Microsoft PowerPoint _Microarray & Bioinformatics (BC2)

Size: px

Start display at page:

Download "Microsoft PowerPoint _Microarray & Bioinformatics (BC2)"

筵酆
7 years ago
Views:

1 生物晶片概論 2: Microarrays & Bioinformatics 陳光琦助理教授 (Kuang-Chi Chen) Office: H

2 If we begin with certainties, we shall end in doubts; but if we begin with doubts, and are patient in them, we shall end in certainties. Francis Bacon

3 What Is Bioinformatics? 一個跨領域的學門 : 結合生物資訊科學數學物理及化學等領域終極目標 : 了解生物特性生物特性及生命本質生命本質重要的子領域 : 大量資料的分析演算法及統計方法各種生物序列, 結構, 功能及演化的分析與解釋管理及使用各種型態資訊的軟體工具

4 Why We Need Bioinformatics? 生物相關資料的累積迅速, 資料量非常大, 亟需電腦協助分析提供實驗設計更宏觀的看法, 從以往個別基因的研究, 邁向整個基因組整個基因組的研究資料探勘以了解基因功能及蛋白質結構了解演化歷史及物種間的演化關係

5 生命科學與資訊科學的互動就某方面而言, 這種互動很類似物理與數學間的互動 : 因為要解釋大量生物資料的信息, 帶動了新的資訊分析方法及工具的製作 ; 新的資訊理論及工具的產生, 為未來的生物學研究, 舖設了新的途徑

6 Human Genome Project

7 人類基因組解讀計畫簡稱為 HGP (Human Genome Project) 主要目標 : identify all the genes in human DNA, determine the sequences of the 3 billion chemical bases that make up human DNA store this information in databases develop tools for data analysis transfer related technologies to the private sector address the ethical, legal, and social issues (ELSI) that may arise from the project

8 Genome Projects 1. To establish an integrated web-based database and research interface. 2. To assemble physical and genetic maps of the genome. 3. To generate and order genomic and expressed gene sequences. 4. To identify and annotate the complete set of genes encoded within a genome. 5. To compile atlases of gene expression. 6. To accumulate functional data, including biochemical and phenotypic properties of genes. 7. To characterize DNA sequence diversity. 8. To provide the resources for comparison with other genomes.

9 HGP 的沿革與進展 HGP 從 1990 年起開始進行 HGP 是由美國及英國主導的一項全球性計畫 2000 年六月與 Celera 私人公司共同宣布人類基因組的初稿已完成

10 基因組的大小 Human: 3000 million bases Mouse: 3000 million bases Drosophila (fruit fly): 165 million bases Nematode (round worm): 100 million bases Yeast (fungus): 14 million bases E. coli (bacteria): 4.67 million bases

11 Genomes The Linkage to Life I. Genomics to biology Elucidating the structure and function of genomes. II. Genomics to health Translating genomebased knowledge into health benefits. III. Genomics to society Promoting the use of genomics to maximize benefits and minimize harms.

12 Mathematics Vertical/Horizontal Integration and Fusion to Create New Frontier (Traditional Core Sciences vs New Life Science Frontier) Physics Chemistry Traditional Science Engineering Biology Medicine Pharmaceutical Agriculture Biochemistry Genetics New Science Mathematics Nanoscience Engineering Information Science Life Science System Science Nano Life Science System Life Science Life Science (Material, Energy, Information, Systems)

13 BIO-X X program, Stanford University initiated in 1998, established in 2003 BioScience BIO-X BioEngineering Biomedicine The James H. Clark Center for Biomedical Engineering and Sciences ~40 PIs, ~25,000 m 2, ~700 Scientists

14 Integration of Core Disciplines to Create New Frontier BioScience Information Science System Sciences Physical Science Nanoscience Okinawa Institute of Science & Technology (OIST) Bio-X of Stanford, Systems Biology Institute, BioMax of Seoul

15 Improved Technology Can Trigger Revolution Determining human genome years years genomic project year HGP & Celera genome & SNP database day - personal database and diagnosis Manipulate Individual Gene Knock out Knock in Ribozyme Antisense RNAi Antibody Peptide Aptamer Gene RNA Protein Function Systemic Approach Genome Transcriptome Proteome Model Organism

