Bioinformatics
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生物資訊 ( Bioinformatics ). 蔡懷寬 E-mail: [email protected] Please tell me. Why you are here? Make a definition of bioinformatics. Introduction. What is bioinformatics? Why bioinformatics? The past, current, and future in bioinformatics. 什麼是生物資訊學?. 它是一個跨領域的學門: 結合生物、資訊科學、數學、物理及化學等領域

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生物資訊 ( Bioinformatics )

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Bioinformatics

生物資訊(Bioinformatics)

蔡懷寬

E-mail: [email protected]


Please tell me

Please tell me

  • Why you are here?

  • Make a definition of bioinformatics


Introduction

Introduction

  • What is bioinformatics?

  • Why bioinformatics?

  • The past, current, and future in bioinformatics


Bioinformatics

什麼是生物資訊學?

  • 它是一個跨領域的學門:

    • 結合生物、資訊科學、數學、物理及化學等領域

  • 終極目標:了解生物特性及生命本質

  • 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


Bioinformatics

為什麼需要生物資訊學?


Bioinformatics

HIGH-THROUGHPUT APPROACH

CLASSICAL APPROACH

EXPERIMENT DRIVEN

Hypothesis Experiment

INFORMATION DRIVEN

Experiment  Hypothesis

REVOLUTION IN BIO-MEDICAL RESEARCH

Northern Hybridization

Western Hybridization

Southern Hybridization

RFPD

Differential Display

Subtraction Library

Real-Time PCR

Microarray

2-Dimensional Protein Electrophoresis

Serial Analysis of Gene Expression

Expression Sequence Tags


Bioinformatics

為什麼需要生物資訊學?

  • 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


The genebank data 9 25 2002

The GeneBank Data (9/25/2002)


Protein databank data 9 25 2002

Protein DataBank Data (9/25/2002)


Bioinformatics

為什麼需要生物資訊學?

  • 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,邁向整個基因組的研究

  • 透過資料挖掘來了解基因功能及蛋白質結構

  • 更進一步了解演化歷史及物種間的演化關係


Bioinformatics

人類基因組解讀計畫


Genome

基因組(genome)

  • All the genetic material in the chromosomes of a particular organism

  • Its size is generally given as its total number of base pairs.


Bioinformatics

基因組的大小

  • Human: 3000 million bases

  • Mouse: 3000 million bases

  • Drosophila (fruit fly): 165 million bases

  • Nematode (roundworm): 100 million bases

  • Yeast (fungus): 14 million bases

  • E. coli (bacteria) 4.67 million bases


Bioinformatics

人類基因組解讀計畫

  • 簡稱為HGP (Human Genome Project)

  • 主要目標有:

    • 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


Human genome

Human Genome


Bioinformatics

HGP的沿革與進展

  • HGP從1990年起開始進行

  • HGP是由美國及英國所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


Bioinformatics

HGP的沿革與進展(續)

  • 2001年2月:

    • Initial sequencing and analysis of the human genome (Nature, Vol. 409, 15 Feb. 2001, by International Human Genome Sequencing Consortium)

    • The sequence of the human genome (Science, Vol. 291, 16 Feb. 2001, by J. C. Venter, et al.)


Biology moves into the silicon stage

Biology moves into the silicon stage

in vivo

in vitro

in silico


Bioinformatics

從HGP來看整個生物資訊界的脈動


Before hgp

Before HGP

  • String analysis

    • Pair-wise, multiple sequence alignment


Sequence analysis alignment

Sequence Analysis Alignment

  • Pair-wise alignment

    SURVIVESURVIVE

    SURIUESUR- IUE

  • Multiple sequence alignment

RPCVCPVLRQAAQ s1 RPCVC_ P__VLRQAAQa1

RPCACCPVLRQVVQ s2 RPCACCP__VLRQVVQa2

KPCLCPRQLRQV s3KPCLC_ P RQLRQV_ _a3

KPCCPRQAAQ s4KPC_C_ P____ RQAAQa4

SA


Before hgp1

Before HGP

  • String alignment

    • Pair-wise, multiple alignment

  • Linkage analysis


Linkage analysis

Linkage Analysis


Before hgp2

Before HGP

  • String alignment

    • Pair-wise, multiple alignment

  • Linkage analysis

  • Phylogenetic tree


Phylogenetic tree

Phylogenetic Tree


Phylogenetic tree1

Phylogenetic Tree


Before hgp3

Before HGP

  • String alignment

    • Pair-wise, multiple alignment

  • Linkage analysis

  • Phylogenetic tree

  • Protein structure prediction


Protein structure prediction

Protein Structure Prediction


During hgp

During HGP

  • Sequencing

    • Physical mapping

    • Fragment assembly


Sequencing strategies 1

Sequencing Strategies (1)

  • Map-Based Assembly:

    • Create a detailed complete fragment map

    • Time-consuming and expensive

    • Provides scaffold for assembly

    • Original strategy of Human Genome Project


Sequencing strategies 2

Sequencing Strategies (2)

  • Shotgun:

    • Quick, highly redundant – requires 7-9X coverage for sequencing reads of 500-750bp. This means that for the Human Genome of 3 billion bp, 21-27 billion bases need to be sequence to provide adequate fragment overlap.

