基于RNA-seq技术的肝细胞肝癌转录组学研究
|
摘要
在全基因组层面上阐明基因及外显子的表达水平,尤其是鉴定新的剪接异构体,RNA-seq具有独特的优势。迄今为止,利用RNA-seq对HBV相关HCC的转录组分析尚未报道。在本项目中,基于Solexa/Illumina GAII测序平台读长36bp单末段测序方法,我们首次对10个HCC患者配对的癌及癌旁组织的转录组进行了测序分析。研究结果表明:每一个测序通道,平均产生了21.6百万测序读数及10.6百万配齐读数,这些读数覆盖了已注释基因的50%以上,能够满足后续的数据分析。此外,基于HCC转录组数据库,我们进一步通过基因表达水平分析发现了1,378个差异表达基因及24,338差异表达的外显子,功能分析表明它们主要参与了细胞生长、代谢及免疫应答,这些结果初步揭示了HCC发生机制的复杂性。染色体定位分析发现差异表达的基因大部分位于染色体8q21.3-24.3。最有意义的结果是:通过外显子的分析获得了序列的变异信息及新的转录本信息。最后,我们鉴定了ATAD2基因存在有一个新的exon-exon junction,且在HCC癌组织中显著上调,并利用实时定量PCR进行了验证。总之,我们从基因及外显子表达水平,选择性剪接异构体等方面对HBV相关HCC的转录组进行了全面分析,对阐明HCC的分子病理机制方面提供了重要的线索。本项目的主要研究内容:
1.RNA-seq测序深度及数据质量的评估。
2.RNA-seq数据与基因芯片数据的比较。
3.肝癌及癌旁的差异基因分析,并利用实时定量PCR在大样本中进行验证。
4.差异基因的染色体定位分析,及染色体扩增、缺失的分析。
5.对差异基因的功能注释,包括信号通路及生物学功能。
6.肝癌及癌旁的差异表达的外显子分析,并利用实时定量PCR在大样本中进行验证。
7.鉴定新的选择性剪接体,并利用实时定量PCR在大样本中进行验证。
本项目的主要研究结果:
1.首次利用RNA-seq技术对HBV相关的肝细胞肝癌进行了全转录组测序,发现了1378个差异表达的基因及24338个差异表达的外显子。
2.在差异表达基因的功能注释分析中,确定了与肝细胞肝癌发生密切相关的54条生物学功能及41条信号通路。
3.多个染色体异常位置被发现,最显著的为8q24。
4.在外显子水平上,鉴定了多种选择性剪接模式,尤其是最有代表性的三种剪切模式在20对样本中利用实时定量PCR技术得到了验证。
5. ATAD2基因的一个新的剪接异构体被发现,并且其在癌中的表达显著高于癌旁组织。
结论:
总之,我们从基因,外显子及选择性剪切模式三个方面,全面系统的阐明了HBV相关的肝细胞肝癌的转录特点,为进一步深入理解肝细胞肝癌发生发展的分子病理机制奠定了基础。
RNA-seq is a powerful tool for comprehensive characterization of whole transcriptome at both gene and exon levels and with a unique ability of identifying novel splicing variants. To date, RNA-seq analysis of HBV-related hepatocellular carcinoma (HCC) has not been reported. In this study, we performed transcriptome analyses for 10 matched pairs of cancer and non-cancerous tissues from Chinese patients with HBV-related HCC using 36bp single-end sequencing approach on Solexa/Illumina GAII platform. On average, about 21.6 million sequencing reads and 10.6 million aligned reads were obtained for samples sequenced on each lane, which was able to identify > 50% of all the annotated genes for each sample. Furthermore, from by far the largest database of transcripts expressed in HCC tissues, we identified 1,378 significantly differently expressed genes (DEGs) and 24,338 differentially expressed exons (DEEs). Comprehensive function analyses indicated that cell growth-related, metabolism-related and immune-related pathways were most significantly enriched by DEGs, pointing to a complex mechanism for HCC carcinogenesis. Positional gene enrichment analysis showed that DEGs were most significantly enriched at chromosome 8q21.3-24.3. The most interesting findings were from the analysis at exon levels where we characterized three major patterns of expression changes between gene levels and exon levels, implying a much complex landscape of transcript-specific differential expressions in HCC. Finally, we identified a novel highly up-regulated exon-exon junction in ATAD2 gene in HCC tissues. Overall, to our best knowledge, our study represents the most comprehensive characterization of the HBV-related HCC transcriptome including exon level expression changes and novel splicing variants, which illustrated the power of RNA-seq and provided important clues for understanding the molecular mechanisms of HCC pathogenesis at system-wide levels.
The key study contents:
(1) evaluation of data quality and sequencing depth required for transcriptome analysis.
(2) Comparison between RNA-seq and microarray.
(3) The analysis and validation of differential gene expression.
(4) The chromosome locations of DEGs.
(5) Functional annotation of DEGs.
(6) Exon expression level analysis.
(7) Identification of novel DE exon-exon junctions.
The key study results:
(1) For the first time the RNA-seq analysis of HBV-related HCC.
(2) 1,378 significantly DEGs and 24, 338 DEEs were identified.
(3) 54 bio-function terms and 41 canonical pathways related to HCC.
(4) Many of chromosomal aberrations were identified, especially 8q24.
(5) Some splicing patterns were identified.
(6) A novel-splicing variant in ATAD2 was identified.
Conclusion:
Overall, by RNA-seq our study represents the most comprehensive characterization of the HBV-related HCC transcriptome including gene level expression changes, exon level expression changes and novel splicing variants, which provided important clues for understanding the molecular mechanisms of HCC pathogenesis at system-wide levels.
1.RNA-seq测序深度及数据质量的评估。
2.RNA-seq数据与基因芯片数据的比较。
3.肝癌及癌旁的差异基因分析,并利用实时定量PCR在大样本中进行验证。
4.差异基因的染色体定位分析,及染色体扩增、缺失的分析。
5.对差异基因的功能注释,包括信号通路及生物学功能。
6.肝癌及癌旁的差异表达的外显子分析,并利用实时定量PCR在大样本中进行验证。
7.鉴定新的选择性剪接体,并利用实时定量PCR在大样本中进行验证。
本项目的主要研究结果:
1.首次利用RNA-seq技术对HBV相关的肝细胞肝癌进行了全转录组测序,发现了1378个差异表达的基因及24338个差异表达的外显子。
2.在差异表达基因的功能注释分析中,确定了与肝细胞肝癌发生密切相关的54条生物学功能及41条信号通路。
3.多个染色体异常位置被发现,最显著的为8q24。
4.在外显子水平上,鉴定了多种选择性剪接模式,尤其是最有代表性的三种剪切模式在20对样本中利用实时定量PCR技术得到了验证。
5. ATAD2基因的一个新的剪接异构体被发现,并且其在癌中的表达显著高于癌旁组织。
结论:
总之,我们从基因,外显子及选择性剪切模式三个方面,全面系统的阐明了HBV相关的肝细胞肝癌的转录特点,为进一步深入理解肝细胞肝癌发生发展的分子病理机制奠定了基础。
RNA-seq is a powerful tool for comprehensive characterization of whole transcriptome at both gene and exon levels and with a unique ability of identifying novel splicing variants. To date, RNA-seq analysis of HBV-related hepatocellular carcinoma (HCC) has not been reported. In this study, we performed transcriptome analyses for 10 matched pairs of cancer and non-cancerous tissues from Chinese patients with HBV-related HCC using 36bp single-end sequencing approach on Solexa/Illumina GAII platform. On average, about 21.6 million sequencing reads and 10.6 million aligned reads were obtained for samples sequenced on each lane, which was able to identify > 50% of all the annotated genes for each sample. Furthermore, from by far the largest database of transcripts expressed in HCC tissues, we identified 1,378 significantly differently expressed genes (DEGs) and 24,338 differentially expressed exons (DEEs). Comprehensive function analyses indicated that cell growth-related, metabolism-related and immune-related pathways were most significantly enriched by DEGs, pointing to a complex mechanism for HCC carcinogenesis. Positional gene enrichment analysis showed that DEGs were most significantly enriched at chromosome 8q21.3-24.3. The most interesting findings were from the analysis at exon levels where we characterized three major patterns of expression changes between gene levels and exon levels, implying a much complex landscape of transcript-specific differential expressions in HCC. Finally, we identified a novel highly up-regulated exon-exon junction in ATAD2 gene in HCC tissues. Overall, to our best knowledge, our study represents the most comprehensive characterization of the HBV-related HCC transcriptome including exon level expression changes and novel splicing variants, which illustrated the power of RNA-seq and provided important clues for understanding the molecular mechanisms of HCC pathogenesis at system-wide levels.