16 Biology Moves into the silicon Stage in vivo in vitro in silico

17 The Post-Genome Era Genomes Gene Products Structure & Function Pathways & Physiology Populations & Evolution Ecosystems Bioinformatics provides the tools To Extract and combine knowledge From isolated data and results in biology into meaningful working models of cells and organisms - Their birth, life and death Source: Shankar Subramaniam, UCSD

18 Targeted Prescription of Medicines: Applied Pharmacogenetics today empirical prescription mass market trial & switch drug a drug c drug d drug b individual physician experience - cost time, money & well-being future rational prescription individualized drug a diagnostic drug b drug c define & treat drug d informed physician diagnosis - save time, money & illness

19 Nature 415, (2002)

20 New Directions in Cancer Research and Treatment: A Better Understanding of the Disease Evolution of Cancer Biology Tumor Site Tumor Histology Molecular Biology Tumor Genotype Molecular definition of disease Improved diagnostic technologies and clinical practice Future paradigm: Integration of molecular diagnostics with targeted therapies for cancer care

21 Central Dogma

22 The Data Explosion Gene Sequences Proteins NCBI Web Site 4/2000 PDB Web Site 6/2000

23 Genomics Reality: Without It, Data is Just...Data! Data is crucial to the success of analysis. Garbage in garbage out Data Information Knowledge Action

24 生物資訊學的相關課題

25 生物資訊相關主題 1 定序 (sequencing) 基因組的 DNA 序列很長, 扭曲在小小的細胞內, 目前仍然沒有方法可以一次將整個序列讀出來現階段的方法是將基因組序列切成很多小段, 然後藉由重疊的區域將整個基因組序列再組合回來

26 生物資訊相關主題 2 序列分析 (sequence analysis) 藉由序列分析的結果, 來探索序列的功能基因組學 (genomics) 分析的基礎 DNA 序列間的比較蛋白質序列間的比較長序列的比較相似序列的比較多重序列比較 SNP (Single( nucleotide polymorphism) Haplotypes

27 生物資訊相關主題 3 找尋基因 (gene finding) 給定一個基因組序列, 決定各個基因的位置由於目前尚未完全理解 DNA 語言, 所以無法百分之一百正確的方法可以直接從基因組序列決定出所有的基因出現位置現階段的方法, 大都是用已知的基因所歸納出的規則來做判斷

28 生物資訊相關主題 4 生物資訊資料庫 (bioinformatics database) 生物序列相關的資訊累積很快, 資料庫已成為生物資訊應用上最重要的工具資料庫是一堆資料的儲存庫, 它的存放方式通常會規劃得讓電腦可以快速搜尋及擷取資料資料庫管理系統讓使用者設計所需要的資料庫, 以及操作資料庫所需的修訂存取及搜尋功能

29 Database Integration SeqFetch Service SeqFetch Service BLAT Service GO Service BLAST Service Database BioXXX Microarray Service SMD Your Script KEGG

30 生物資訊相關主題 5 蛋白質結構的預測 (protein structure prediction) 蛋白質的功能很多是由它的結構所決定的 X-ray 及 NMR 是目前決定蛋白質結構常用的方式從蛋白質的一維序列推測它的三維結構, 即蛋白即質摺疊 (protein folding) 蛋白質結合 (protein docking)

31 生物資訊相關主題 6 蛋白質體學 (proteomics) - Protein + Genome Proteome - Proteome Proteomics - Protein-Protein Interaction

32 生物資訊相關主題 7 演化樹的建構 (evolutionary tree construction) 演化樹的建構可協助了解演化過程及歷史有的方法根據特徵 (character) 保留的狀況表來決定演化樹有的方法根據物種間的距離來決定演化樹大部分的演化樹建構演化樹建構問題都是 NP-complete ( 換句話說, 都是很難的計算問題 )

33 其他課題 : 生物資訊相關主題 8 RNA 二維結構預測 (RNA secondary structures) 比較基因組學 (comparative genomics) 基因網路 (genetic networks) 微陣列晶片 (microarrays 或 gene chip) 分子計算機 (molecular computers)

