Vogel C. Global signatures of protein and mRNA expression levels. Mol Biosyst. 2009;5:1512–26.

Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227–32.

Low TY, van Heesch S, van den Toorn H, Giansanti P, Cristobal A, Toonen P. Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis. Cell Rep. 2013;5:1469–78.

Suhre K, Schmitt-Kopplin P. MassTRIX: mass translator into pathways. Nucleic Acids Res. 2008;36(Web Server issue):W481–4.

García-Alcalde F, García-López F, Dopazo J, Conesa A. Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics. 2011;27:137–9.

Rohn H, Junker A, Hartmann A, Grafahrend-Belau E, Treutler H, Klapperstück M, et al. VANTED v2: a framework for systems biology applications. BMC Syst Biol. 2012;6:139.

Tuncbag N, McCallum S, Huang SS, Fraenkel E. SteinerNet: a web server for integrating ‘omic’ data to discover hidden components of response pathways. Nucleic Acids Res. 2012;40:W505–9.

Zhang S, Li Q, Liu J, Zhou XJ. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics. 2011;27:i401–9.

Le H-S, Bar-Joseph Z. Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation. Bioinformatics. 2013;29:i89–97.

Sass S, Buettner F, Mueller NS, Theis FJ. A modular framework for gene set analysis integrating multilevel omics data. Nucleic Acids Res. 2013;41:9622–33.

Kuo TC, Tian TF, Tseng YJ. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst Biol. 2013;7:64.

Ovaska K, Laakso M, Haapa-Paananen S, Louhimo R, Chen P, Aittomäki V, et al. Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. Genome Med. 2010;2:65.

Goecks J, Nekrutenko A, Taylor J. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010;11:R86.

Kallio MA, Tuimala JT, Hupponen T, Klemelä P, Gentile M, Scheinin I, et al. Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genomics. 2011;12:507.

Pang CNI, Tay AP, Aya C. Tools to covisualize and coanalyze proteomic data with genomes and transcriptomes: validation of genes and alternative mRNA splicing. J Proteome Res. 2014;13:84–98.

Ramsköld D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82.

Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096–8.

Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14.

Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014;24:496–510.

Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2014;11:163–6.

Kivioja T, Vähärautio A, Karlsson K, Bonke M, Enge M, Linnarsson S, et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods. 2011;9:72–4.

Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X, Proserpio V, et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods. 2013;10:1093–5.

Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet. 2015;16:133–45.

Trapnell C, Cacchiarelli D. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–6.

Lorthongpanich C, Cheow LF, Balu S, Quake SR, Knowles BB, Burkholder WF, et al. Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos. Science. 2013;341:1110–2.

Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523:486–90.

Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015;348:910–4.

Tilgner H, Jahanbani F, Blauwkamp T, Moshrefi A, Jaeger E, Chen F, et al. Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events. Nat Biotechnol. 2015;33:736–42.

Au KF, Sebastiano V, Afshar PT, Durruthy JD, Lee L, Williams BA, et al. Characterization of the human ESC transcriptome by hybrid sequencing. Proc Natl Acad Sci U S A. 2013;110:E4821–30.

Tilgner H, Grubert F, Sharon D, Snyder MP. Defining a personal, allele-specific, and single-molecule long-read transcriptome. Proc Natl Acad Sci U S A. 2014;111:9869–74.

Au KF, Underwood JG, Lee L, Wong WH. Improving PacBio long read accuracy by short read alignment. PLoS One. 2012;7:e46679.

Hansen KD, Wu Z, Irizarry RA, Leek JT. Sequencing technology does not eliminate biological variability. Nat Biotechnol. 2011;29:572–3.

Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher JP. Calculating sample size estimates for RNA sequencing data. J Comput Biol. 2013;20:970–8.

Busby MA, Stewart C, Miller CA, Grzeda KR, Marth GT. Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics. 2013;29:656–7.

Oshlack A, Wakefield MJ. Transcript length bias in RNA-seq data confounds systems biology. Biol Direct. 2009;4:14.

Noble WS. How does multiple testing correction work? Nat Biotechnol. 2009;27:1135–7.

Robinson DG, Storey JD. subSeq: determining appropriate sequencing depth through efficient read subsampling. Bioinformatics. 2014;30:3424–6.

Liu Y, Zhou J, White KP. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics. 2013;30:301–4.

SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat Biotechnol. 2014;32:903–14.

Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 2011;21:1543–51.

Kouzine F, Wojtowicz D, Yamane A, Resch W, Kieffer-Kwon KR, Bandle R, et al. Global regulation of promoter melting in naive lymphocytes. Cell. 2013;153:988–99.

Van Dijk EL, Jaszczyszyn Y, Thermes C. Library preparation methods for next-generation sequencing: tone down the bias. Exp Cell Res. 2014;322:12–20.

Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.

Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:R36.

Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010;26:873–81.

Jean G, Kahles A, Sreedharan VT, De Bona F, Rätsch G. RNA-Seq read alignments with PALMapper. Curr Protoc Bioinformatics. 2010;11(6).

Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 2010;38:e178.

Marco-Sola S, Sammeth M, Guigó R, Ribeca P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat Methods. 2012;9:1185–8.

Zhao S, Zhang B. A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification. BMC Genomics. 2015;16:97.

Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.

Kvam VM, Liu P, Si Y. A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot. 2012;99:248–56.

Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM. Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics. 2012;13:484.

Nookaew I, Papini M, Pornputtapong N, Scalcinati G, Fagerberg L, Uhlén M, et al. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res. 2012;40:10084–97.

Seyednasrollah F, Rantanen K, Jaakkola P, Elo LL. ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer. Nucleic Acids Res. 2016;44(1):e1. doi: 10.1093/nar/gkv806 .

Bi Y, Davuluri RV. NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data. BMC Bioinformatics. 2013;14:262.

Nueda MJ, Tarazona S, Conesa A. Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics. 2014;30:2598–602.

GTEx Consortium. The Genotype-Tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.

[-]

This item appears in the following Collection(s)

Show full item record