Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57–63.
Article
CAS
PubMed
PubMed Central
Google Scholar
Lister R, O'Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, et al. Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis. Cell. 2008;133(3):523–36.
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–8.
Article
CAS
PubMed
Google Scholar
Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science (New York, NY). 2008;320(5881):1344–9.
Sboner A, Mu XJ, Greenbaum D, Auerbach RK, Gerstein MB. The real cost of sequencing: higher than you think! Genome Biol. 2011;12(8):1–10.
Article
Google Scholar
Yu Y, Fuscoe JC, Zhao C, Guo C, Jia M, Qing T, et al. A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages. Nat Commun. 2014;5:3230.
Jiang Y, Xie M, Chen W, Talbot R, Maddox JF, Faraut T, et al. The sheep genome illuminates biology of the rumen and lipid metabolism. Science (New York, NY). 2014;344(6188):1168–73.
Sekhon RS, Briskine R, Hirsch CN, Myers CL, Springer NM, Buell CR, et al. Maize Gene Atlas Developed by RNA Sequencing and Comparative Evaluation of Transcriptomes Based on RNA Sequencing and Microarrays. PLoS One. 2013;8(4):e61005.
Stelpflug SC, Sekhon RS, Vaillancourt B, Hirsch CN, Buell CR, de Leon N, Kaeppler SM. An Expanded Maize Gene Expression Atlas based on RNA Sequencing and its Use to Explore Root Development. Plant Genome 2016, 9(1).
Severin AJ, Woody JL, Bolon Y-T, Joseph B, Diers BW, Farmer AD, et al. RNA-Seq Atlas of Glycine max: A guide to the soybean transcriptome. BMC Plant Biol. 2010;10(1):1–16.
O’Rourke JA, Iniguez LP, Fu F, Bucciarelli B, Miller SS, Jackson SA, et al. An RNA-Seq based gene expression atlas of the common bean. BMC Genomics. 2014;15(1):1–17.
Alves-Carvalho S, Aubert G, Carrère S, Cruaud C, Brochot A-L, Jacquin F, et al. Full-length de novo assembly of RNA-seq data in pea (Pisum sativum L.) provides a gene expression atlas and gives insights into root nodulation in this species. Plant J. 2015;84(1):1–19.
The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489(7414):57–74.
Article
PubMed Central
Google Scholar
Muyal JP, Muyal V, Kaistha BP, Seifart C, Fehrenbach H. Systematic comparison of RNA extraction techniques from frozen and fresh lung tissues: checkpoint towards gene expression studies. Diagn Pathol. 2009;4:9.
Article
PubMed
PubMed Central
Google Scholar
Sultan M, Amstislavskiy V, Risch T, Schuette M, Dökel S, Ralser M, et al. Influence of RNA extraction methods and library selection schemes on RNA-seq data. BMC Genomics. 2014;15(1):1–13.
O'Neil D, Glowatz H, Schlumpberger M. Ribosomal RNA depletion for efficient use of RNA-seq capacity. Curr Protoc Mol Biol. 2013;Chapter 4:Unit 4 19.
PubMed
Google Scholar
Moore MJ, Proudfoot NJ. Pre-mRNA processing reaches back to transcription and ahead to translation. Cell. 2009;136(4):688–700.
Article
CAS
PubMed
Google Scholar
Detke S, Stein JL, Stein GS. Synthesis of histone messenger RNAs by RNA polymerase II in nuclei from S phase HeLa S3 cells. Nucleic Acids Res. 1978;5(5):1515–28.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sunwoo H, Dinger ME, Wilusz JE, Amaral PP, Mattick JS, Spector DL. MEN epsilon/beta nuclear-retained non-coding RNAs are up-regulated upon muscle differentiation and are essential components of paraspeckles. Genome Res. 2009;19(3):347–59.
Article
CAS
PubMed
PubMed Central
Google Scholar
Wilusz JE, Freier SM, Spector DL. 3′ end processing of a long nuclear-retained noncoding RNA yields a tRNA-like cytoplasmic RNA. Cell. 2008;135(5):919–32.
Article
CAS
PubMed
PubMed Central
Google Scholar
Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.
Zhang X-O, Yin Q-F, Chen L-L, Yang L. Gene expression profiling of non-polyadenylated RNA-seq across species. Genomics Data. 2014;2:237–41.
Article
PubMed
PubMed Central
Google Scholar
Yang L, Duff MO, Graveley BR, Carmichael GG, Chen L-L. Genomewide characterization of non-polyadenylated RNAs. Genome Biol. 2011;12(2):R16.
Article
CAS
PubMed
PubMed Central
Google Scholar
Cui P, Lin Q, Ding F, Xin C, Gong W, Zhang L, et al. A comparison between ribo-minus RNA-sequencing and polyA-selected RNA-sequencing. Genomics. 2010;96(5):259–65.
Ameur A, Zaghlool A, Halvardson J, Wetterbom A, Gyllensten U, Cavelier L, et al. Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain. Nat Struct Mol Biol. 2011;18(12):1435–40.