34 Overview of Bioinformatics Bioinformatics, Biocomputing, Computational Biology Interface between biotechnology and computer science Flavours Integration of relevant biomedical information Platform for in silico biology Focus on Health Applications From Genetics to Genomics to Postgenomics to Molecular Medicine

35 The role of bioinformatics supporting genetics Sequences Alignments Phylogenetic trees Structures

36 The role of Bioinformatics in support of genomics Sequencing Gene prediction ATCGCGCTA Genome databases Annotation

37 Bioinformatics in support of Post-Genomic Research Genomes Proteomics SNPs DNA microarrays

38 Bioinformatics in support of Systems Biology Metabolic Pathways Signaling pathways Genetic Networks Interactions

39 New Opportunities for Health Informatics Genome Project Interest for biologists One gene at a time Monogenic diseases Tedious genotyping DNA level Bioinformatics explosion Post-Genomics Clinical interest Hundreds or thousands of genes simultaneously Complex diseases High throughput genotyping DNA, RNA, Proteins Integration of clinical and genetic information

40 Overview Human Genetic Variation Technologies Data Applications Genome Genotyping Haplotyping Individual genomics (SNPs and mutations) Diagnosis Pharmacogenetics Individualised healthcare BIOINFORMATICS & MEDICAL INFORMATICS Information Gene Expression DNA arrays MS, 2D ef Functional genomics proteomics Disease classification Pharmacogenomics Molecular medicine Molecular causes of diseases

41 Molecular Medicine and Individualised Healthcare New approaches: Pharmacogenetics,, DNA arrays, proteomics,, SNPs, genetic diag. Molecular Medicine - Effort in explaining life and disease in terms of the presence and regulation of molecular entities Individualised Healthcare Application of genomics to identify individual predispositions to disease and to design therapies adapted to the genetic profiles of patients and that could be prescribed with guarantee of security & efficiency

42 The convergence between MI and BI

43 A Model for Studying Interactions To foster the application of bioinformatics in health To adapt medical informatics systems to the genetics paradigm To apply Information Tech to facilitate molecular medicine

44 The Application of Informatics in Molecular Medicine Informatics Medical Informatics? Bioinformatics Medicine Molecular Medicine Genomics

45 Gene Chip 基因晶片

46 What Is Gene Chip? 基因晶片生物晶片 DNA 晶片基因微陣列 (microarray) 基因晶片技術被發展來大規模篩選並監測基因的表現用來尋找基因瞭解基因, 進而應用基因, 最重要的是了解其功能及其作用了解其功能及其作用

47 What Is Gene Chip? (cont d) Put a large number (~100K) of cdna sequences or synthetic DNA oligomers onto a glass slide (or other substrate) in known locations on a grid. Label a RNA sample and hybridize. Measure amounts of RNA bound to each square in the grid.

48 Why We Need Gene Chip? 西元 2003 年, 媲美 60 年代登月計劃的人類基因體計劃結束長達十五年的發展過程, 人類 46 條染色體九成五以上定序完畢, 進入一個新世紀, 即後基因體時代即後基因體時代定序完成只提供讓人一窺遺傳密碼全貌的機會, 定序結果像是一本電話簿, 只有電話號碼和住址而無姓名 ; 或高速公路之公里數

49 Why We Need Gene Chip? (cont d) 後基因體時代最重要工作之一係尋找基因探索基因的功能及其間的交互作用尋找基因瞭解基因應用基因, 最重要的是了解其功能, 它的作用是甚麼人類約有十萬個? 可表現功能基因 (functional genes) 控制著人類生長發育遺傳行為及疾病等生理現象及生化反應, 其中約 90% 是不知道功能的

50 Why We Need Gene Chip? (cont d) 基因晶片技術的崛起, 不僅提供方法解決上述基因研究上的問題, 也帶動基因功能分析自動化, 加速了功能基因研究的進度近年來, 有三項主要的科技被發展來大規模篩選並監測基因的表現 :(a) cdna microarray (b) DNA chips (c) Serial Analysis of Gene Expression (SAGE)

51 Flowchart of cdna Microarray Biological question Differentially expressed genes Sample class prediction etc. Experimental design Estimation Microarray experiment Image analysis Normalization 16-bit TIFF files (Rfg, Rbg), (Gfg, Gbg) R, G Testing Clustering Discrimination Biological verification and interpretation