The key study contents:
(1) evaluation of data quality and sequencing depth required for transcriptome analysis.
(2) Comparison between RNA-seq and microarray.
(3) The analysis and validation of differential gene expression.
(4) The chromosome locations of DEGs.
(5) Functional annotation of DEGs.
(6) Exon expression level analysis.
(7) Identification of novel DE exon-exon junctions.
The key study results:
(1) For the first time the RNA-seq analysis of HBV-related HCC.
(2) 1,378 significantly DEGs and 24, 338 DEEs were identified.
(3) 54 bio-function terms and 41 canonical pathways related to HCC.
(4) Many of chromosomal aberrations were identified, especially 8q24.
(5) Some splicing patterns were identified.
(6) A novel-splicing variant in ATAD2 was identified.
Conclusion:
Overall, by RNA-seq our study represents the most comprehensive characterization of the HBV-related HCC transcriptome including gene level expression changes, exon level expression changes and novel splicing variants, which provided important clues for understanding the molecular mechanisms of HCC pathogenesis at system-wide levels.
引文
1.But DYK, Lai CL, Yuen MF. Natural history of hepatitis-related hepatocellular carcinoma. World journal of gastroenterology: WJG 2008. 14(11): 1652.
2.Patil MA, Gütgemann I, Zhang J, et al. Array-based comparative genomic hybridization reveals recurrent chromosomal aberrations and Jab1 as a potential target for 8q gain in hepatocellular carcinoma. Carcinogenesis 2005. 26(12): 2050.
3.Xu XR, Huang J, Xu ZG, et al. Insight into hepatocellular carcinogenesis at transcriptome level by comparing gene expression profiles of hepatocellular carcinoma with those of corresponding noncancerous liver. Proceedings of the National Academy of Sciences of the United States of America 2001. 98(26): 15089.
4.Liang CRMY, Leow CK, Neo JCH, et al. Proteome analysis of human hepatocellular carcinoma tissues by two‐dimensional difference gel electrophoresis and mass spectrometry. Proteomics 2005. 5(8): 2258-2271.
5.Nam SW, Park JY, Ramasamy A, et al. Molecular changes from dysplastic nodule to hepatocellular carcinoma through gene expression profiling. Hepatology 2005. 42(4): 809-818.
6.Lee JS, Chu IS, Heo J, et al. Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling. Hepatology 2004. 40(3): 667-676.
7.Iizuka N, Oka M, Yamada-Okabe H, et al. Comparison of gene expression profiles between hepatitis B virus-and hepatitis C virus-infected hepatocellular carcinoma by oligonucleotide microarray data on the basis of a supervised learning method. Cancer research 2002. 62(14): 3939.
8.Pawitan Y, Michiels S, Koscielny S, Gusnanto A, Ploner A. False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 2005. 21(13): 3017.
9.Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research 2008. 18(9): 1509.
10.Mardis ER. The impact of next-generation sequencing technology on genetics. Trends in Genetics 2008. 24(3): 133-141.
11.Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 2009. 10(1): 57-63.
12.Nagalakshmi U, Wang Z, Waern K, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008. 320(5881): 1344.
13.Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature methods 2008. 5(7): 621-628.
14.Sultan M, Schulz MH, Richard H, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008. 321(5891): 956.
15.Berger MF, Levin JZ, Vijayendran K, et al. Integrative analysis of the melanoma transcriptome. Genome research 20(4): 413.
16.Hieter P, Boguski M. Functional genomics: it's all how you read it. Science 1997. 278(5338): 601.
17.McKusick VA. Genomics: structural and functional studies of genomes. Genomics 1997. 45(2): 244-249.
18.Burley SK. An overview of structural genomics. Nature Structural & Molecular Biology 2000. 7(932-934.
19.黄琛,武明花,李小玲,李桂源.多基因遗传性肿瘤的组学对接研究.中南大学学报(医学版) 2007. 32(2)
20.Scheel J, Brevern MC, H rlein A, Fischer A, Schneider A, Bach A. Yellow pages to the transcriptome. Pharmacogenomics 2002. 3(6): 791-807.
21.Dunwell JM, Moya-LeóN MA, Herrera R. Transcriptome analysis and crop improvement:(A review). Biological Research 2001. 34(153-164.
22.Storz G. An expanding universe of noncoding RNAs. Science 2002. 296(5571): 1260.
23.Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature 2001. 409(6822): 860-921.
24.Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. science 2001. 291(5507): 1304.
25.王亚馥,戴灼华.遗传学高等教育出版社. 1999.
26.Little PFR. Structure and function of the human genome. Genome research 2005. 15(12): 1759.
27.Adams MD, Kelley JM, Gocayne JD, et al. Complementary DNA sequencing: expressedsequence tags and human genome project. Science 1991. 252(5013): 1651.
28.Wolfsberg TG, Landsman D. A comparison of expressed sequence tags (ESTs) to human genomic sequences. Nucleic acids research 1997. 25(8): 1626.
29.杨延玲.基于Z曲线理论的编码和非编码区序列分析.山东师范大学, 2008.
30.陈庆山,杨春明,刘春燕,等. RNA分子表达技术研究进展.东北农业大学学报2005. 36(2): 235-240.
31.王忠华,周美园,夏英武.表达序列标签及其应用. PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS 2001. 28(1)
32.Dear PH. Expressed‐sequence Tag (EST).
33.Boguski MS, Schuler GD. ESTablishing a human transcript map. Nature Genetics 1995. 10(4): 369-371.
34.Boguski MS, Tolstoshev CM, Bassett Jr DE. Gene discovery in dbEST. Science 1994. 265(5181): 1993-1994.
35.Gerhard DS, Wagner L, Feingold EA, et al. The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC). Genome research 2004. 14(10B): 2121.
36.Castelli V, Aury JM, Jaillon O, et al. Whole genome sequence comparisons and“full-length”cDNA sequences: A combined approach to evaluate and improve Arabidopsis genome annotation. Genome research 2004. 14(3): 406.
37.王正志,李稚锋,杭兴宜,等.适合可变剪接研究的转录组序列分析策略.国防科技大学学报2006. 28(004): 37-42.
38.丁克越,沈岩. ESTs数据分析及ESTs数据系统.国外医学:分子生物学分册2002. 24(002): 113-116.
39.Ma HM, Schulze S, Lee S, et al. An EST survey of the sugarcane transcriptome. TAG Theoretical and Applied Genetics 2004. 108(5): 851-863.
40.陈作舟. EST转录组功能差异的数据挖掘系统及应用.浙江大学, 2007.
41.Mu X, Zhao S, Pershad R, et al. Gene expression in the developing mouse retina by EST sequencing and microarray analysis. Nucleic acids research 2001. 29(24): 4983.
42.van Ruissen F, Baas F. Serial analysis of gene expression (SAGE). METHODS IN MOLECULAR BIOLOGY-CLIFTON THEN TOTOWA- 2007. 383(41.
43.Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression. Science 1995. 270(5235): 484.
44.Zhang L, Zhou W, Velculescu VE, et al. Gene expression profiles in normal and cancer cells. Science 1997. 276(5316): 1268.
45.Saha S, Sparks AB, Rago C, et al. Using the transcriptome to annotate the genome. Nature biotechnology 2002. 20(5): 508-512.
46.Van Kampen AHC, Van Schaik BDC, Pauws E, et al. USAGE: a web-based approach towards the analysis of SAGE data. Bioinformatics 2000. 16(10): 899.
47.Ruijter JM, Van Kampen AHC, Baas F. Statistical evaluation of SAGE libraries: consequences for experimental design. Physiological genomics 2002. 11(2): 37.
48.Baggerly KA, Deng L, Morris JS, Aldaz CM. Overdispersed logistic regression for SAGE: modelling multiple groups and covariates. BMC bioinformatics 2004. 5(1): 144.
49.Man MZ, Wang X, Wang Y. POWER_SAGE: comparing statistical tests for SAGE experiments. Bioinformatics 2000. 16(11): 953.
50.Margulies EH, Innis JW. eSAGE: managing and analysing data generated with serial analysis of gene expression (SAGE). Bioinformatics 2000. 16(7): 650-651.
51.Morris JS, Baggerly KA, Coombes KR. Bayesian shrinkage estimation of the relative abundance of mRNA transcripts using SAGE. Biometrics 2003. 59(3): 476-486.
52.Galante PAF, Trimarchi J, Cepko CL, De Souza SJ, Ohno-Machado L, Kuo WP. Automatic correspondence of tags and genes (ACTG): a tool for the analysis of SAGE, MPSS and SBS data. Bioinformatics 2007. 23(7): 903.
53.Brenner S, Johnson M, Bridgham J, et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nature biotechnology 2000. 18(6): 630-634.
