Marra, M. et al. An encyclopedia of mouse genes. Nat. Genet. 21, 191–194 (1999).
Carninci, P. et al. Targeting a complex transcriptome: the construction of the mouse full-length cDNA encyclopedia. Genome Res. 13, 1273–1289 (2003).
de Souza, S.J. et al. Identification of human chromosome 22 transcribed sequences with ORF expressed sequence tags. Proc. Natl. Acad. Sci. USA 97, 12690–12693 (2000).
Guttman, M. et al. Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals. Nature 458, 223–227 (2009).
Wang, E.T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).
Adams, M.D. et al. Complementary DNA sequencing: expressed sequence tags and human genome project. Science 252, 1651–1656 (1991).
Haas, B.J. et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res. 31, 5654–5666 (2003).
Kent, W.J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).
Wu, T.D. & Watanabe, C.K. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005).
Kapranov, P. et al. Large-scale transcriptional activity in chromosomes 21 and 22. Science 296, 916–919 (2002).
Pan, Q. et al. Revealing global regulatory features of mammalian alternative splicing using a quantitative microarray platform. Mol. Cell 16, 929–941 (2004).
Castle, J.C. et al. Expression of 24,426 human alternative splicing events and predicted cis regulation in 48 tissues and cell lines. Nat. Genet. 40, 1416–1425 (2008).
Schena, M., Shalon, D., Davis, R.W. & Brown, P.O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).
Golub, T.R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).
Cloonan, N. et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 5, 613–619 (2008).
Denoeud, F. et al. Annotating genomes with massive-scale RNA sequencing. Genome Biol. 9, R175 (2008).
Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008).
Maher, C.A. et al. Transcriptome sequencing to detect gene fusions in cancer. Nature 458, 97–101 (2009).
Marioni, J.C., Mason, C.E., Mane, S.M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).
First systematic comparison of expression arrays and RNA-seq revealed that technical variability between RNA-seq runs is extremely low; the authors developed the first methods for principled differential analysis of expression with read counts.
Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621–628 (2008).
One of the first papers to describe the RNA-seq experimental protocol and provided the foundations for the computational analysis of quantitative transcriptome sequencing by introducing the RPKM expression metric.
Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).
Sultan, M. et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321, 956–960 (2008).
Yassour, M. et al. Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing. Proc. Natl. Acad. Sci. USA 106, 3264–3269 (2009).
Blekhman, R., Marioni, J.C., Zumbo, P., Stephens, M. & Gilad, Y. Sex-specific and lineage-specific alternative splicing in primates. Genome Res. 20, 180–189 (2010).
Wilhelm, B.T. et al. RNA-seq analysis of two closely related leukemia clones that differ in their self-renewal capacity. Blood 117, e27–e38 (2010).
Berger, M.F. et al. Integrative analysis of the melanoma transcriptome. Genome Res. 20, 413–427 (2010).
Mortazavi, A. et al. Scaffolding a Caenorhabditis nematode genome with RNA-seq. Genome Res. 20, 1740–1747 (2010).
Guttman, M. et al. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat. Biotechnol. 28, 503–510 (2010).
This paper describes a spliced alignment–based genome-guided transcript reconstruction methods that allow discovery of novel genes and isoforms from RNA-seq data.
Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
This paper describes a spliced alignment–based genome-guided transcript reconstruction methods that allow discovery of novel genes and isoforms from RNA-seq data and provided a method for estimating the expression of each reconstructed isoform.
Katz, Y., Wang, E.T., Airoldi, E.M. & Burge, C.B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).
This paper describes a computational method that estimates isoform expression making use of both single and paired-end reads, and provides a Bayesian approach for detecting differential isoform expression.
Homer, N., Merriman, B. & Nelson, S.F. BFAST: an alignment tool for large scale genome resequencing. PLoS ONE 4, e7767 (2009).
Jiang, H. & Wong, W.H. SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics 24, 2395–2396 (2008).
A statistical algorithm to calculate isoform abundances for alternatively spliced genes is described.
Li, H., Ruan, J. & Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18, 1851–1858 (2008).
Li, R., Li, Y., Kristiansen, K. & Wang, J. SOAP: short oligonucleotide alignment program. Bioinformatics 24, 713–714 (2008).
Lunter, G. & Goodson, M. Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. advance online publication 27 October 2010 (doi:10.1101/gr.111120.110).
Rizk, G. & Lavenier, D. GASSST: global alignment short sequence search tool. Bioinformatics 26, 2534–2540 (2010).
Rumble, S.M. et al. SHRiMP: accurate mapping of short color-space reads. PLoS Comput. Biol. 5, e1000386 (2009).
Smith, A.D., Xuan, Z. & Zhang, M.Q. Using quality scores and longer reads improves accuracy of Solexa read mapping. BMC Bioinformatics 9, 128 (2008).
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
Introduced short read alignment with the Burrows-Wheeler transform, allowing the construction of the first fast alignment pipelines for RNA-seq.
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009).
Burrows, M. & Wheeler, D.J.A. Block-sorting lossless data compression algorithm. Digital SRC Reports 124, [AU: provide an article ID number or page numbers, or some other identifying information for this paper, such as a doi number or Pubmed or CrossRef ID] (1994).