52 cdna Microarray (a) cdna Microarray 由 P. Brown 等人所發展, 將經由聚合酶聯鎖反應 (PCR) 且純化之基因片段以微陣列方式配合化學方法, 將 DNA 固定於載玻片上以兩種核酸探針 (probe) 分別標示上不同波長的螢光分子, 與載玻片上的基因進行雜交反應經過雷射光的激發及掃瞄後, 放射出來的兩種波長的螢光可同時被收集並數位化, 以用來估量基因表現的程度估量基因表現的程度

53 DNA Chip (b) DNA Chip 應用單一核單一核苷酸陣列 (oligonucleotide arrays),, 為 90 年代初發展於定序的方法在玻璃或矽晶片上直接合成特定序列的單一核苷酸, 再以標記螢光分子的探針與之雜交, 分析那些單一核苷酸序列有雜交反應發生, 並以電腦重組這些序列可快速快速且同時定量許多基因表現的程度

54 DNA Chip (cont d) (b) DNA Chip 在 1.28 cm 1.28 cm 的晶片上約可合成 409,000 個單一核苷酸酸, 可代表約 10,000 個基因?? (?? 1.64 cm 2, 40 萬個微點 )

55 SAGE (c) Serial Analysis of Gene Expression (SAGE) 由 Kinzler 等人發展, 利用特殊限制脢 (Fok I 及 Nla III) 及聯結子 (linkers) 以形成一連串的聯結序列 (serial link tags),, 然後應用對聯結子具專一性的引子 (primer) 進行聚合酶聯鎖反應. 最後將這些增殖的基因片段作定序的工作藉由許多聯結子所包圍產生之九個核苷酸 cdna 序列出現的機率, 可用來估計基因表現的程度

56 Gene Chip 基因晶片生物晶片生物晶片基因微陣列 DNA 晶片依製程製程及承載基因之及承載基因之材料, 分為微陣列 (microarray 或基因微陣列 ) 及 DNA 晶片 (DNA chip) 兩種目前, 將上述兩者 (microarray 及 DNA chip) 均統稱為基因晶片基因晶片或生物晶片 (gene chip or biological chip),, 意謂含有一小範圍的承載物 ( 薄膜玻璃或矽晶片 ) 上, 可同時獲得大量的基因或生物訊息

57 Microarray 將經聚合酶鏈鎖反應 (PCR) 增殖之片段或合成之單一核苷酸酸, 以精密儀器佈放至尼龍薄膜或玻璃載玻片上上, 即微陣列即 (microarray) 或基因微陣列製作陣列方式的晶片費用較低, 也較能為一般實驗室所採用

58 DNA Chip 利用半導體製程半導體製程及化學合成技術, 直接在矽晶片上合成特定序列的單一核單一核苷酸酸, 即所謂的 DNA 晶片 (DNA chip) 其製程複雜且所費不貲, 並不適合一般實驗室發展應用

59 Major Technologies cdna probes (> 200 nt),, usually produced by PCR, attached to either nylon or glass supports. Oligonucleotides (25-30 nt) synthesized in situ on silica wafers (Affymetrix( Affymetrix). Oligonucleotides (25-80 nt) attached to glass support (Agilent( Agilent,, ink-jet printing). Probes attached to tagged beads.

60 Gene Chip 基因晶片為本世紀最受矚目的功能基因研究工具之一, 具有 : (1) 大量樣本 (2) 同時進行 (3) 自動化分析等三項特點及 (1) 基因定量 (2) 差異基因搜尋兩項特質

61 Application of Gene Chip

62 Gene Chip 應用範圍一差異表現基因的篩選 : 比較正常細胞與癌細胞之差異, 可大量篩選腫瘤標記基因 (tumor marker) 或應用於具不同癌轉移能力之細胞株以獲得癌轉移相關基因二細胞週期基因表現的研究 : 剖繪細胞週期中不同時期基因之表現, 並探索細胞從靜止期 (G 0 ) 回到生長週期的關鍵基因, 以發展使停止生長的細胞重新生長的方法

