RNA Sequencing and its Applications in Cancer Diagnosis and Targeted Therapy
Abstract
High throughput DNA and RNA sequencing (DNA-Seq and RNA-Seq) is increasingly impacting the clinical practice of medicine. RNA-Seq has so far had a smaller role in the clinical practice, but it has advantageous features and is complementary to DNA-Seq. RNA-Seq can profile the abundance and composition of the entire transcriptome, including both mRNA and non-coding RNA. It is thus capable of revealing diverse functional and structural changes affecting genes, such as gene overexpression, silencing and various abnormalities and alterations among which may be substitutions, deletions, inversions, alternative splicing and gene fusions. As cancers are characterized by many of these changes, RNA-Seq can be valuable for diagnosing and characterizing tumors. Here we will describe the use of RNA-Seq in cancer diagnosis and personalized therapy, with an emphasis on the detection of fusion transcripts, which are frequently associated with cancer and are often drug targets for cancer therapy.
Keywords
Full Text:
PDFReferences
Koboldt DC, Steinberg KM, Larson DE, Wilson RK, Mardis ER. The next-generation sequencing revolution and its impact on genomics. Cell. 2013;155(1):27-38.
Rivera CM1, Ren B. Mapping human epigenomes. Cell. 2013;155(1):39-55.
Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57-63.
Martin JA1, Wang Z. Next-generation transcriptome assembly. Nat Rev Genet. 2011;12(10):671-682.
Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities. Nat Rev Genet. 2011;12(2):87-98.
Zheng Z, Liebers M, Zhelyazkova B, et al. Anchored multiplex PCR for targeted next-generation sequencing. Nat Med. [in press]
Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010;11(1):31-46.
Mardis ER. A decade's perspective on DNA sequencing technology. Nature. 2011;470(7333):198-203.
Mardis ER. Next-generation sequencing platforms. Annu Rev Anal Chem. 2013;6:287-303.
Bonnal RJP, Aerts J, Githinji G, et al. Biogem: an effective tool-based approach for scaling up open source software development in bioinformatics. Bioinformatics. 2012;28(7):1035-1037.
St.Laurent AM. Understanding Open Source and Free Software Licensing. O’Reill Media Inc, Sebastopol, CA. 2004.
Vincent AT, Charette SJ. Freedom in bioinformatics. Front. Genet. 2014;5:259.
Li H, Ruan J, Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008; 18(11):1851-1858.
Li R, Li Y, Kristiansen K, et al. SOAP: short oligonucleotide alignment program. Bioinformatics. 2008;24(5):713-714.
Jiang H, Wong WH. SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics. 2008;24(20):2395-2396.
Weese D, Emde AK, Rausch T, et al. RazerS - fast read mapping with sensitivity control. Genome Res. 2009;19(9):1646-1654.
Langmead B, Trapnell C, Pop M, et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009; 10(3):R25.
Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754-1760.
Homer N, Merriman B, Nelson SF. BFAST: an alignment tool for large scale genome resequencing. PLoS One. 2009;4(11):e7767.
Rumble SM, Lacroute P, Dalca AV, et al. SHRiMP: Accurate Mapping of Short Color-space Reads. PLoS Comput Biol. 2009; 5(5):e1000386.
http://www.novocraft.com/main/page.php?s=novoalign.
Li R, Yu C, Li Y, et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009; 25(15):1966-1967.
Clement NL, Snell Q, Clement MJ, et al. The GNUMAP algorithm: unbiased probabilistic mapping of oligonucleotides from next-generation sequencing. Bioinformatics. 2010; 26(1):38-45.
Gerton L, Goodson M. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res. 2011; 21(6):936-939.
Mu JC, Jiang H, Kiani A, et al. Fast and accurate read alignment for resequencing. Bioinformatics. 2012;28(18):2366-2373.
Lee W-P, Stromberg MP, Ward A, et al. MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping. PLoS ONE. 2014; 9(3):e90581.
Bona FD, Ossowski S, Schneeberger K, et al. Optimal spliced alignments of short sequence reads. Bioinformatics. 2008;24(16):i174-i180.
Trapnell C, Pathter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105-1111.
http://www.sanger.ac.uk/resources/software/smalt/.
Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010;26(7):873-881.
Jean G, Kahles A, Sreedharan VT, De Bona F, Rätsch G. RNA-Seq read alignments with PALMapper. Curr Protoc Bioinformatics. 2010; Chapter 11: Unit 11.6.