Ferragina, P. & Manzini, G. An experimental study of a compressed index. Inf. Sci. 135, 13–28 (2001).
Griffith, M. et al. Alternative expression analysis by RNA sequencing. Nat. Methods 7, 843–847 (2010).
Cloonan, N. et al. RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data. Bioinformatics 25, 2615–2616 (2009).
Degner, J.F. et al. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics 25, 3207–3212 (2009).
Au, K.F., Jiang, H., Lin, L., Xing, Y. & Wong, W.H. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Res. 38, 4570–4578 (2010).
Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).
This method combined fast read alignment using Burrows-Wheeler transform alignment with novel junction discovery, was one of the first scalable RNA-seq alignment programs, and paved the way for gene discovery and transcript reconstruction with RNA-seq.
Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).
Wu, T.D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).
De Bona, F., Ossowski, S., Schneeberger, K. & Ratsch, G. Optimal spliced alignments of short sequence reads. Bioinformatics 24, i174–i180 (2008).
Mikkelsen, T.S. et al. Genome of the marsupial Monodelphis domestica reveals innovation in non-coding sequences. Nature 447, 167–177 (2007).
Robertson, G. et al. De novo assembly and analysis of RNA-seq data. Nat. Methods 7, 909–912 (2010).
Described a variable k-mer approach for genome-independent reconstruction that allows for transcript discovery without a reference genome.
Birol, I. et al. De novo transcriptome assembly with ABySS. Bioinformatics 25, 2872–2877 (2009).
Surget-Groba, Y. & Montoya-Burgos, J.I. Optimization of de novo transcriptome assembly from next-generation sequencing data. Genome Res. 20, 1432–1440 (2010).
De Bruijn, N.G. A combinatorial problem. Koninklijke Nederlandse Akademie v. Wetenschappen 46, 6 (1946).
Pevzner, P.A. 1-Tuple DNA sequencing: computer analysis. J. Biomol. Struct. Dyn. 7, 63–73 (1989).
Zerbino, D.R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).
Zerbino, D.R. Using the Velvet de novo assembler for short-read sequencing technologies. Curr. Protoc. Bioinformatics 31, 11.5.1–11.5.12 (2010).
Blencowe, B.J., Ahmad, S. & Lee, L.J. Current-generation high-throughput sequencing: deepening insights into mammalian transcriptomes. Genes Dev. 23, 1379–1386 (2009).
Lister, R., Gregory, B.D. & Ecker, J.R. Next is now: new technologies for sequencing of genomes, transcriptomes, and beyond. Curr. Opin. Plant Biol. 12, 107–118 (2009).
Pepke, S., Wold, B. & Mortazavi, A. Computation for ChIP-seq and RNA-seq studies. Nat. Methods 6, S22–S32 (2009).
Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).
Oshlack, A. & Wakefield, M.J. Transcript length bias in RNA-seq data confounds systems biology. Biol. Direct 4, 14 (2009).
Robinson, M.D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
Jiang, H. & Wong, W.H. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics 25, 1026–1032 (2009).
Li, B., Ruotti, V., Stewart, R.M., Thomson, J.A. & Dewey, C.N. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26, 493–500 (2010).
Bullard, J.H., Purdom, E., Hansen, K.D. & Dudoit, S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11, 94 (2010).
Wang, X., Wu, Z. & Zhang, X. Isoform abundance inference provides a more accurate estimation of gene expression levels in RNA-seq. J. Bioinform. Comput. Biol. 8 (Suppl. 1), 177–192 (2010).
Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116–5121 (2001).
Grant, G.R., Manduchi, E. & Stoeckert, C.J. Jr. Analysis and management of microarray gene expression data. Curr. Protoc. Mol. Biol. 19 6 (2007).
Grant, G.R., Liu, J. & Stoeckert, C.J. Jr. A practical false discovery rate approach to identifying patterns of differential expression in microarray data. Bioinformatics 21, 2684–2690 (2005).
Langmead, B., Hansen, K.D. & Leek, J.T. Cloud-scale RNA-sequencing differential expression analysis with Myrna. Genome Biol. 11, R83 (2010).
Robinson, M.D. & Smyth, G.K. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881–2887 (2007).
Provided a statistical framework that is well suited to differential expression testing when a small number of RNA-seq replicates are available, and which also works well for larger experiments.
Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
Wang, L., Feng, Z., Wang, X. & Zhang, X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26, 136–138 (2010).
Levin, J.Z. et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat. Methods 7, 709–715 (2010).
Jan, C.H., Friedman, R.C., Ruby, J.G. & Bartel, D.P. Formation, regulation and evolution of Caenorhabditis elegans 3′UTRs. Nature 469, 97–101 (2011).
Mangone, M. et al. The landscape of C. elegans 3′UTRs. Science 329, 432–435 (2010).
Plessy, C. et al. Linking promoters to functional transcripts in small samples with nanoCAGE and CAGEscan. Nat. Methods 7, 528–534 (2010).
Lee, S. et al. Accurate quantification of transcriptome from RNA-Seq data by effective length normalization. Nucleic Acids Res. 39, e9 (2010).