54.Brenner S, Williams SR, Vermaas EH, et al. In vitro cloning of complex mixtures of DNA on microbeads: physical separation of differentially expressed cDNAs. Proceedings of the National Academy of Sciences of the United States of America 2000. 97(4): 1665.
55.Reinartz J, Bruyns E, Lin JZ, et al. Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms. Briefings in functional genomics & proteomics 2002. 1(1): 95.
56.Fodor SP, Read JL, Pirrung MC, Stryer L, Lu AT, Solas D. Light-directed, spatially addressable parallel chemical synthesis. Science 1991. 251(4995): 767.
57.Pease AC, Solas D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SP. Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proceedings of the National Academy of Sciences of the United States of America 1994. 91(11): 5022.
58.Davis N, Favero T, de HoogС. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995. 270(467.
59.Lipshutz RJ, Fodor SPA, Gingeras TR, Lockhart DJ. High density synthetic oligonucleotide arrays. Nature genetics 1999. 21(Suppl 1): 20-24.
60.Schena M, Shalon D, Heller R, Chai A, Brown PO, Davis RW. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proceedings of the National Academy of Sciences of the United States of America 1996. 93(20): 10614.
61.Schummer M, Ng WI, Nelson PS, Bumgarner RE, Hood L. Inexpensive handheld device for the construction of high-density nucleic acid arrays. Biotechniques 1997. 23(6): 1087-1092.
62.Gunderson KL, Huang XC, Morris MS, Lipshutz RJ, Lockhart DJ, Chee MS. Mutation detection by ligation to complete n-mer DNA arrays. Genome research 1998. 8(11): 1142.
63.Kapranov P, Cawley SE, Drenkow J, et al. Large-scale transcriptional activity in chromosomes 21 and 22. Science 2002. 296(5569): 916.
64.Royce TE, Rozowsky JS, Bertone P, et al. Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping. Trends in Genetics 2005. 21(8): 466-475.
65.Li L, Wang X, Stolc V, et al. Genome-wide transcription analyses in rice using tiling microarrays. Nature genetics 2005. 38(1): 124-129.
66.Martienssen RA, Doerge RW, Colot V. Epigenomic mapping in Arabidopsis using tiling microarrays. Chromosome Research 2005. 13(3): 299-308.
67.王立强,倪旭翔,陆祖康,李映笙,郑旭峰.激光共聚焦生物芯片扫描仪的研究.浙江大学学报(工学版) 2005. 39(4)
68.张帆,薛利军,李自田,吴小华.基于CCD的生物芯片扫描分析系统的设计.光子学报2006. 35(004): 565-568.
69.Baggerly KA, Coombes KR, Hess KR, Stivers DN, Abruzzo LV, Zhang W. Identifying differentially expressed genes in cDNA microarray experiments. Journal of Computational Biology 2001. 8(6): 639-659.
70.Towsend K, Trevino V, Falciani F, Stewart PM, Hewison M, Campbell MJ. Identificationof VDR-responsive gene signatures in breast cancer cells. Oncology 2007. 71(1-2): 111-123.
71.Smyth GK, Yang YH, Speed T. Statistical issues in cDNA microarray data analysis. METHODS IN MOLECULAR BIOLOGY-CLIFTON THEN TOTOWA- 2003. 224(111-136.
72.Larsson O, Wahlestedt C, Timmons JA. Considerations when using the significance analysis of microarrays(SAM) algorithm. BMC bioinformatics 2005. 6(1): 129.
73.Makretsov NA, Huntsman DG, Nielsen TO, et al. Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma. Clinical cancer research 2004. 10(18): 6143.
74.Chen X. Curve-based clustering of time course gene expression data using self-organizing maps. J Bioinform Comput Biol. v7 i4: 645-661.
75.Raychaudhuri S, Stuart JM, Altman RB. Principal components analysis to summarize microarray experiments: application to sporulation time series. pp. 455: NIH Public Access, 2000.
76.Chen T, Tsai TH, Chen YT, et al. A combined K-means and hierarchical clustering method for improving the clustering efficiency of microarray. pp. 405-408: IEEE, 2005.
77.Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000. 16(10): 906.
78.Guo Y, Hastie T, Tibshirani R. Regularized linear discriminant analysis and its application in microarrays. Biostatistics 2007. 8(1): 86.
79.Tritchler D, Parkhomenko E, Beyene J. Filtering genes for cluster and network analysis. BMC bioinformatics 2009. 10(1): 193.
80.Cheng J, Sun S, Tracy A, et al. NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis. Bioinformatics 2004. 20(9): 1462.
81.Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic acids research 2000. 28(1): 27.
82.Rapaport F, Zinovyev A, Dutreix M, Barillot E, Vert JP. Classification of microarray data using gene networks. BMC bioinformatics 2007. 8(1): 35.
83.王升启.基因芯片技术及应用研究进展[J].生物工程进展1999. 19(4): 45-51.
84.王全军.毒理芯片技术在药物毒理学中的应用.国外医学:药学分册2003. 30(002): 110-113.
85.Willenbrock H, Salomon J, S kilde R, et al. Quantitative miRNA expression analysis: Comparing microarrays with next-generation sequencing. Rna 2009. 15(11): 2028.
86.Morozova O, Marra MA. Applications of next-generation sequencing technologies in functional genomics. Genomics 2008. 92(5): 255-264.
87.Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences of the United States of America 1977. 74(12): 5463.
88.Margulies M, Egholm M, Altman WE, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005. 437(7057): 376-380.
89.Wheeler DA, Srinivasan M, Egholm M, et al. The complete genome of an individual by massively parallel DNA sequencing. Nature 2008. 452(7189): 872-876.
90.Fredriksson S, Banér J, Dahl F, et al. Multiplex amplification of all coding sequences within 10 cancer genes by Gene-Collector. Nucleic Acids Research 2007. 35(7): e47.
91.Morin RD, O’Connor MD, Griffith M, et al. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome research 2008. 18(4): 610.
92.Jothi R, Cuddapah S, Barski A, Cui K, Zhao K. Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data. Nucleic Acids Research 2008. 36(16): 5221.
93.Bainbridge MN, Wang M, Burgess DL, et al. Whole exome capture in solution with 3 Gbp of data. Genome biology 11(6): R62.
94.Ng SB, Buckingham KJ, Lee C, et al. Exome sequencing identifies the cause of a mendelian disorder. Nature genetics 2009. 42(1): 30-35.
95.Bilgüvar K, ztürk AK, Louvi A, et al. Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations. Nature 467(7312): 207-210.
96.Ng SB, Bigham AW, Buckingham KJ, et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nature genetics 42(9): 790-793.
97.Musunuru K, Pirruccello JP, Do R, et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. New England Journal of Medicine 363(23): 2220-2227.
98.Taylor KH, Kramer RS, Davis JW, et al. Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer research 2007. 67(18): 8511.
99.Pomraning KR, Smith KM, Freitag M. Genome-wide high throughput analysis of DNA methylation in eukaryotes. Methods 2009. 47(3): 142-150.
100.Shendure J, Ji H. Next-generation DNA sequencing. Nature biotechnology 2008. 26(10): 1135-1145.
101.Ruparel H, Bi L, Li Z, et al. Design and synthesis of a 3′-O-allyl photocleavable fluorescent nucleotide as a reversible terminator for DNA sequencing by synthesis. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(17): 5932.
102.Seo TS, Bai X, Kim DH, et al. Four-color DNA sequencing by synthesis on a chip using photocleavable fluorescent nucleotides. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(17): 5926.
103.Ju J, Kim DH, Bi L, et al. Four-color DNA sequencing by synthesis using cleavable fluorescent nucleotide reversible terminators. Proceedings of the National Academy of Sciences 2006. 103(52): 19635.
104.Fedurco M, Romieu A, Williams S, Lawrence I, Turcatti G. BTA, a novel reagent for DNA attachment on glass and efficient generation of solid-phase amplified DNA colonies. Nucleic acids research 2006. 34(3): e22.
105.Shendure JA, Porreca GJ, Church GM. Overview of DNA sequencing strategies. Curr Protoc Mol Biol 2008. 7(
106.Mardis ER. Next-generation DNA sequencing methods. Annual review of genomics and human genetics 2008. 9(1): 387.
107.Fuller CW, Middendorf LR, Benner SA, et al. The challenges of sequencing by synthesis. Nature biotechnology 2009. 27(11): 1013-1023.
108.Croucher NJ, Fookes MC, Perkins TT, et al. A simple method for directional transcriptome sequencing using Illumina technology. Nucleic acids research 2009.
109.Parkhomchuk D, Borodina T, Amstislavskiy V, et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic acids research 2009. 37(18): e123.
