博碩士論文 972211004 詳細資訊




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姓名 賴俊傑(Chun-chieh Lai)  查詢紙本館藏   畢業系所 系統生物與生物資訊研究所
論文名稱 藉由比較基因表現資料研究次世代定序與晶片技術分析差異
(The gene expression characteristic differences between next-generation sequencing and microarray)
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摘要(中) 人類基因體計畫完成之後,DNA序列的分析的規模出現了重大的變化。高通量技術以具有成本效益的方式提供了非常詳細和大規模的數據。其中,尤其次世代定序技術與微陣列晶片兩大平台被廣泛的使用在分析基因表現上。所以我們發展了一個工作流程來結合次世代定序技術與微陣列晶片的資料在基因表現的層級上來研究轉錄體的複雜性。相對使用單一平台的資料,這個工作流程產生更可靠的資料並提供更多方面的資訊來描述全基因體基因表現的特性。最後,我們初步討論了在這兩平台產生分析差異的原因。
摘要(英) There has been a dramatic changed in the scale of sequence analyses, especially the time after human genome project. High-throughput technologies provide highly detailed and large scale data that can be generated by cost-effective manner. In particular, next-generation sequencing and microarray technologies are two major platforms that immensely used for study gene expression. We developed a workflow to integrate next-generation sequencing and microarray data, to survey the complexity of transcriptomes in gene expression level. This workflow generates more reliable information than using single platform data, and provides more aspects of information to characterize gene expression of whole genome. Finally, we had a preliminary discussion on the cause of the characteristic differences between next-generation sequencing and microarray.
關鍵字(中) ★ 次世代定序
★ 微陣列晶片
★ 基因表現
關鍵字(英) ★ microarray
★ next-generation sequencing
★ gene expression
論文目次 Chinese abstract ...................................................................................................................... i
English abstract ..................................................................................................................... ii
Figure content ....................................................................................................................... v
Table content ...................................................................................................................... vi
Chapter 1 Introduction ................................................................................................. 1
1-1 Next-Generation Sequencing ........................................................................... 1
1-1-1 SOLiD System ..................................................................................... 2
1-1-2 Solexa System ..................................................................................... 4
1-2 Microarray ....................................................................................................... 5
1-3 Motivation ....................................................................................................... 7
Chapter 2 Material and Methods .................................................................................. 8
2-1 Data Source...................................................................................................... 8
2-2 Workflow ......................................................................................................... 9
2-3 Methods ......................................................................................................... 10
2-3-1 CLC Genomic Workbench ................................................................ 10
2-3-2 Affymetrix Expression Console ........................................................ 13
2-3-3 Principle Component Analysis (PCA) ............................................... 16
2-4 Gene Ontology (GO) Enrichment.................................................................. 17
Chapter 3 Results ....................................................................................................... 18
3-1 Microarray data analyses ............................................................................... 18
3-1-1 Principle Component Analysis .......................................................... 18
3-1-2 Affymetrix Expression Console analysis .......................................... 20
3-2 Sequencing data analyses .............................................................................. 22
3-2-1 CLC Genomic Workbench analysis ................................................... 22
3-3 Integration of Sequencing and Microarray .................................................... 24
3-3-1 Comparison of gene expression between two platforms ................... 25
3-4 Genes in microarray sensitive region ............................................................ 30
3-5 Genes in sequencing sensitive region ............................................................ 33
Chapter 4 Discussion .................................................................................................. 34
References ..................................................................................................................... 36
參考文獻 1.Morrissy, A.S., et al., Next-generation tag sequencing for cancer gene expression profiling. Genome Res, 2009. 19(10): p. 1825-35.
2.Cloonan, N., et al., Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods, 2008. 5(7): p. 613-9.
3.Hashimoto, S., et al., High-resolution analysis of the 5'-end transcriptome using a next generation DNA sequencer. PLoS One, 2009. 4(1): p. e4108.
4.Goff, L.A., et al., Ago2 immunoprecipitation identifies predicted microRNAs in human embryonic stem cells and neural precursors. PLoS One, 2009. 4(9): p. e7192.
5.Mayr, C. and D.P. Bartel, Widespread shortening of 3'UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell, 2009. 138(4): p. 673-84.
6.Yoon, S., et al., Sensitive and accurate detection of copy number variants using read depth of coverage. Genome Res, 2009. 19(9): p. 1586-92.
7.Reis-Filho, J.S., Next-generation sequencing. Breast Cancer Res, 2009. 11 Suppl 3: p. S12.
8.Rubin, C.J., et al., Whole-genome resequencing reveals loci under selection during chicken domestication. Nature, 2010. 464(7288): p. 587-91.
9.Pop, M. and S.L. Salzberg, Bioinformatics challenges of new sequencing technology. Trends Genet, 2008. 24(3): p. 142-9.
10.Li, H., J. Ruan, and R. Durbin, Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res, 2008. 18(11): p. 1851-8.
11.Li, H. and R. Durbin, Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009. 25(14): p. 1754-60.
12.Mardis, E.R., The impact of next-generation sequencing technology on genetics. Trends Genet, 2008. 24(3): p. 133-41.
13.Fullwood, M.J., et al., Next-generation DNA sequencing of paired-end tags (PET) for transcriptome and genome analyses. Genome Res, 2009. 19(4): p. 521-32.
14.Mokry, M., et al., Accurate SNP and mutation detection by targeted custom microarray-based genomic enrichment of short-fragment sequencing libraries. Nucleic Acids Res, 2010. 38(10): p. e116.
15.Tuch, B.B., et al., Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations. PLoS One, 2010. 5(2): p. e9317.
16.Bormann Chung, C.A., et al., Whole methylome analysis by ultra-deep sequencing using two-base encoding. PLoS One, 2010. 5(2): p. e9320.
17.Dressman, D., et al., Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci U S A, 2003. 100(15): p. 8817-22.
18.Allison, D.B., et al., Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet, 2006. 7(1): p. 55-65.
19.GeneChip® Whole Transcript (WT) Sense Target Labeling Assay user manual.
20.Solmi, R., et al., Displayed correlation between gene expression profiles and submicroscopic alterations in response to cetuximab, gefitinib and EGF in human colon cancer cell lines. BMC Cancer, 2008. 8: p. 227.
21.CLC Genomics Workbench product sheet. CLC Genomics Workbench
22.CLC Genomics Workbench user manual. CLC Genomics Workbench
23.Mortazavi, A., et al., Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods, 2008. 5(7): p. 621-8.
24.Ewing, B. and P. Green, Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res, 1998. 8(3): p. 186-94.
25.Tang, F., et al., mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods, 2009. 6(5): p. 377-82.
26.Affymetrix Expression Console™ Software Version 1.0-user Guide.
27.Huang da, W., B.T. Sherman, and R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 2009. 4(1): p. 44-57.
28.Dennis, G., Jr., et al., DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol, 2003. 4(5): p. P3.
29.Kapur, K., et al., Cross-hybridization modeling on Affymetrix exon arrays. Bioinformatics, 2008. 24(24): p. 2887-93.
30.Uva, P. and E. de Rinaldis, CrossHybDetector: detection of cross-hybridization events in DNA microarray experiments. BMC Bioinformatics, 2008. 9: p. 485.
31.Carter, S.L., et al., Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements. BMC Bioinformatics, 2005. 6: p. 107.
指導教授 吳立青(Li-ching Wu) 審核日期 2010-7-19
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