Common applications of next-generation sequencing technologies in genomic research

Translational Cancer Research

Volumn 2, Issue 1, 2013, Pages 33-45

  Lee, Chien Yueh ^a   Chiu, Yu Chiao ^a   Wang, Liang Bo ^a   Kuo, Yu Lun ^a   Chuang, Eric Y ^a   Lai, Liang Chuan ^a   Tsai, Mong Hsun ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

DNA sequencing; Next generation sequencing; RNA sequencing; Small RNA sequencing

Indexed keywords

NUCLEOTIDE; RNA; TRANSCRIPTOME;

CHROMATIN IMMUNOPRECIPITATION; EPIGENETICS; GENE EXPRESSION PROFILING; GENE SEQUENCE; GENETIC VARIATION; GENOMICS; HUMAN; NEXT GENERATION SEQUENCING; NONHUMAN; PRACTICE GUIDELINE; REVIEW; RNA SEQUENCE; TRANSCRIPTOMICS; WHOLE GENOME SEQUENCING;

EID: 84962713603     PISSN: 2218676X     EISSN: 22196803     Source Type: Journal    
DOI: 10.3978/j.issn.2218-676X.2013.02.09     Document Type: Review

References (90)

1. Lamb J, Crawford ED, Peck D, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006;313:1929-35.
2. Git A, Dvinge H, Salmon-Divon M, et al. Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA 2010;16:991-1006.
3. Roh SW, Abell GC, Kim KH, et al. Comparing microarrays and next-generation sequencing technologies for microbial ecology research. Trends Biotechnol 2010;28:291-9.
4. Sîrbu A, Kerr G, Crane M, et al. RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering. PLoS One 2012;7:e50986.
5. Li R, Fan W, Tian G, et al. The sequence and de novo assembly of the giant panda genome. Nature 2010;463:311-7.
6. Vallender EJ. Expanding whole exome resequencing into non-human primates. Genome Biol 2011;12:R87.
7. Voelkerding KV, Dames S, Durtschi JD. Next generation sequencing for clinical diagnostics-principles and application to targeted resequencing for hypertrophic cardiomyopathy: a paper from the 2009 William Beaumont Hospital Symposium on Molecular Pathology. J Mol Diagn 2010;12:539-51.
8. Friedländer MR, Chen W, Adamidi C, et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 2008;26:407-15.
9. Xu G, Wu J, Zhou L, et al. Characterization of the small RNA transcriptomes of androgen dependent and independent prostate cancer cell line by deep sequencing. PLoS One 2010;5:e15519.
10. Zywicki M, Bakowska-Zywicka K, Polacek N. Revealing stable processing products from ribosome-associated small RNAs by deep-sequencing data analysis. Nucleic Acids Res 2012;40:4013-24.
11. Zhang J, Chiodini R, Badr A, et al. The impact of next-generation sequencing on genomics. J Genet Genomics 2011;38:95-109.
12. Li H, Ruan J, Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 2008;18:1851-8.
13. Homer N, Merriman B, Nelson SF. BFAST: an alignment tool for large scale genome resequencing. PLoS One 2009;4:e7767.
14. Novocraft. Novoalign. Available online: http://www.novocraft.com
15. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 2010;26:589-95.
16. Liu CM, Wong T, Wu E, et al. SOAP3: ultra-fast GPU-based parallel alignment tool for short reads. Bioinformatics 2012;28:878-9.
17. Jeck WR, Reinhardt JA, Baltrus DA, et al. Extending assembly of short DNA sequences to handle error. Bioinformatics 2007;23:2942-4.
18. Margulies M, Egholm M, Altman WE, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005;437:376-80.
19. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 2008;18:821-9.
20. DePristo MA, Banks E, Poplin R, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 2011;43:491-8.
21. Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25:2078-9.
22. Koboldt DC, Chen K, Wylie T, et al. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 2009;25:2283-5.
23. Koboldt DC, Zhang Q, Larson DE, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 2012;22:568-76.
24. Larson DE, Harris CC, Chen K, et al. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics 2012;28:311-7.
25. Roth A, Ding J, Morin R, et al. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics 2012;28:907-13.