63 可能的應用範圍 (cont (cont d) 三基因突變之解析 : 利用單一核苷酸晶片快速定出基因突變的位置及序列如 P53 晶片進行臨床篩檢, 判斷被篩檢者是否為癌症高危險群四驗證基因或部分序列之效能 : 轉染 PTEN 基因 (transfection) 至高度癌轉移細胞, 觀察哪些基因受到影響, 是否因此導致細胞轉移能力受到抑制同時比較此基因存在與否對下游基因表現的影響, 為基因網路研究之一環

64 可能的應用範圍 (cont (cont d) 五遺傳網路的建構 : 利用不同發育時期之老鼠胚胎, 分析脊髓及海馬迴中不同基因變化趨勢, 以瞭解中樞神經系統發育之各階段基因的交互作用 (interaction),, 進而建構出中樞神經系統發育的基因網絡 (genetic network)

65 可能的應用範圍 (cont (cont d) 六疾病之基因型分類 : 如急性淋巴母細胞白血病 (ALL) 與急性骨髓細胞白血病 (AML),, 經由基因晶片與統計分析, 定出某些基因表現的特定型態, 並用於臨床上區分該兩類白血病亦可用於疾病新亞型的發現, 如非霍奇金氏淋巴瘤 (non-hodgkin's lymphoma) 新亞型之鑑定七致病原對細胞宿主之影響 : 如愛滋病毒 (HIV-1) 感染免疫細胞 (CD4+ T-cells) T 後, 造成整體性基因表現改變的情形, 亦可作為預防預防愛滋病毒或其他病毒感染研究的基礎

66 可能的應用範圍 (cont (cont d) 八藥物開發及藥理學研究 : 篩選開發出來的藥物是否可抑制某些致病基因或誘導抗病基因的表現, 而不影響其他維持正常生命功能之基因的表現可更準確的篩選準確的篩選及預測新藥的功能, 並大幅縮短藥物開發的時程, 如動物及人體試驗, 或提高臨床人體試驗的安全性亦可應用於藥物對基因表現的影響, 以瞭解一些藥物在分子層次上的作用機制, 或為臨床診斷上判斷使用劑量復發率治癒率及存活率的參考依歸

67 可能的應用範圍 (cont (cont d) 九轉錄因子的搜尋 : 在未來與晶片進行雜交反應的不再是核酸分子, 而是蛋白質, 可應用於搜尋轉錄因子或其他核酸結合蛋白質十生物數量遺傳之研究 : 例如作物產量受多基因控制, 利用晶片多基因分析的能力, 或許可解開數量遺傳的基因之謎, 為分子育種學豎立新的里程碑

68 Health Information Systems in the Age of Post-Genomic Research

69 Forces Driving the Bioinformatics Revolution The promise of health applications of the human genome information Proof of concept with the human genome sequencing Availability of new raw information Sequences (genome projects), SNPs (variability) Gene expression data (DNA arrays), Proteomics Interest of new users Pharma and Biotech, Biomedical research firms, Clinical New tools Internet, Data mining New discipline Innovation brand, Search for funding center,, IT

71 Why HI & MI? Continuity of healthcare (Patient,, GP, secondary, Terciary) Growing information processing role of ALL health professionals (nurses, pharmacists, biologists,,...) Convergence with bioinformatics made easier Integration of environmental and genetic data

72 Health Informatics Subdivisions Information level (molecular, cellular, tissue, organ. Patient, disease, population) Clinical specialties or diseases (cancer informatics,, cardiovascular,...) User (patient, clinical, pharma,, nurse, bio) Agent (HMO, hospital, government,,...) Technology (AI, Telemedicine, Decision making, imaging, genomics)

73 Health Information Levels Public Health Informatics Medical Informatics Medical Imaging Bioinformatics Martin-Sanchez et al, Methods. Inf. Med. 2002, 41:25-30

74 Types of Medical Data lab results administrative orders, appointments images signals, EKG microbiology results demographic familiar history of prescriptions GENETIC

75 Genetic Data Special Features Multiple sources Large amounts More static Probabilistic Multilevel (DNA, RNA, Prot) Accumulative (SNPs, multifactorial diseases) Context-dependent Needs comparison with public databases Not quantitative Complex Informs about relatives, not only about patients Predictive power, even in the absence of clinical signs or symptoms; Has the potential to generate a unique identifier profile for individuals.