110.Perkins TT, Kingsley RA, Fookes MC, et al. A strand-specific RNA-Seq analysis of the
98.Taylor KH, Kramer RS, Davis JW, et al. Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer research 2007. 67(18): 8511.
99.Pomraning KR, Smith KM, Freitag M. Genome-wide high throughput analysis of DNA methylation in eukaryotes. Methods 2009. 47(3): 142-150.
100.Shendure J, Ji H. Next-generation DNA sequencing. Nature biotechnology 2008. 26(10): 1135-1145.
101.Ruparel H, Bi L, Li Z, et al. Design and synthesis of a 3′-O-allyl photocleavable fluorescent nucleotide as a reversible terminator for DNA sequencing by synthesis. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(17): 5932.
102.Seo TS, Bai X, Kim DH, et al. Four-color DNA sequencing by synthesis on a chip using photocleavable fluorescent nucleotides. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(17): 5926.
103.Ju J, Kim DH, Bi L, et al. Four-color DNA sequencing by synthesis using cleavable fluorescent nucleotide reversible terminators. Proceedings of the National Academy of Sciences 2006. 103(52): 19635.
104.Fedurco M, Romieu A, Williams S, Lawrence I, Turcatti G. BTA, a novel reagent for DNA attachment on glass and efficient generation of solid-phase amplified DNA colonies. Nucleic acids research 2006. 34(3): e22.
105.Shendure JA, Porreca GJ, Church GM. Overview of DNA sequencing strategies. Curr Protoc Mol Biol 2008. 7(
106.Mardis ER. Next-generation DNA sequencing methods. Annual review of genomics and human genetics 2008. 9(1): 387.
107.Fuller CW, Middendorf LR, Benner SA, et al. The challenges of sequencing by synthesis. Nature biotechnology 2009. 27(11): 1013-1023.
108.Croucher NJ, Fookes MC, Perkins TT, et al. A simple method for directional transcriptome sequencing using Illumina technology. Nucleic acids research 2009.
109.Parkhomchuk D, Borodina T, Amstislavskiy V, et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic acids research 2009. 37(18): e123.
110.Perkins TT, Kingsley RA, Fookes MC, et al. A strand-specific RNA-Seq analysis of the
125.Jiang H, Wong WH. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics 2009. 25(8): 1026.
126.Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26(1): 136.
127.Trapnell C, Williams BA, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature biotechnology 28(5): 511-515.
128.Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nature genetics 2008. 40(12): 1413-1415.
129.Wang L, Xi Y, Yu J, Dong L, Yen L, Li W. A statistical method for the detection of alternative splicing using RNA-seq. PloS one 5(1): 2141-2144.
130.Blekhman R, Marioni JC, Zumbo P, Stephens M, Gilad Y. Sex-specific and lineage-specific alternative splicing in primates. Genome research 20(2): 180.
131.Feng J, Li W, Jiang T. Inference of isoforms from short sequence reads. pp. 138-157: Springer.
132.Hiller D, Jiang H, Xu W, Wong WH. Identifiability of isoform deconvolution from junction arrays and RNA-Seq. Bioinformatics 2009. 25(23): 3056.
133.Nix DA, Courdy SJ, Boucher KM. Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks. BMC bioinformatics 2008. 9(1): 523.
134.Li H, Handsaker B, Wysoker A, et al. The sequence alignment/map format and SAMtools. Bioinformatics 2009. 25(16): 2078.
135.Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26(6): 841.
136.Birzele F, Schaub J, Rust W, et al. Into the unknown: expression profiling without genome sequence information in CHO by next generation sequencing. Nucleic Acids Research 38(12): 3999.
137.Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome research 2008. 18(5): 821.
138.Birol I, Jackman SD, Nielsen CB, et al. De novo transcriptome assembly with ABySS. Bioinformatics 2009. 25(21): 2872.
139.Li JB, Levanon EY, Yoon JK, et al. Genome-wide identification of human RNA editingsites by parallel DNA capturing and sequencing. Science 2009. 324(5931): 1210.
140.Levin JZ, Berger MF, Adiconis X, et al. Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts. Genome biology 2009. 10(10): R115.
141.Croucher NJ, Fookes MC, Perkins TT, et al. A simple method for directional transcriptome sequencing using Illumina technology. Nucleic acids research 2009. 37(22): e148.
142.Ashburner M, Ball CA, Blake JA, et al. Gene Ontology: tool for the unification of biology. Nature genetics 2000. 25(1): 25-29.
143.Zeeberg BR, Feng W, Wang G, et al. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 2003. 4(4): R28.
144.Dennis Jr G, Sherman BT, Hosack DA, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 2003. 4(5): P3.
145.Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology 11(2): R14.
146.Hu Z, Hung JH, Wang Y, et al. VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic acids research 2009. 37(suppl 2): W115.
147.Eid J, Fehr A, Gray J, et al. Real-time DNA sequencing from single polymerase molecules. Science 2009. 323(5910): 133.
148.Rusk N. Cheap third-generation sequencing. Nature Methods 2009. 6(4): 244-244.
149.Braslavsky I, Hebert B, Kartalov E, Quake SR. Sequence information can be obtained from single DNA molecules. Proceedings of the National Academy of Sciences of the United States of America 2003. 100(7): 3960.
150.Harris TD, Buzby PR, Babcock H, et al. Single-molecule DNA sequencing of a viral genome. Science 2008. 320(5872): 106.
151.Pushkarev D, Neff NF, Quake SR. Single-molecule sequencing of an individual human genome. Nature biotechnology 2009. 27(9): 847-850.
152.Korlach J, Marks PJ, Cicero RL, et al. Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in zero-mode waveguide nanostructures. Proceedings of the National Academy of Sciences 2008. 105(4): 1176.
153.Flusberg BA, Webster DR, Lee JH, et al. Direct detection of DNA methylation duringsingle-molecule, real-time sequencing. Nature methods 7(6): 461-465.
154.Metzker ML. Sequencing technologies—the next generation. Nature Reviews Genetics 2009. 11(1): 31-46.
155.Tsutsui M, Taniguchi M, Yokota K, Kawai T. Identifying single nucleotides by tunnelling current. Nature nanotechnology 5(4): 286-290.
156.Clarke J, Wu HC, Jayasinghe L, Patel A, Reid S, Bayley H. Continuous base identification for single-molecule nanopore DNA sequencing. Nature nanotechnology 2009. 4(4): 265-270.
157.Branton D, Deamer DW, Marziali A, et al. The potential and challenges of nanopore sequencing. Nature biotechnology 2008. 26(10): 1146-1153.
158.Simonetti RG, CammàC, Fiorello F, Politi F, D'Amico G, Pagliaro L. Hepatocellular carcinoma. Digestive diseases and sciences 1991. 36(7): 962-972.
159.陈建国,宋新明.中国肝癌发病水平的估算及分析.中国肿瘤2005. 14(1): 28-31.
160.董志伟,乔友林,李连弟,等.中国癌症控制策略研究报告.中国肿瘤2002. 11(5): 250-260.
161.孟炜,陆鸿雁,蔡如琳, et al.原发性肝癌的遗传流行病学研究.中华流行病学杂志2002. 23(6): 438-440.
162.Dulbecco R. A turning point in cancer research: sequencing the human genome. Science 1986. 231(4742): 1055.
163.Campbell PJ, Stephens PJ, Pleasance ED, et al. Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nature genetics 2008. 40(6): 722-729.
164.Yoon SY, Kim JM, Oh JH, et al. Gene expression profiling of human HBV-and/or HCV-associated hepatocellular carcinoma cells using expressed sequence tags. International journal of oncology 2006. 29(2): 315-327.
165.Wang G, Zhao Y, Liu X, et al. Allelic loss and gain, but not genomic instability, as the major somatic mutation in primary hepatocellular carcinoma. Genes, Chromosomes and Cancer 2001. 31(3): 221-227.
166.Zhao X, Li J, He Y, et al. A novel growth suppressor gene on chromosome 17p13. 3 with a high frequency of mutation in human hepatocellular carcinoma. Cancer research 2001. 61(20): 7383.
167.Schultz DC, Vanderveer L, Berman DB, Hamilton TC, Wong AJ, Godwin AK.Identification of two candidate tumor suppressor genes on chromosome 17p13. 3. Cancer research 1996. 56(9): 1997.
168.Huang J, Sheng HH, Shen T, et al. Correlation between genomic DNA copy number alterations and transcriptional expression in hepatitis B virus-associated hepatocellular carcinoma. FEBS letters 2006. 580(15): 3571-3581.
169.Midorikawa Y, Yamamoto S, Ishikawa S, et al. Molecular karyotyping of human hepatocellular carcinoma using single-nucleotide polymorphism arrays. Oncogene 2006. 25(40): 5581-5590.