26. Chen K, Wallis JW, McLellan MD, et al. BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nat Methods 2009;6:677-81.
27. Hormozdiari F, Hajirasouliha I, Dao P, et al. Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery. Bioinformatics 2010;26:i350-7.
28. Zeitouni B, Boeva V, Janoueix-Lerosey I, et al. SVDetect: a tool to identify genomic structural variations from paired-end and mate-pair sequencing data. Bioinformatics 2010;26:1895-6.
29. Korbel JO, Abyzov A, Mu XJ, et al. PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Genome Biol 2009;10:R23.
30. Grabherr MG, Haas BJ, Yassour M, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 2011;29:644-52.
31. Robertson G, Schein J, Chiu R et al. De novo assembly and analysis of RNA-seq data. Nat Methods 2010;7:909-12.
32. Schulz MH, Zerbino DR, Vingron M, et al. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 2012;28:1086-92.
33. 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:R25.
34. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012;9:357-9.
35. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009;25:1105-11.
36. Anders S. HTSeq: Analysing high-throughput sequencing data with Python. [cited 2013]; Available online: http://www-huber.embl.de/users/anders/HTSeq/
37. Roberts A, Trapnell C, Donaghey J, et al. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol 2011;12:R22.
38. Trapnell C, Williams BA, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010;28:511-5.
39. Roberts A, Pimentel H, Trapnell C, et al. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics 2011;27:2325-9.
40. Trapnell C, Hendrickson DG, Sauvageau M, et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 2013;31:46-53.
41. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010;11:R106.
42. Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 2010;11:422.
43. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-40.
44. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet journal 2011;17.
45. Illumina. Flicker 3.0. Available online: http://support.illumina.com/sequencing/sequencing_software/flicker_30_small_rna_analysis.ilmn
46. Lab H. FASTX-Toolkit. Available online: http://hannonlab.cshl.edu/fastx_toolkit/
47. Buffalo V. scythe: A 3'-end Aadapter Contaminant Trimmer. Available online: https://github.com/vsbuffalo/scythe
48. Patel RK, Jain M. NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data. PLoS One 2012;7:e30619.
49. Bioinformatics B. FastQC: A quality control tool for high throughput sequence data.; Available online: http://www.bioinformatics.babraham.ac.uk/
50. Buffalo V. qrqc: Quick Read Quality Control. 2012.
51. Huang PJ, Liu YC, Lee CC, et al. DSAP: deep-sequencing small RNA analysis pipeline. Nucleic Acids Res 2010;38:W385-91.
52. Hackenberg M, Rodríguez-Ezpeleta N, Aransay AM. miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res 2011;39:W132-8.
53. Friedlander MR, Chen W, Adamidi C, et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 2008;26:407-15.
54. Friedlander MR, Mackowiak SD, Li N, et al. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 2012;40:37-52.
55. Mathelier A, Carbone A. MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics 2010;26:2226-34.
56. Guan DG, Liao JY, Qu ZH, et al. mirExplorer: detecting microRNAs from genome and next generation sequencing data using the AdaBoost method with transition probability matrix and combined features. RNA Biol 2011;8:922-34.
57. Hendrix D, Levine M, Shi W. miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data. Genome Biol 2010;11:R39.
58. Eipper-Mains JE, Eipper BA, Mains RE. Global approaches to the role of miRNAs in drug-induced changes in gene expression. Front Genet 2012;3:109.
59. Burrows M, Wheeler DJ. A block-sorting lossless data compression algorithm. HP Labs Technical Reports 1994:SRC-RR-124.