76 Sources of Genetic Data Genome and sequence databases EMBL Protein sequence and structures PDB, SwissProt Genetic diseases OMIM, GeneCards, GeneReviews Genetic tests Geneclinics, EddNal Mutations Central variation databases HGMD Single Locus Databases SNPs - (dbsnp) SNPs

77 Examples Gene - RPS6KA4 Mutations [76A>C; 83G>C] 112_117delAGGTCAinsTG 117delAGGTCAinsTG K29_M29insQSK SNP: rs Submitter Handle: TSC-CSHL CSHL Submitter Method ID: TSC-WUGSC WUGSC-1-2 GenBanK Accession: AC Length: : 673 Flanking Sequence Information: : GATGGGACCA CTGGTAGGAG... Observed: : C/T No. of Chromosomes Sampled: : 16 Allele: : C = / T = 0.563

78 Genetic Data in Medical Coding Systems ICD SNOMED UMLS MeSH LOINC GALEN

79 Knowledge Representation in Biology The MGED ontology ontology - ontologies for describing gene expression experiments and data. TAMBIS ontology (TaO) an ontology of bioinformatics and molecular biology. RiboWeb an ontology describing ribosomal components, associated data and computations for processing those data. EcoCyc an ontology describing the genes, gene product function, metabolism and regulation within E. coli. Gene Ontology (GO) an ontology describing the function, the process and cellular location of gene products from eukaryotes.

80 Where is the Information? What is the Data? Embanks genetic sequences Swiss-prot protein sequences DNA chips / microarrays Metabolic pathways Signaling pathways / regulatory networks Medline biomedical literature Taxonomies / Ontologies

83 Related Scientific Activities Dec,, Brussels EC Synergy between Research in Medical Informatics,, Bioinformatics and Neuroinformatics Manchester March 2002 meeting: Genotype To Phenotype: Linking Bioinformatics and Medical Informatics Ontologies Nov AMIA Conference: Bio-medical informatics: one discipline EC IST BIOINFOMED ACMI - NLM

84 BIOINFOMED Prospective Analysis on the Relationships and Synergy between Medical Informatics and Bioinformatics URL: (starting from January 2002) Institute of Health Carlos III Madrid SPAIN Polytechnical University of Madrid (Prof. Victor Maojo) - SPAIN Linkoping University (Prof. Ankica Babic) - SWEDEN State of the Art and Inventory of resources of interest and standardisation initiatives Identification of key groups and priority research lines Collaboration between experts and groups Final report and Workshop (Nov 2002)

85 Modelling MI Top-down approach from clinical manifestations to the underlying pathophysiological processes BI - Bottom-up approach, from genomic information to physiological function - An integrated approach could provide a unified vision Maojo, Martin-Sanchez et al, Journal of Biomedical Informatics. In press

86 Conclusions Interaction between MI and BI is needed: Not reinventing the wheel Not making the same mistakes Collaboration is better integrated approach to disease Synergy will it be possible? New developments from scratch Birth of a new discipline? BIOMEDICAL INFORMATICS MI MI MI BI BI BI

87 The Present and Future Move from Information to Knowledge

88 生物資訊的相關文獻

89 Bioinformatics and Computational Biology- Related Journals: Bioinformatics ( 期刊原名為 CABIOS) BMC Bioinformatics Journal of Computational Biology Bulletin of Mathematical Biology Computers and Biomedical Research Journal of Molecular Biology Genome Research Genomics Nature Science..

90 Bioinformatics and Computational Biology- Related Websites: MIT Biology Hypertextbook www/7001main.html The International Society for Computational Biology: National Center for Biotechnology Information (NCBI, NIH): (NCBI, EBI 及 DDBJ 是目前生物序列的三大資料庫 ) European Bioinformatics Institute (EBI): DNA Data Bank of Japan (DDBJ):

untitled

untitled Course No. 00333280 will lead you Computational Biology / Bioinformatics? NIH Computational Biology (The development and application of data-analytical and theoretical methods, mathematical modeling and