170.Jongeneel CV, Delorenzi M, Iseli C, et al. An atlas of human gene expression from massively parallel signature sequencing (MPSS). Genome research 2005. 15(7): 1007.
171.Sugarbaker DJ, Richards WG, Gordon GJ, et al. Transcriptome sequencing of malignant pleural mesothelioma tumors. Proceedings of the National Academy of Sciences 2008. 105(9): 3521.
172.Pfaffl MW. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic acids research 2001. 29(9): e45.
173.Nakatsura T, Yoshitake Y, Senju S, et al. Glypican-3, overexpressed specifically in human hepatocellular carcinoma, is a novel tumor marker* 1. Biochemical and biophysical research communications 2003. 306(1): 16-25.
174.Ohmachi Y, Murata A, Yasuda T, et al. Specific expression of the pancreatic‐secretory‐trypsin‐inhibitor (PSTI) gene in hepatocellular carcinoma. International journal of cancer 1993. 55(5): 728-734.
175.Kang YH, Ji NY, Lee CI, et al. ESM-1 silencing decreased cell survival, migration, and invasion and modulated cell cycle progression in hepatocellular carcinoma. Amino Acids: 1-11.
176.Yang JD, Roberts LR. Hepatocellular carcinoma: a global view. Nature Reviews Gastroenterology and Hepatology 7(8): 448-458.
177.Morgan ET. Down-regulation of multiple cytochrome p450 gene products by inflammatory mediators in vivo:: Independence from the hypothalamo-pituitary axis. Biochemical pharmacology 1993. 45(2): 415-419.
178.Yeligar S, Tsukamoto H, Kalra VK. Ethanol-Induced Expression of ET-1 and ET-BR in Liver Sinusoidal Endothelial Cells and Human Endothelial Cells Involves Hypoxia-Inducible Factor-1αand MicroRNA-199. The Journal of Immunology 2009.183(8): 5232.
179.Chan KYY, Lai PBS, Squire JA, et al. Positional expression profiling indicates candidate genes in deletion hotspots of hepatocellular carcinoma. Modern pathology 2006. 19(12): 1546-1554.
180.Schmidt EV. The role of c-myc in cellular growth control. Oncogene 1999. 18(19): 2988.
181.Payton M, Coats S. Cyclin E2, the cycle continues. The International Journal of Biochemistry & Cell Biology 2002. 34(4): 315-320.
182.Wang X, Narayanan M, Bruey JM, et al. Protective role of Cop in Rip2/caspase-1/caspase-4-mediated HeLa cell death. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 2006. 1762(8): 742-754.
183.Wong N, Chan A, Lee SW, et al. Positional mapping for amplified DNA sequences on 1q21-q22 in hepatocellular carcinoma indicates candidate genes over-expression. Journal of Hepatology 2003. 38(3): 298-306.
184.Nurse P. Universal control mechanism regulating onset of M-phase. Nature 1990. 344(503-508.
185.Krens SF, Spaink HP, Snaar-Jagalska BE. Functions of the MAPK family in vertebrate-development. FEBS letters 2006. 580(21): 4984-4990.
186.Venables JP, Klinck R, Koh CS, et al. Cancer-associated regulation of alternative splicing. Nature structural & molecular biology 2009. 16(6): 670-676.
187.Bemmo A, Benovoy D, Kwan T, Gaffney DJ, Jensen RV, Majewski J. Gene expression and isoform variation analysis using Affymetrix Exon Arrays. BMC genomics 2008. 9(1): 529.
188.CiróM, Prosperini E, Quarto M, et al. ATAD2 is a novel cofactor for MYC, overexpressed and amplified in aggressive tumors. Cancer Research 2009. 69(21): 8491.
189.Zou JX, Guo L, Revenko AS, et al. Androgen-induced coactivator ANCCA mediates specific androgen receptor signaling in prostate cancer. Cancer research 2009. 69(8): 3339.
190.Mo F, Hong X, Gao F, et al. A compatible exon-exon junction database for the identification of exon skipping events using tandem mass spectrum data. BMC bioinformatics 2008. 9(1): 537.
191.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1): 139.
192.Burchard J, Zhang C, Liu AM, et al. microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma. Molecular Systems Biology 6(1)
193.De Preter K, Barriot R, Speleman F, Vandesompele J, Moreau Y. Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions. Nucleic Acids Research 2008. 36(7): e43.
194.Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(43): 15545.
195.De Bona F, Ossowski S, Schneeberger K, R tsch G. Optimal spliced alignments of short sequence reads. BMC Bioinformatics 2008. 9(Suppl 10): O7.
196.Au KF, Jiang H, Lin L, Xing Y, Wong WH. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Research 38(14): 4570.
197.Wang K, Singh D, Zeng Z, et al. MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Research 38(18): e178.
198.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-[Delta][Delta] CT method. Methods 2001. 25(4): 402-408.
2.Patil MA, Gütgemann I, Zhang J, et al. Array-based comparative genomic hybridization reveals recurrent chromosomal aberrations and Jab1 as a potential target for 8q gain in hepatocellular carcinoma. Carcinogenesis 2005. 26(12): 2050.
3.Xu XR, Huang J, Xu ZG, et al. Insight into hepatocellular carcinogenesis at transcriptome level by comparing gene expression profiles of hepatocellular carcinoma with those of corresponding noncancerous liver. Proceedings of the National Academy of Sciences of the United States of America 2001. 98(26): 15089.
4.Liang CRMY, Leow CK, Neo JCH, et al. Proteome analysis of human hepatocellular carcinoma tissues by two‐dimensional difference gel electrophoresis and mass spectrometry. Proteomics 2005. 5(8): 2258-2271.
5.Nam SW, Park JY, Ramasamy A, et al. Molecular changes from dysplastic nodule to hepatocellular carcinoma through gene expression profiling. Hepatology 2005. 42(4): 809-818.
6.Lee JS, Chu IS, Heo J, et al. Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling. Hepatology 2004. 40(3): 667-676.
7.Iizuka N, Oka M, Yamada-Okabe H, et al. Comparison of gene expression profiles between hepatitis B virus-and hepatitis C virus-infected hepatocellular carcinoma by oligonucleotide microarray data on the basis of a supervised learning method. Cancer research 2002. 62(14): 3939.
8.Pawitan Y, Michiels S, Koscielny S, Gusnanto A, Ploner A. False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 2005. 21(13): 3017.
9.Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research 2008. 18(9): 1509.
10.Mardis ER. The impact of next-generation sequencing technology on genetics. Trends in Genetics 2008. 24(3): 133-141.
11.Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 2009. 10(1): 57-63.
12.Nagalakshmi U, Wang Z, Waern K, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008. 320(5881): 1344.
13.Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature methods 2008. 5(7): 621-628.
14.Sultan M, Schulz MH, Richard H, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008. 321(5891): 956.
15.Berger MF, Levin JZ, Vijayendran K, et al. Integrative analysis of the melanoma transcriptome. Genome research 20(4): 413.
16.Hieter P, Boguski M. Functional genomics: it's all how you read it. Science 1997. 278(5338): 601.
17.McKusick VA. Genomics: structural and functional studies of genomes. Genomics 1997. 45(2): 244-249.
18.Burley SK. An overview of structural genomics. Nature Structural & Molecular Biology 2000. 7(932-934.
19.黄琛,武明花,李小玲,李桂源.多基因遗传性肿瘤的组学对接研究.中南大学学报(医学版) 2007. 32(2)
20.Scheel J, Brevern MC, H rlein A, Fischer A, Schneider A, Bach A. Yellow pages to the transcriptome. Pharmacogenomics 2002. 3(6): 791-807.
21.Dunwell JM, Moya-LeóN MA, Herrera R. Transcriptome analysis and crop improvement:(A review). Biological Research 2001. 34(153-164.
22.Storz G. An expanding universe of noncoding RNAs. Science 2002. 296(5571): 1260.
23.Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome. Nature 2001. 409(6822): 860-921.
24.Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. science 2001. 291(5507): 1304.
25.王亚馥,戴灼华.遗传学高等教育出版社. 1999.
26.Little PFR. Structure and function of the human genome. Genome research 2005. 15(12): 1759.
27.Adams MD, Kelley JM, Gocayne JD, et al. Complementary DNA sequencing: expressedsequence tags and human genome project. Science 1991. 252(5013): 1651.
28.Wolfsberg TG, Landsman D. A comparison of expressed sequence tags (ESTs) to human genomic sequences. Nucleic acids research 1997. 25(8): 1626.
29.杨延玲.基于Z曲线理论的编码和非编码区序列分析.山东师范大学, 2008.
30.陈庆山,杨春明,刘春燕,等. RNA分子表达技术研究进展.东北农业大学学报2005. 36(2): 235-240.