60. Treangen TJ, Salzberg SL. Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nat Rev Genet 2012;13:36-46.
61. Miller JR, Koren S, Sutton G. Assembly algorithms for next-generation sequencing data. Genomics 2010;95:315-27.
62. Feuk L, Carson AR, Scherer SW. Structural variation in the human genome. Nat Rev Genet 2006;7:85-97.
63. Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 2012. [Epub ahead of print].
64. Robinson JT, Thorvaldsdottir H, Winckler W, et al. Integrative genomics viewer. Nat Biotechnol 2011;29:24-6.
65. Bullard JH, Purdom E, Hansen KD, et al Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010;11:94.
66. Marioni JC, Mason CE, Mane SM, et al. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 2008;18:1509-17.
67. Miller AC, Obholzer ND, Shah AN, et al. RNA-seq-based mapping and candidate identification of mutations from forward genetic screens. Genome Res 2013. [Epub ahead of print].
68. Cánovas A, Rincon G, Islas-Trejo A, et al. SNP discovery in the bovine milk transcriptome using RNA-Seq technology. Mamm Genome 2010;21:592-8.
69. Chepelev I, Wei G, Tang Q, et al. Detection of single nucleotide variations in expressed exons of the human genome using RNA-Seq. Nucleic Acids Res 2009;37:e106.
70. Hill JT, Demarest BL, Bisgrove BW, et al. MMAPPR: Mutation Mapping Analysis Pipeline for Pooled RNA-seq. Genome Res 2013. [Epub ahead of print].
71. Seila AC, Calabrese JM, Levine SS, et al. Divergent transcription from active promoters. Science 2008;322:1849-51.
72. Affymetrix ENCODE Transcriptome Project, Cold Spring Harbor Laboratory ENCODE Transcriptome Project. Post-transcriptional processing generates a diversity of 5'-modified long and short RNAs. Nature 2009;457:1028-32.
73. Taft RJ, Glazov EA, Cloonan N, et al. Tiny RNAs associated with transcription start sites in animals. Nat Genet 2009;41:572-8.
74. Guttman M, Donaghey J, Carey BW, et al. lincRNAs act in the circuitry controlling pluripotency and differentiation. Nature 2011;477:295-300.
75. Ng JH, Ng HH. LincRNAs join the pluripotency alliance. Nat Genet 2010;42:1035-6.
76. Jacquier A. The complex eukaryotic transcriptome: unexpected pervasive transcription and novel small RNAs. Nat Rev Genet 2009;10:833-44.
77. McGettigan PA. Transcriptomics in the RNA-seq era. Curr Opin Chem Biol 2013. [Epub ahead of print].
78. Burge SW, Daub J, Eberhardt R, et al. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res 2013;41:D226-32.
79. Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 2011;39:D152-7.
80. Yang X, Li L. miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics 2011;27:2614-5.
81. Barzon L, Lavezzo E, Militello V, et al. Applications of Next-Generation Sequencing Technologies to Diagnostic Virology. Int J Mol Sci 2011;12:7861-84.
82. Vodovar N, Goic B, Blanc H, et al. In silico reconstruction of viral genomes from small RNAs improves virus-derived small interfering RNA profiling. J Virol 2011;85:11016-21.
83. Li Y, Zhang Z, Liu F, et al. Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis. Nucleic Acids Res 2012;40:4298-305.
84. Williamson V, Kim A, Xie B, et al. Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation. Brief Bioinform 2013;14:36-45.
85. Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer 2006;6:857-66.
86. Pritchard CC, Cheng HH, Tewari M. MicroRNA profiling: approaches and considerations. Nat Rev Genet 2012;13:358-69.
87. Git A, Dvinge H, Salmon-Divon M, et al. Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA 2010;16:991-1006.
88. Linsen SEV, de Wit E, Janssens G, et al. Limitations and possibilities of small RNA digital gene expression profiling. Nat Methods 2009;6:474-6.
89. Tian G, Yin X, Luo H, et al. Sequencing bias: comparison of different protocols of microRNA library construction. BMC Biotechnol 2010;10:64.
90. Jayaprakash AD, Jabado O, Brown BD, et al. Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing. Nucleic Acids Res 2011;39:e141.