人類疾病差異表現基因與調控網路之整合系統

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姓名

紀永鑫(Yung-Hsin Chi) 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

人類疾病差異表現基因與調控網路之整合系統
(Linking Path: A web-based viewer linking pathways to differentially expressed genes of human disease)

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摘要(中)

基因表現的分析對於探索人類疾病的發生與機制一直以來都是一項很有用的工具。許多現有的分析工具針對生物調控網路分析基因表現的資料，但很少有工具探討生物調控網路上面基因組之間的相依關係。我們提出了一個新的方法，可以利用公開的基因表現資料庫所取得的人類疾病資料，來探討生物調控網路上不同交互關係的基因組之間的相依程度。差異表現的基因組可以容易且有效率的在我們的系統上被找到。我們的資料也證實了生物調控網路上基因組之間的相依關係。同時，我們的系統為研究人員提供了一個快速且容易理解的視覺化環境，可以方便的找出生物調控網路與高通量資料之間的關聯性。

摘要(英)

Gene expression analysis has been an useful tool for discovering the formation and mechanism of human diseases. Many existing tools analyze gene expression data on biological pathways. But seldom of them discover the correlation of gene pairs on the pathways. We propose a new approach for discovering the correlation between all interactions of gene pairs in a pathway using gene expression profiles acquired from public domain databases related to human diseases. Differentially expressed gene pairs can easily and efficiently found on our system. The results of our data confirm the correlation between gene pairs in the pathways. Our system also provides a prompt visualization and insight into finding the connections between high-throughput data and pathway relations for researchers.

關鍵字(中)

★ 生物調控網路
★ 生物晶片

關鍵字(英)

★ microarray
★ pathway

論文目次

Chapter 1 Introduction ........................................................................................ 1
1.1 Background ........................................................................................ 1
1.1.1 Gene Expression ........................................................................ 1
1.1.2 Pathway ...................................................................................... 2
1.1.3 Microarray.................................................................................. 2
1.2 Motivation .......................................................................................... 2
1.3 Goal .................................................................................................... 2
Chapter 2 Related Works .................................................................................... 3
2.1 Pathway Analysis ............................................................................... 3
2.1.1 WholePathwayScope ..................................................................... 3
2.1.2 GenMAPP .................................................................................. 4
2.1.3 ArrayXPath ................................................................................ 5
2.2 Microarray Analysis ........................................................................... 5
KegArray................................................................................................ 5
2.3 Integrated Tools .................................................................................. 5
Genevestigator ....................................................................................... 5
2.4 Summary of the related works ........................................................... 6
Chapter 3 Material and Methods ........................................................................ 7
3.1 Materials ............................................................................................ 7
3.1.1 Gene Expression Data of Human Disease ................................. 7
3.1.2 Pathway ...................................................................................... 8
3.2 Methods............................................................................................ 11
3.2.1 Data Acquiring and Preprocessing ........................................... 11
3.2.2 Mapping Gene Expression Data to the Pathway...................... 13
3.2.3 Finding Differentially Expressed Genes .................................. 14
3.3 Summary .......................................................................................... 15
Chapter 4 Implementation ................................................................................ 16
4.1 System Flow..................................................................................... 16
4.2 Data Storage ..................................................................................... 18
4.3 Pathway Map Rendering .................................................................. 19
Chapter 5 Results .............................................................................................. 21
V
5.1 Web Interface ................................................................................... 21
5.2 Statistics ........................................................................................... 24
5.2.1 Relation Distribution ................................................................ 24
5.2.2 PCC Score Distribution............................................................ 27
5.3 Case Study ....................................................................................... 28
Chapter 6 Summary .......................................................................................... 32
6.1 Discussion ........................................................................................ 32
6.2 Future Work ..................................................................................... 32
References ............................................................................................................ 34

參考文獻

1. Curtis, R.K., M. Oresic, and A. Vidal-Puig, Pathways to the analysis of microarray data. Trends Biotechnol, 2005. 23(8): p. 429-35.
2. Zhao, H., et al., Different gene expression patterns in invasive lobular and ductal carcinomas of the breast. Mol Biol Cell, 2004. 15(6): p. 2523-36.
3. Hughes, T.R., et al., Functional discovery via a compendium of expression profiles. Cell, 2000. 102(1): p. 109-26.
4. Kasamsetty, H.N., X. Wu, and J.Y. Chen, Towards an integrative human pathway database for systems biology applications. Proceedings of the 2008 ACM symposium on Applied computing, 2008: p. 1297-1301.
5. Draghici, S., et al., Global functional profiling of gene expression. Genomics, 2003. 81(2): p. 98-104.
6. Yi, M., et al., WholePathwayScope: a comprehensive pathway-based analysis tool for high-throughput data. BMC Bioinformatics, 2006. 7: p. 30.
7. Dahlquist, K.D., et al., GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet, 2002. 31(1): p. 19-20.
8. Chung, H.J., et al., ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics. Nucleic Acids Res, 2004. 32(Web Server issue): p. W460-4.
9. Kanehisa, M., et al., From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res, 2006. 34(Database issue): p. D354-7.
10. Zimmermann, P., et al., GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox. Plant Physiol, 2004. 136(1): p. 2621-32.
11. Norman, S.A., et al., Modifiable risk factors for breast cancer recurrence: what can we tell survivors? J Womens Health (Larchmt), 2007. 16(2): p. 177-90.
12. Loi, S., et al., Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. J Clin Oncol, 2007. 25(10): p. 1239-46.
13. Debled, M., et al., Expression profiling in breast carcinoma: new insights on old prognostic factors? J Clin Oncol, 2007. 25(27): p. 4316-7; author reply 4317-8.
14. Spira, A., et al., Gene expression profiling of human lung tissue from smokers with severe emphysema. Am J Respir Cell Mol Biol, 2004. 31(6): p. 601-10.
15. Moscow, J.A., et al., Loss of heterozygosity of the human cytosolic glutathione peroxidase I gene in lung cancer. Carcinogenesis, 1994. 15(12): p. 2769-73.
16. Knight, J.A., et al., Genetic variants of GPX1 and SOD2 and breast cancer risk at the Ontario site of the Breast Cancer Family Registry. Cancer Epidemiol Biomarkers Prev, 2004. 13(1): p. 146-9.
17. Rosenberger, A., et al., Do genetic factors protect for early onset lung cancer? A case control study before the age of 50 years. BMC Cancer, 2008. 8: p. 60.
18. de Kok, J.B., et al., Normalization of gene expression measurements in tumor tissues: comparison of 13 endogenous control genes. Lab Invest, 2005. 85(1): p. 154-9.
19. McNeill, R.E., N. Miller, and M.J. Kerin, Evaluation and validation of candidate endogenous control genes for real-time quantitative PCR studies of breast cancer. BMC Mol Biol, 2007. 8: p. 107.
20. Grant, S.G., et al., Elevated levels of somatic mutation in a manifesting BRCA1 mutation carrier. Pathol Oncol Res, 2007. 13(4): p. 276-83.
21. Choi, J.K., et al., Integrative analysis of multiple gene expression profiles applied to liver cancer study. FEBS Lett, 2004. 565(1-3): p. 93-100.

指導教授

洪炯宗、吳立青
(Jorng-Tzong Horng、Li-Ching Wu)

審核日期

2008-7-15

推文