31.王忠华,周美园,夏英武.表达序列标签及其应用. PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS 2001. 28(1)
32.Dear PH. Expressed‐sequence Tag (EST).
33.Boguski MS, Schuler GD. ESTablishing a human transcript map. Nature Genetics 1995. 10(4): 369-371.
34.Boguski MS, Tolstoshev CM, Bassett Jr DE. Gene discovery in dbEST. Science 1994. 265(5181): 1993-1994.
35.Gerhard DS, Wagner L, Feingold EA, et al. The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC). Genome research 2004. 14(10B): 2121.
36.Castelli V, Aury JM, Jaillon O, et al. Whole genome sequence comparisons and“full-length”cDNA sequences: A combined approach to evaluate and improve Arabidopsis genome annotation. Genome research 2004. 14(3): 406.
37.王正志,李稚锋,杭兴宜,等.适合可变剪接研究的转录组序列分析策略.国防科技大学学报2006. 28(004): 37-42.
38.丁克越,沈岩. ESTs数据分析及ESTs数据系统.国外医学:分子生物学分册2002. 24(002): 113-116.
39.Ma HM, Schulze S, Lee S, et al. An EST survey of the sugarcane transcriptome. TAG Theoretical and Applied Genetics 2004. 108(5): 851-863.
40.陈作舟. EST转录组功能差异的数据挖掘系统及应用.浙江大学, 2007.
41.Mu X, Zhao S, Pershad R, et al. Gene expression in the developing mouse retina by EST sequencing and microarray analysis. Nucleic acids research 2001. 29(24): 4983.
42.van Ruissen F, Baas F. Serial analysis of gene expression (SAGE). METHODS IN MOLECULAR BIOLOGY-CLIFTON THEN TOTOWA- 2007. 383(41.
43.Velculescu VE, Zhang L, Vogelstein B, Kinzler KW. Serial analysis of gene expression. Science 1995. 270(5235): 484.
44.Zhang L, Zhou W, Velculescu VE, et al. Gene expression profiles in normal and cancer cells. Science 1997. 276(5316): 1268.
45.Saha S, Sparks AB, Rago C, et al. Using the transcriptome to annotate the genome. Nature biotechnology 2002. 20(5): 508-512.
46.Van Kampen AHC, Van Schaik BDC, Pauws E, et al. USAGE: a web-based approach towards the analysis of SAGE data. Bioinformatics 2000. 16(10): 899.
47.Ruijter JM, Van Kampen AHC, Baas F. Statistical evaluation of SAGE libraries: consequences for experimental design. Physiological genomics 2002. 11(2): 37.
48.Baggerly KA, Deng L, Morris JS, Aldaz CM. Overdispersed logistic regression for SAGE: modelling multiple groups and covariates. BMC bioinformatics 2004. 5(1): 144.
49.Man MZ, Wang X, Wang Y. POWER_SAGE: comparing statistical tests for SAGE experiments. Bioinformatics 2000. 16(11): 953.
50.Margulies EH, Innis JW. eSAGE: managing and analysing data generated with serial analysis of gene expression (SAGE). Bioinformatics 2000. 16(7): 650-651.
51.Morris JS, Baggerly KA, Coombes KR. Bayesian shrinkage estimation of the relative abundance of mRNA transcripts using SAGE. Biometrics 2003. 59(3): 476-486.
52.Galante PAF, Trimarchi J, Cepko CL, De Souza SJ, Ohno-Machado L, Kuo WP. Automatic correspondence of tags and genes (ACTG): a tool for the analysis of SAGE, MPSS and SBS data. Bioinformatics 2007. 23(7): 903.
53.Brenner S, Johnson M, Bridgham J, et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nature biotechnology 2000. 18(6): 630-634.
54.Brenner S, Williams SR, Vermaas EH, et al. In vitro cloning of complex mixtures of DNA on microbeads: physical separation of differentially expressed cDNAs. Proceedings of the National Academy of Sciences of the United States of America 2000. 97(4): 1665.
55.Reinartz J, Bruyns E, Lin JZ, et al. Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms. Briefings in functional genomics & proteomics 2002. 1(1): 95.
56.Fodor SP, Read JL, Pirrung MC, Stryer L, Lu AT, Solas D. Light-directed, spatially addressable parallel chemical synthesis. Science 1991. 251(4995): 767.
57.Pease AC, Solas D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SP. Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proceedings of the National Academy of Sciences of the United States of America 1994. 91(11): 5022.
58.Davis N, Favero T, de HoogС. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995. 270(467.
59.Lipshutz RJ, Fodor SPA, Gingeras TR, Lockhart DJ. High density synthetic oligonucleotide arrays. Nature genetics 1999. 21(Suppl 1): 20-24.
60.Schena M, Shalon D, Heller R, Chai A, Brown PO, Davis RW. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proceedings of the National Academy of Sciences of the United States of America 1996. 93(20): 10614.
61.Schummer M, Ng WI, Nelson PS, Bumgarner RE, Hood L. Inexpensive handheld device for the construction of high-density nucleic acid arrays. Biotechniques 1997. 23(6): 1087-1092.
62.Gunderson KL, Huang XC, Morris MS, Lipshutz RJ, Lockhart DJ, Chee MS. Mutation detection by ligation to complete n-mer DNA arrays. Genome research 1998. 8(11): 1142.
63.Kapranov P, Cawley SE, Drenkow J, et al. Large-scale transcriptional activity in chromosomes 21 and 22. Science 2002. 296(5569): 916.
64.Royce TE, Rozowsky JS, Bertone P, et al. Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping. Trends in Genetics 2005. 21(8): 466-475.
65.Li L, Wang X, Stolc V, et al. Genome-wide transcription analyses in rice using tiling microarrays. Nature genetics 2005. 38(1): 124-129.
66.Martienssen RA, Doerge RW, Colot V. Epigenomic mapping in Arabidopsis using tiling microarrays. Chromosome Research 2005. 13(3): 299-308.
67.王立强,倪旭翔,陆祖康,李映笙,郑旭峰.激光共聚焦生物芯片扫描仪的研究.浙江大学学报(工学版) 2005. 39(4)
68.张帆,薛利军,李自田,吴小华.基于CCD的生物芯片扫描分析系统的设计.光子学报2006. 35(004): 565-568.
69.Baggerly KA, Coombes KR, Hess KR, Stivers DN, Abruzzo LV, Zhang W. Identifying differentially expressed genes in cDNA microarray experiments. Journal of Computational Biology 2001. 8(6): 639-659.
70.Towsend K, Trevino V, Falciani F, Stewart PM, Hewison M, Campbell MJ. Identificationof VDR-responsive gene signatures in breast cancer cells. Oncology 2007. 71(1-2): 111-123.
71.Smyth GK, Yang YH, Speed T. Statistical issues in cDNA microarray data analysis. METHODS IN MOLECULAR BIOLOGY-CLIFTON THEN TOTOWA- 2003. 224(111-136.
72.Larsson O, Wahlestedt C, Timmons JA. Considerations when using the significance analysis of microarrays(SAM) algorithm. BMC bioinformatics 2005. 6(1): 129.
73.Makretsov NA, Huntsman DG, Nielsen TO, et al. Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma. Clinical cancer research 2004. 10(18): 6143.
74.Chen X. Curve-based clustering of time course gene expression data using self-organizing maps. J Bioinform Comput Biol. v7 i4: 645-661.
75.Raychaudhuri S, Stuart JM, Altman RB. Principal components analysis to summarize microarray experiments: application to sporulation time series. pp. 455: NIH Public Access, 2000.
76.Chen T, Tsai TH, Chen YT, et al. A combined K-means and hierarchical clustering method for improving the clustering efficiency of microarray. pp. 405-408: IEEE, 2005.
77.Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000. 16(10): 906.
78.Guo Y, Hastie T, Tibshirani R. Regularized linear discriminant analysis and its application in microarrays. Biostatistics 2007. 8(1): 86.
79.Tritchler D, Parkhomenko E, Beyene J. Filtering genes for cluster and network analysis. BMC bioinformatics 2009. 10(1): 193.
80.Cheng J, Sun S, Tracy A, et al. NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis. Bioinformatics 2004. 20(9): 1462.
81.Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic acids research 2000. 28(1): 27.
82.Rapaport F, Zinovyev A, Dutreix M, Barillot E, Vert JP. Classification of microarray data using gene networks. BMC bioinformatics 2007. 8(1): 35.
83.王升启.基因芯片技术及应用研究进展[J].生物工程进展1999. 19(4): 45-51.
84.王全军.毒理芯片技术在药物毒理学中的应用.国外医学:药学分册2003. 30(002): 110-113.