Zhang X, Rosen BD, Tang H, Krishnakumar V, Town CD. Polyribosomal RNA-Seq reveals the decreased complexity and diversity of the Arabidopsis translatome. PLoS One. 2015;10(2):e0117699.
Article
PubMed
PubMed Central
Google Scholar
Mabbott NA, Baillie JK, Brown H, Freeman TC, Hume DA. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics. 2013;14(1):1–13.
Article
Google Scholar
Flecknell P. Replacement, reduction and refinement. ALTEX. 2002;19(2):73–8.
PubMed
Google Scholar
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12(1):323.
Article
CAS
PubMed
PubMed Central
Google Scholar
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 Protocols. 2012;7(3):562–78.
Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotech. 2016;34(5):525–7.
Article
CAS
Google Scholar
Patro R, Mount SM, Kingsford C. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotech. 2014;32(5):462–4.
Article
CAS
Google Scholar
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–19.
Zhang Z, Wang W. RNA-Skim: a rapid method for RNA-Seq quantification at transcript level. Bioinformatics. 2014;30(12):i283–92.
Article
CAS
PubMed
PubMed Central
Google Scholar
Srivastava A, Sarkar H, Gupta N, Patro R. RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes. Bioinformatics. 2016;32(12):i192–200.
Article
CAS
PubMed
PubMed Central
Google Scholar
Clark EL, Bush SJ, McCulloch MEB, Farquhar IL, Young R, Lefevre L, Pridans C, Tsang HG, Wu C, Afrasiabi C, et al. A High Resolution Atlas Of Gene Expression In The Domestic Sheep (Ovis aries). bioRxiv. 2017. doi: 10.1101/132696. http://biorxiv.org/content/early/2017/05/01/132696.
Schroder K, Irvine KM, Taylor MS, Bokil NJ, Le Cao K-A, Masterman K-A, et al. Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages. Proc Natl Acad Sci. 2012;109(16):E944–53.
Kapetanovic R, Fairbairn L, Beraldi D, Sester DP, Archibald AL, Tuggle CK, et al. Pig Bone Marrow-Derived Macrophages Resemble Human Macrophages in Their Response to Bacterial Lipopolysaccharide. J Immunol. 2012;188(7):3382–94.
Karagianni AE, Kapetanovic R, McGorum BC, Hume DA, Pirie SR. The equine alveolar macrophage: Functional and phenotypic comparisons with peritoneal macrophages(). Vet Immunol Immunopathol. 2013;155(4):219–28.
Article
CAS
PubMed
PubMed Central
Google Scholar
Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotech. 2016;34:525-7.
O'Reilly D, Dienstbier M, Cowley SA, Vazquez P, Drożdż M, Taylor S, et al. Differentially expressed, variant U1 snRNAs regulate gene expression in human cells. Genome Res. 2013;23(2):281–91.
Robert C, Watson M. Errors in RNA-Seq quantification affect genes of relevance to human disease. Genome Biol. 2015;16(1):177.
Article
PubMed
PubMed Central
Google Scholar
Consortium TEP. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 2007;447(7146):799–816.
Article
Google Scholar
Soneson C, Matthes KL, Nowicka M, Law CW, Robinson MD. Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage. Genome Biol. 2016;17(1):12.
Article
PubMed
PubMed Central
Google Scholar
Lin S, Lin Y, Nery JR, Urich MA, Breschi A, Davis CA, et al. Comparison of the transcriptional landscapes between human and mouse tissues. Proc Natl Acad Sci U S A. 2014;111(48):17224–9.
Freeman TC, Goldovsky L, Brosch M, van Dongen S, Maziere P, Grocock RJ, et al. Construction, visualisation, and clustering of transcription networks from microarray expression data. PLoS Comput Biol. 2007;3(10):2032–42.
Mabbott NA, Kenneth Baillie J, Hume DA, Freeman TC. Meta-analysis of lineage-specific gene expression signatures in mouse leukocyte populations. Immunobiology. 2010;215(9-10):724–36.
Article
CAS
PubMed
Google Scholar
Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Haberle V, et al. A promoter-level mammalian expression atlas. Nature. 2014;507(7493):462–70.
Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507(7493):455–61.
Dhanasekaran SM, Balbin OA, Chen G, Nadal E, Kalyana-Sundaram S, Pan J, et al. Transcriptome meta-analysis of lung cancer reveals recurrent aberrations in NRG1 and Hippo pathway genes. Nat Commun. 2014;5:5893.
Cunningham F, Amode MR, Barrell D, Beal K, Billis K, Brent S, et al. Ensembl 2015. Nucleic Acids Res. 2015;43(D1):D662–9.
Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protocols. 2013;8(8):1494–512.