Ameur A, Wetterbom A, Feuk L, Gyllensten U. Global and unbiased detection of splice junctions from RNA-seq data. Genome Biol. 2010;11(3):R34.
Au KF, Jiang H, Lin L, Xing Y, Wong WH. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucl. Acids Res. 2010;38(14):4570-4578.
Wang K, Singh D, Zeng Z, et al. MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery. Nucl Acids Res. 2010;38(18):e178.
Dimon MT, Sorber K, DeRisi JL. HMMSplicer: A Tool for Efficient and Sensitive Discovery of Known and Novel Splice Junctions in RNA-Seq Data. PLoS ONE. 2010;5(11):e13875
Dobin A, Davis C, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21.
Marco-Sola S, Sammeth M, Guigó R, Ribeca P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat Methods. 2012;9(12):1185-1188.
Liao Y, Smyth GK, Shi W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 2013;41(10):e108.
Nix DA, Courdy SJ, Boucher KM. Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks. BMC Bioinformatics. 2008;9:523.
Trapnell C, Roberts A, Goff L, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protoc. 2012;7(3):562-578.
Auer PL, Doerge R. A Two-Stage Poisson Model for Testing RNA-Seq Data. Statistical Applications in Genetics and Molecular Biology. 2011;10(1):1-26.
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.
Cumbie JS, Kimbrel JA, Di Y, et al. GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences. PLoS One. 2011;6(10):e25279.
Li J, Tibshirani R. Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Stat Methods Med Res. 2013;22(5):519-536.
Zhou YH, Xia K, Wright FA. A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics. 2011;27(19):2672-2678.
Feng J, Meyer CA, Wang Q, Liu JS, Liu XS, Zhang Y. GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics. 2012;28(21):2782-2788.
Smyth GK. Limma: linear models for microarray data. In Gentleman R, Carey V, Dudoit S, Irizarry R and Huber W (eds.), Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer, New York. 2005;pp.397-420.
Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106.
Hardcastle TJ, Kelly KA. BaySeq: Empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics. 2010;11:422.
Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A, Conesa A. Differential expression in RNA-seq: a matter of depth. Genome Res. 2011;21(12):2213-2223.
Robinson MD, McCarthy D, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-140.
Yu D, Huber W, Vitek O. Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size. Bioinformatics. 2013;29(10):1275-1282.
Leng N, Dawson JA, Kendziorski C. EBSeq: An R package for gene and isoform differential expression analysis of RNA-seq data. R package version 1.5.3, 2014.
https://www.biostat.wisc.edu/~kendzior/EBSEQ/.
Robertson G, Schein J, Chiu R, et al. De novo assembly and analysis of RNA-seq data. Nat Methods. 2010;7(11):909-912.
Grabherr MG, Haas BJ, Yassour M, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29(7):644-652.
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(8):1086-1092.
Xie Y, Wu G, Tang J, et al. SOAPdenovo-Trans: De novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014;30(12):1660-1666.
Piskol R, Ramaswami G, Li JB. Reliable Identification of Genomic Variants from RNA-Seq Data. Am J Hum Genet. 2013;93(4):641-651.
Hill JT, Demarest BL, Bisgrove BW, et al. MMAPPR: Mutation Mapping Analysis Pipeline for Pooled RNA-seq. Genome Res. 2013;23(4):687-697.
https://github.com/davidliwei/rnaseqmut.
Katz Y, Wang ET, Airoldi EM, Burge CB. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010;7(12):1009-1015.
Griffith M, Griffith OL, Mwenifumbo J, et al. Alternative expression analysis by RNA sequencing. Nat Methods. 2010;7(10):843-847.
Zhou A, Breese MR, Hao Y, et al. Alt Event Finder: a tool for extracting alternative splicing events from RNA-seq data. BMC Genomics. 2012; 13(Suppl 8):S10.
Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res. 2012;22(10):2008-2017.
Drewe P, Stegle O, Hartmann L, et al. Accurate detection of differential RNA processing. Nucleic Acids Res. 2013;41(10):5189-5198.
Mazin P, Xiong J, Liu X, et al. Widespread splicing changes in human brain development and aging. Mol Syst Biol. 2013; 9:633.
Rasche A, Lienhard M, Yaspo ML, Lehrach H, Herwig R. ARH-seq: identification of differential splicing in RNA-seq data. Nucl Acids Res. 2014;42(14):e110.
Majoros WH, Lebeck N, Ohler U, Li Song. Improved transcript isoform discovery using ORF graphs. Bioinformatics. 2014; 30(14):1958-1964.