85.Willenbrock H, Salomon J, S kilde R, et al. Quantitative miRNA expression analysis: Comparing microarrays with next-generation sequencing. Rna 2009. 15(11): 2028.
86.Morozova O, Marra MA. Applications of next-generation sequencing technologies in functional genomics. Genomics 2008. 92(5): 255-264.
87.Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences of the United States of America 1977. 74(12): 5463.
88.Margulies M, Egholm M, Altman WE, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005. 437(7057): 376-380.
89.Wheeler DA, Srinivasan M, Egholm M, et al. The complete genome of an individual by massively parallel DNA sequencing. Nature 2008. 452(7189): 872-876.
90.Fredriksson S, Banér J, Dahl F, et al. Multiplex amplification of all coding sequences within 10 cancer genes by Gene-Collector. Nucleic Acids Research 2007. 35(7): e47.
91.Morin RD, O’Connor MD, Griffith M, et al. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome research 2008. 18(4): 610.
92.Jothi R, Cuddapah S, Barski A, Cui K, Zhao K. Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data. Nucleic Acids Research 2008. 36(16): 5221.
93.Bainbridge MN, Wang M, Burgess DL, et al. Whole exome capture in solution with 3 Gbp of data. Genome biology 11(6): R62.
94.Ng SB, Buckingham KJ, Lee C, et al. Exome sequencing identifies the cause of a mendelian disorder. Nature genetics 2009. 42(1): 30-35.
95.Bilgüvar K, ztürk AK, Louvi A, et al. Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations. Nature 467(7312): 207-210.
96.Ng SB, Bigham AW, Buckingham KJ, et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nature genetics 42(9): 790-793.
97.Musunuru K, Pirruccello JP, Do R, et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. New England Journal of Medicine 363(23): 2220-2227.
98.Taylor KH, Kramer RS, Davis JW, et al. Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer research 2007. 67(18): 8511.
99.Pomraning KR, Smith KM, Freitag M. Genome-wide high throughput analysis of DNA methylation in eukaryotes. Methods 2009. 47(3): 142-150.
100.Shendure J, Ji H. Next-generation DNA sequencing. Nature biotechnology 2008. 26(10): 1135-1145.
101.Ruparel H, Bi L, Li Z, et al. Design and synthesis of a 3′-O-allyl photocleavable fluorescent nucleotide as a reversible terminator for DNA sequencing by synthesis. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(17): 5932.
102.Seo TS, Bai X, Kim DH, et al. Four-color DNA sequencing by synthesis on a chip using photocleavable fluorescent nucleotides. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(17): 5926.
103.Ju J, Kim DH, Bi L, et al. Four-color DNA sequencing by synthesis using cleavable fluorescent nucleotide reversible terminators. Proceedings of the National Academy of Sciences 2006. 103(52): 19635.
104.Fedurco M, Romieu A, Williams S, Lawrence I, Turcatti G. BTA, a novel reagent for DNA attachment on glass and efficient generation of solid-phase amplified DNA colonies. Nucleic acids research 2006. 34(3): e22.
105.Shendure JA, Porreca GJ, Church GM. Overview of DNA sequencing strategies. Curr Protoc Mol Biol 2008. 7(
106.Mardis ER. Next-generation DNA sequencing methods. Annual review of genomics and human genetics 2008. 9(1): 387.
107.Fuller CW, Middendorf LR, Benner SA, et al. The challenges of sequencing by synthesis. Nature biotechnology 2009. 27(11): 1013-1023.
108.Croucher NJ, Fookes MC, Perkins TT, et al. A simple method for directional transcriptome sequencing using Illumina technology. Nucleic acids research 2009.
109.Parkhomchuk D, Borodina T, Amstislavskiy V, et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic acids research 2009. 37(18): e123.
110.Perkins TT, Kingsley RA, Fookes MC, et al. A strand-specific RNA-Seq analysis of the
98.Taylor KH, Kramer RS, Davis JW, et al. Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer research 2007. 67(18): 8511.
99.Pomraning KR, Smith KM, Freitag M. Genome-wide high throughput analysis of DNA methylation in eukaryotes. Methods 2009. 47(3): 142-150.
100.Shendure J, Ji H. Next-generation DNA sequencing. Nature biotechnology 2008. 26(10): 1135-1145.
101.Ruparel H, Bi L, Li Z, et al. Design and synthesis of a 3′-O-allyl photocleavable fluorescent nucleotide as a reversible terminator for DNA sequencing by synthesis. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(17): 5932.
102.Seo TS, Bai X, Kim DH, et al. Four-color DNA sequencing by synthesis on a chip using photocleavable fluorescent nucleotides. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(17): 5926.
103.Ju J, Kim DH, Bi L, et al. Four-color DNA sequencing by synthesis using cleavable fluorescent nucleotide reversible terminators. Proceedings of the National Academy of Sciences 2006. 103(52): 19635.
104.Fedurco M, Romieu A, Williams S, Lawrence I, Turcatti G. BTA, a novel reagent for DNA attachment on glass and efficient generation of solid-phase amplified DNA colonies. Nucleic acids research 2006. 34(3): e22.
105.Shendure JA, Porreca GJ, Church GM. Overview of DNA sequencing strategies. Curr Protoc Mol Biol 2008. 7(
106.Mardis ER. Next-generation DNA sequencing methods. Annual review of genomics and human genetics 2008. 9(1): 387.
107.Fuller CW, Middendorf LR, Benner SA, et al. The challenges of sequencing by synthesis. Nature biotechnology 2009. 27(11): 1013-1023.
108.Croucher NJ, Fookes MC, Perkins TT, et al. A simple method for directional transcriptome sequencing using Illumina technology. Nucleic acids research 2009.
109.Parkhomchuk D, Borodina T, Amstislavskiy V, et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic acids research 2009. 37(18): e123.
110.Perkins TT, Kingsley RA, Fookes MC, et al. A strand-specific RNA-Seq analysis of the
125.Jiang H, Wong WH. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics 2009. 25(8): 1026.
126.Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26(1): 136.
127.Trapnell C, Williams BA, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature biotechnology 28(5): 511-515.
128.Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nature genetics 2008. 40(12): 1413-1415.
129.Wang L, Xi Y, Yu J, Dong L, Yen L, Li W. A statistical method for the detection of alternative splicing using RNA-seq. PloS one 5(1): 2141-2144.
130.Blekhman R, Marioni JC, Zumbo P, Stephens M, Gilad Y. Sex-specific and lineage-specific alternative splicing in primates. Genome research 20(2): 180.
131.Feng J, Li W, Jiang T. Inference of isoforms from short sequence reads. pp. 138-157: Springer.
132.Hiller D, Jiang H, Xu W, Wong WH. Identifiability of isoform deconvolution from junction arrays and RNA-Seq. Bioinformatics 2009. 25(23): 3056.
133.Nix DA, Courdy SJ, Boucher KM. Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks. BMC bioinformatics 2008. 9(1): 523.
134.Li H, Handsaker B, Wysoker A, et al. The sequence alignment/map format and SAMtools. Bioinformatics 2009. 25(16): 2078.
135.Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26(6): 841.
136.Birzele F, Schaub J, Rust W, et al. Into the unknown: expression profiling without genome sequence information in CHO by next generation sequencing. Nucleic Acids Research 38(12): 3999.
137.Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome research 2008. 18(5): 821.
138.Birol I, Jackman SD, Nielsen CB, et al. De novo transcriptome assembly with ABySS. Bioinformatics 2009. 25(21): 2872.
139.Li JB, Levanon EY, Yoon JK, et al. Genome-wide identification of human RNA editingsites by parallel DNA capturing and sequencing. Science 2009. 324(5931): 1210.
140.Levin JZ, Berger MF, Adiconis X, et al. Targeted next-generation sequencing of a cancer transcriptome enhances detection of sequence variants and novel fusion transcripts. Genome biology 2009. 10(10): R115.
141.Croucher NJ, Fookes MC, Perkins TT, et al. A simple method for directional transcriptome sequencing using Illumina technology. Nucleic acids research 2009. 37(22): e148.
142.Ashburner M, Ball CA, Blake JA, et al. Gene Ontology: tool for the unification of biology. Nature genetics 2000. 25(1): 25-29.
143.Zeeberg BR, Feng W, Wang G, et al. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 2003. 4(4): R28.
144.Dennis Jr G, Sherman BT, Hosack DA, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 2003. 4(5): P3.
145.Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology 11(2): R14.
146.Hu Z, Hung JH, Wang Y, et al. VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic acids research 2009. 37(suppl 2): W115.
147.Eid J, Fehr A, Gray J, et al. Real-time DNA sequencing from single polymerase molecules. Science 2009. 323(5910): 133.
148.Rusk N. Cheap third-generation sequencing. Nature Methods 2009. 6(4): 244-244.