Chen C, Grennan K, Badner J, Zhang D, Gershon E, Jin L, et al. Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods. PLoS One. 2011;6(2):e17238.
van Dijk EL, Jaszczyszyn Y, Thermes C. Library preparation methods for next-generation sequencing: Tone down the bias. Exp Cell Res. 2014;322(1):12–20.
Article
PubMed
Google Scholar
Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11(10):733–9.
Auer PL, Doerge RW. Statistical Design and Analysis of RNA Sequencing Data. Genetics. 2010;185(2):405–16.
Article
CAS
PubMed
PubMed Central
Google Scholar
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(1):1–14.
Article
Google Scholar
McIntyre LM, Lopiano KK, Morse AM, Amin V, Oberg AL, Young LJ, et al. RNA-seq: technical variability and sampling. BMC Genomics. 2011;12(1):1–13.
García-Ortega LF, Martínez O. How Many Genes Are Expressed in a Transcriptome? Estimation and Results for RNA-Seq. PLoS One. 2015;10(6):e0130262.
Article
PubMed
PubMed Central
Google Scholar
Balwierz PJ, Carninci P, Daub CO, Kawai J, Hayashizaki Y, Van Belle W, et al. Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data. Genome Biol. 2009;10(7):R79.
Hebenstreit D, Fang M, Gu M, Charoensawan V, van Oudenaarden A, Teichmann SA. RNA sequencing reveals two major classes of gene expression levels in metazoan cells. Mol Syst Biol. 2011;7:497.
Article
PubMed
PubMed Central
Google Scholar
Kapetanovic R, Fairbairn L, Beraldi D, Sester DP, Archibald AL, Tuggle CK, Hume DA. Pig bone marrow-derived macrophages resemble human macrophages in their response to bacterial lipopolysaccharide. J Immunol. 2012;188(7):3382-94.
Yates A, Akanni W, Amode MR, Barrell D, Billis K, Carvalho-Silva D, et al. Ensembl 2016. Nucleic Acids Res. 2016;44(D1):D710–6.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.
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 Biotech. 2011;29(7):644–52.
Marçais G, Kingsford C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics. 2011;27(6):764–70.
Article
PubMed
PubMed Central
Google Scholar
Langmead B, Trapnell C, Pop M, Salzberg S. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25.
Article
PubMed
PubMed Central
Google Scholar
Mistry J, Finn RD, Eddy SR, Bateman A, Punta M. Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions. Nucleic Acids Res. 2013;41(12):e121.
Article
CAS
PubMed
PubMed Central
Google Scholar
Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 2016;44(D1):D279–85.
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421.
Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. UniProtKB/Swiss-Prot. Methods in molecular biology (Clifton, NJ). 2007;406:89–112.
CAS
Google Scholar
The UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43(Database issue):D204–12.
Article
Google Scholar
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402.
Rice P, Longden I, Bleasby A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 2000;16(6):276–7.
Article
CAS
PubMed
Google Scholar
Wang L, Park HJ, Dasari S, Wang S, Kocher J-P, Li W. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res. 2013;41(6):e74.
Fickett JW. Recognition of protein coding regions in DNA sequences. Nucleic Acids Res. 1982;10(17):5303–18.
Article
CAS
PubMed
PubMed Central
Google Scholar
Fickett JW, Tung C-S. Assessment of protein coding measures. Nucleic Acids Res. 1992;20(24):6441–50.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9.
topGO: Enrichment analysis for Gene Ontology [http://www.bioconductor.org/packages/release/bioc/html/topGO.html]. Accessed 29 Nov 2016.
Alexa A, Rahnenführer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22(13):1600–7.
Article
CAS
PubMed
Google Scholar
Kinsella RJ, Kahari A, Haider S, Zamora J, Proctor G, Spudich G, et al. Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database : the journal of biological databases and curation. 2011;2011:bar030.
Kumar S, Stecher G, Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Mol Biol Evol. 2016;33(7):1870–4.
Article
CAS
PubMed
Google Scholar
R: A Language and Environment for Statistical Computing [http://www.R-project.org]. Accessed 29 Nov 2016.
Cliff N. Dominance statistics: Ordinal analyses to answer ordinal questions. Psychol Bull. 1993;114(3):494–509.
Article
Google Scholar
Macbeth G, Razumiejczyk E, Ledesma RD. Cliff's delta calculator: a non-parametric effect size program for two groups of observations. Universitas Psychologica. 2011;10(2):545–55.
Google Scholar
effsize: Efficient Effect Size Computation (R package version 0.5.4) [http://cran.r-project.org/web/packages/effsize/index.html]. Accessed 29 Nov 2016.
Romano J, Kromrey JD, Coraggio J, Skowronek J. Appropriate statistics for ordinal level data: should we really be using t-test and Cohen's d for evaluating group differences on the NSSE and other surveys? In: Annual Meeting of the Florida Association of Institutional Research; Cocoa Beach, Florida, USA. 2006.
Venny: an interactive tool for comparing lists with Venn's diagrams [http://bioinfogp.cnb.csic.es/tools/venny/]. Accessed 29 Nov 2016.