Gonzàlez-Porta M, Brazma A. Identification, annotation and visualisation of extreme changes in splicing from RNA-seq experiments with SwitchSeq, bioRxiv. 2014;
Kim D, Salzberg SL. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 2011;12(8):R72.
Ge H, Liu K, Juan T, Fang F, Newman M, Hoeck W. FusionMap: detecting fusion genes from next-generation sequencing data at base-pair resolution. Bioinformatics. 2011;27(14):1922-1928.
Francis RW, Thompson-Wicking K, Carter KW, Anderson D, Kees UR. FusionFinder: A Software Tool to Identify Expressed Gene Fusion Candidates from RNA-Seq Data. PLoS ONE. 2012;7(6):e39987.
Wu H, Wu MC, Zhi D, Santorico SA, Cui X. Statistics for next generation sequencing - meeting report. Front Genet. 2012;3:128.
Sboner A, Habegger L, Pflueger D, Terry S, Chen DZ. FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data. Genome Biol. 2010;11(10):R104.
Li Y, Chien J, Smith DI, Ma J. FusionHunter: identifying fusion transcripts in cancer using paired-end RNA-seq. Bioinformatics. 2011; 27(12):1708-1710.
Iyer MK, Chinnaiyan AM, Maher CA. ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics. 2011;27(20):2903-2904.
Asmann YW, Hossain A, Necela BM, et al. A novel bioinformatics pipeline for identification and characterization of fusion transcripts in breast cancer and normal cell lines. Nucleic Acids Res. 2011; 39(15):e100.
McPherson A, Hormozdiari F, Zayed A, et al. deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data. PLoS Comput Biol. 2011;7(5):e1001138.
Kinsella M, Harismendy O, Nakano M, Frazer KA, Bafna V. Sensitive Gene Fusion Detection Using Ambiguously Mapping RNA-Seq Read Pairs. Bioinformatics. 2011;27(8):1068-1075.
Benelli M, Pescucci C, Marseglia G, Severgnini M, Torricelli F, Magi A. Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript. Bioinformatics. 2012;28(24):3232-3239.
Wu J, Zhang W, Huang S, et al. SOAPfusion: a robust and effective computational fusion discovery tool for RNA-seq reads. Bioinformatics. 2013;29(23):2971-2978.
Chepelev I1, Wei G, Tang Q, Zhao K. Detection of single nucleotide variations in expressed exons of the human genome using RNA-Seq. Nucleic Acids Res. 2009;37(16):e106.
Atak ZK, Gianfelici V, Hulselmans G, et al. Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia. PLoS Genet. 2013; 9(12):e1003997.
Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817-26.
Sinicropi D, Qu K, Collin F, et al. Whole transcriptome RNA-Seq analysis of breast cancer recurrence risk using formalin-fixed paraffin-embedded tumor tissue. PLoS One. 2012;7(7):e40092.
Ma Y, Ambannavar R, Stephans J, et al. Fusion transcript discovery in formalin-fixed paraffin-embedded human breast cancer tissues reveals a link to tumor progression. PLoS One. 2014;11:9(4):e94202.
Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368(22):2059-2074.
Rehm HL1, Bale SJ, Bayrak-Toydemir P, et al. ACMG clinical laboratory standards for next-generation sequencing. Genet Med. 2013; 15(9):733-747.
Gargis AS, Kalman L, Berry MW, et al. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nat Biotechnol. 2012;30(11):1033-1036.
Ferreira-Gonzalez A, Emmadi R, Day SP, et al, Revisiting oversight and regulation of molecular-based laboratory-developed tests: a position statement of the Association for Molecular Pathology. J Mol Diagn. 2014;16(1):3-6.
Wu AR Neff NF, Kalisky T, et al, Quantitative assessment of single-cell RNA-sequencing methods. Nat Methods. 2014; 11(1):41-46.
Jaitin DA1, Kenigsberg E, Keren-Shaul H, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014; 343(6172):776-779.
Lu J, Getz G, Miska EA, et al, MicroRNA expression profiles classify human cancers. Nature. 2005; 435(7043):834-838.
Adams BD, Kasinski AL, Slack FJ. Aberrant Regulation and Function of MicroRNAs in Cancer. Curr Biol. 2014; 24(16):R762-R776.
Ulitsky I, Bartel DP. lincRNAs: genomics, evolution, and mechanisms. Cell. 2013;154(1):26-46.
Refbacks
- There are currently no refbacks.