149.Braslavsky I, Hebert B, Kartalov E, Quake SR. Sequence information can be obtained from single DNA molecules. Proceedings of the National Academy of Sciences of the United States of America 2003. 100(7): 3960.
150.Harris TD, Buzby PR, Babcock H, et al. Single-molecule DNA sequencing of a viral genome. Science 2008. 320(5872): 106.
151.Pushkarev D, Neff NF, Quake SR. Single-molecule sequencing of an individual human genome. Nature biotechnology 2009. 27(9): 847-850.
152.Korlach J, Marks PJ, Cicero RL, et al. Selective aluminum passivation for targeted immobilization of single DNA polymerase molecules in zero-mode waveguide nanostructures. Proceedings of the National Academy of Sciences 2008. 105(4): 1176.
153.Flusberg BA, Webster DR, Lee JH, et al. Direct detection of DNA methylation duringsingle-molecule, real-time sequencing. Nature methods 7(6): 461-465.
154.Metzker ML. Sequencing technologies—the next generation. Nature Reviews Genetics 2009. 11(1): 31-46.
155.Tsutsui M, Taniguchi M, Yokota K, Kawai T. Identifying single nucleotides by tunnelling current. Nature nanotechnology 5(4): 286-290.
156.Clarke J, Wu HC, Jayasinghe L, Patel A, Reid S, Bayley H. Continuous base identification for single-molecule nanopore DNA sequencing. Nature nanotechnology 2009. 4(4): 265-270.
157.Branton D, Deamer DW, Marziali A, et al. The potential and challenges of nanopore sequencing. Nature biotechnology 2008. 26(10): 1146-1153.
158.Simonetti RG, CammàC, Fiorello F, Politi F, D'Amico G, Pagliaro L. Hepatocellular carcinoma. Digestive diseases and sciences 1991. 36(7): 962-972.
159.陈建国,宋新明.中国肝癌发病水平的估算及分析.中国肿瘤2005. 14(1): 28-31.
160.董志伟,乔友林,李连弟,等.中国癌症控制策略研究报告.中国肿瘤2002. 11(5): 250-260.
161.孟炜,陆鸿雁,蔡如琳, et al.原发性肝癌的遗传流行病学研究.中华流行病学杂志2002. 23(6): 438-440.
162.Dulbecco R. A turning point in cancer research: sequencing the human genome. Science 1986. 231(4742): 1055.
163.Campbell PJ, Stephens PJ, Pleasance ED, et al. Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nature genetics 2008. 40(6): 722-729.
164.Yoon SY, Kim JM, Oh JH, et al. Gene expression profiling of human HBV-and/or HCV-associated hepatocellular carcinoma cells using expressed sequence tags. International journal of oncology 2006. 29(2): 315-327.
165.Wang G, Zhao Y, Liu X, et al. Allelic loss and gain, but not genomic instability, as the major somatic mutation in primary hepatocellular carcinoma. Genes, Chromosomes and Cancer 2001. 31(3): 221-227.
166.Zhao X, Li J, He Y, et al. A novel growth suppressor gene on chromosome 17p13. 3 with a high frequency of mutation in human hepatocellular carcinoma. Cancer research 2001. 61(20): 7383.
167.Schultz DC, Vanderveer L, Berman DB, Hamilton TC, Wong AJ, Godwin AK.Identification of two candidate tumor suppressor genes on chromosome 17p13. 3. Cancer research 1996. 56(9): 1997.
168.Huang J, Sheng HH, Shen T, et al. Correlation between genomic DNA copy number alterations and transcriptional expression in hepatitis B virus-associated hepatocellular carcinoma. FEBS letters 2006. 580(15): 3571-3581.
169.Midorikawa Y, Yamamoto S, Ishikawa S, et al. Molecular karyotyping of human hepatocellular carcinoma using single-nucleotide polymorphism arrays. Oncogene 2006. 25(40): 5581-5590.
170.Jongeneel CV, Delorenzi M, Iseli C, et al. An atlas of human gene expression from massively parallel signature sequencing (MPSS). Genome research 2005. 15(7): 1007.
171.Sugarbaker DJ, Richards WG, Gordon GJ, et al. Transcriptome sequencing of malignant pleural mesothelioma tumors. Proceedings of the National Academy of Sciences 2008. 105(9): 3521.
172.Pfaffl MW. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic acids research 2001. 29(9): e45.
173.Nakatsura T, Yoshitake Y, Senju S, et al. Glypican-3, overexpressed specifically in human hepatocellular carcinoma, is a novel tumor marker* 1. Biochemical and biophysical research communications 2003. 306(1): 16-25.
174.Ohmachi Y, Murata A, Yasuda T, et al. Specific expression of the pancreatic‐secretory‐trypsin‐inhibitor (PSTI) gene in hepatocellular carcinoma. International journal of cancer 1993. 55(5): 728-734.
175.Kang YH, Ji NY, Lee CI, et al. ESM-1 silencing decreased cell survival, migration, and invasion and modulated cell cycle progression in hepatocellular carcinoma. Amino Acids: 1-11.
176.Yang JD, Roberts LR. Hepatocellular carcinoma: a global view. Nature Reviews Gastroenterology and Hepatology 7(8): 448-458.
177.Morgan ET. Down-regulation of multiple cytochrome p450 gene products by inflammatory mediators in vivo:: Independence from the hypothalamo-pituitary axis. Biochemical pharmacology 1993. 45(2): 415-419.
178.Yeligar S, Tsukamoto H, Kalra VK. Ethanol-Induced Expression of ET-1 and ET-BR in Liver Sinusoidal Endothelial Cells and Human Endothelial Cells Involves Hypoxia-Inducible Factor-1αand MicroRNA-199. The Journal of Immunology 2009.183(8): 5232.
179.Chan KYY, Lai PBS, Squire JA, et al. Positional expression profiling indicates candidate genes in deletion hotspots of hepatocellular carcinoma. Modern pathology 2006. 19(12): 1546-1554.
180.Schmidt EV. The role of c-myc in cellular growth control. Oncogene 1999. 18(19): 2988.
181.Payton M, Coats S. Cyclin E2, the cycle continues. The International Journal of Biochemistry & Cell Biology 2002. 34(4): 315-320.
182.Wang X, Narayanan M, Bruey JM, et al. Protective role of Cop in Rip2/caspase-1/caspase-4-mediated HeLa cell death. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 2006. 1762(8): 742-754.
183.Wong N, Chan A, Lee SW, et al. Positional mapping for amplified DNA sequences on 1q21-q22 in hepatocellular carcinoma indicates candidate genes over-expression. Journal of Hepatology 2003. 38(3): 298-306.
184.Nurse P. Universal control mechanism regulating onset of M-phase. Nature 1990. 344(503-508.
185.Krens SF, Spaink HP, Snaar-Jagalska BE. Functions of the MAPK family in vertebrate-development. FEBS letters 2006. 580(21): 4984-4990.
186.Venables JP, Klinck R, Koh CS, et al. Cancer-associated regulation of alternative splicing. Nature structural & molecular biology 2009. 16(6): 670-676.
187.Bemmo A, Benovoy D, Kwan T, Gaffney DJ, Jensen RV, Majewski J. Gene expression and isoform variation analysis using Affymetrix Exon Arrays. BMC genomics 2008. 9(1): 529.
188.CiróM, Prosperini E, Quarto M, et al. ATAD2 is a novel cofactor for MYC, overexpressed and amplified in aggressive tumors. Cancer Research 2009. 69(21): 8491.
189.Zou JX, Guo L, Revenko AS, et al. Androgen-induced coactivator ANCCA mediates specific androgen receptor signaling in prostate cancer. Cancer research 2009. 69(8): 3339.
190.Mo F, Hong X, Gao F, et al. A compatible exon-exon junction database for the identification of exon skipping events using tandem mass spectrum data. BMC bioinformatics 2008. 9(1): 537.
191.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1): 139.
192.Burchard J, Zhang C, Liu AM, et al. microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma. Molecular Systems Biology 6(1)
193.De Preter K, Barriot R, Speleman F, Vandesompele J, Moreau Y. Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions. Nucleic Acids Research 2008. 36(7): e43.
194.Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 2005. 102(43): 15545.
195.De Bona F, Ossowski S, Schneeberger K, R tsch G. Optimal spliced alignments of short sequence reads. BMC Bioinformatics 2008. 9(Suppl 10): O7.
196.Au KF, Jiang H, Lin L, Xing Y, Wong WH. Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Research 38(14): 4570.
197.Wang K, Singh D, Zeng Z, et al. MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Research 38(18): e178.
198.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-[Delta][Delta] CT method. Methods 2001. 25(4): 402-408.