    • Computationally intensive

    • Troubles with repetitive DNA

    • Original strategy of Celera Genomics


Shotgun sequencing assembly of random sequence fragments

Shotgun Sequencing: Assembly of Random Sequence Fragments

  • To sequence a Bacterial Artificial Chromosome (100-300Kb), millions of copies are sheared randomly, inserted into plasmids, and then sequenced. If enough fragments are sequenced, it will be possible to reconstruct the BAC based on overlapping fragments.


During hgp1

During HGP

  • Sequencing

    • Physical mapping

    • Fragment assembly

  • Gene Prediction


During hgp2

During HGP

  • Sequencing

    • Physical mapping

    • Fragment assembly

  • Gene Prediction


After hgp post genomic

After HGP (Post Genomic)

  • Microarray


Microarray

Microarray


After hgp post genomic1

After HGP (Post Genomic)

  • Microarray

  • Regulatory network


Regulatory network simplified representation of the nf b network

Regulatory Network  Simplified representation of the NF- B network.


After hgp post genomic2

After HGP (Post Genomic)

  • Microarray

  • Regulatory network

  • Proteomics


Bioinformatics

生物資訊學的相關課題


Bioinformatics

生物資訊相關主題(1)

  • 定序(sequencing)

    • 基因組的DNA序列很長,但卻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

    • 現階段的方法都是將基因組序列切成很多的小段,然後藉由重疊的區域將整個基因組序列再組合回來


Bioinformatics

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

    • 這是基因組學(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


Bioinformatics

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,所以並沒有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,很多都是用已知的基因所歸納出來的規則來做判斷


Bioinformatics

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

    • 資料庫就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。而資料庫管理系統則可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。


Bioinformatics

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

    • 如何從蛋白質的一維序列推測它的三維結構,是一個很難但很重要的研究課題


Bioinformatics

生物資訊相關主題(6)

  • 蛋白體學(proteomics)

    • methodological developments in protein separation and characterization

    • advances in bioinformatics, and

    • novel applications of proteomics in all areas of the life sciences and industry.

      (These endeavours give new insights into protein functions, interactions and pathways.)


Bioinformatics

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

    • 有個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問題都是NP-Complete (換句話說,都是很難的計算問題)


Bioinformatics

生物資訊相關主題(8)

  • 其他課題:

    • 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或稱基因晶片)

    • 分子計算機(molecular computers)


Bioinformatics

生物資訊的相關文獻


Bioinformatics and computational biology related journals

Bioinformatics and Computational Biology-Related Journals:

  • Bioinformatics (期刊原名為CABIOS)

  • Bulletin of Mathematical Biology

  • Computers and Biomedical Research

  • Genome Research

  • Genomics

  • Journal of Computational Biology

  • Journal of Molecular Biology

  • Nature

  • Science


Bioinformatics and computational biology related conferences

Bioinformatics and Computational Biology-Related Conferences:

  • the first IEEE Computer Society Bioinformatics Conference (CSB 2002, CA, USA)

  • Intelligent Systems for Molecular Biology (ISMB 2003, Brisbane, Australia)

  • Pacific Symposium on Biocomputing(PSB 2003, Kauai, Hawaii, USA)

  • The Seventh Annual International Conference on Research in Computational Molecular Biology(RECOMB 2003, Berlin, Germany)


Bioinformatics and computational biology related books

Bioinformatics and Computational Biology-Related Books:

  • Calculating the Secrets of Life: Applications of the Mathematical Sciences in Molecular Biology, by Eric S. Lander and Michael S. Waterman (1995)

  • Introduction to Computational Biology: Maps, Sequences, and Genomes, by Michael S. Waterman (1995)

  • Introduction to Computational Molecular Biology, by Joao Carlos Setubal and Joao Meidanis (1996)

  • Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, by Dan Gusfield (1997)

  • Computational Molecular Biology: An Algorithmic Approach, by Pavel Pevzner (2000)

  • Introduction to Bioinformatics, by Arthur M. Lesk (2002)


Bioinformatics

生物資訊學相關網頁

  • MIT Biology Hypertextbook

    • http://www.mit.edu:8001/afs/athena/course/other/esgbio/www/7001main.html

    • 很不錯的on-line生物學

  • The International Society for Computational Biology:

    • http://www.iscb.org/

  • National Center for Biotechnology Information (NCBI, NIH):

    • http://www.ncbi.nlm.nih.gov/

    • (NCBI, EBI 及 DDBJ是目前生物序列的三大集散中心,它們互相傳遞資料)

  • European Bioinformatics Institute (EBI):

    • http://www.ebi.ac.uk/

  • DNA Data Bank of Japan (DDBJ):

    • http://www.ddbj.nig.ac.jp/


Bioinformatics

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:

    • 因為要解釋某些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,也為未來的生物學研究,舖設了新的途徑。


Bioinformatics

跨領域合作

  • 文化背景不同

    • Credits

    • 隔行如隔山

  • 研究步驟不同

    • 分生的應用常迫在眉睫;而資訊理論的開發卻常曠日費時且充滿不確定性

    • Theory & Practice

真理只有一個

眼光要放遠


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