医学网络平台分析课件GO课件

1、Gene Ontology (GO) 简介,目的： 为了查找某个研究领域的相关信息，生物学家往往要花费大量的时间，更糟糕的是，不同的生物学数据库可能会使用不同的术语，好比是一些方言一样，这让信息查找更加麻烦，尤其是使得机器查找无章可循。Gene Ontology就是为了解决这种问题而发起的一个项目。,Gene Ontology中最基本的概念是term。 GO里面的每一个entry都有一个唯一的数字标记，形如GO:nnnnnnn，还有一个term名，比如“cell”, “fibroblast（纤维组织母细胞 ） growth factor receptor（受体） binding”，或者“sig

2、nal transduction”。 每个term都属于一个ontology，总共有三个ontology，它们分别是molecular function, cellular component和biological process。,一个基因product可能会出现在不止一个cellular component里面，也可能会在很多biological process里面起作用，并且在其中发挥不同的molecular function。 比如，基因product “cytochrome（细胞色素） c” 用molecular function term描述是“oxidoreductase act

3、ivity（氧化还原酶活性）”，而用biological process term描述就是“oxidative phosphorylation（氧化磷酸化）”和“induction of cell death”， 最后，它的celluar component term是“mitochondrial matrix（线粒体）”和“mitochondrial inner membrane”（线粒体内部膜）。,Ontology中的term有两种相互关系，它们分别是is_a关系和part_of关系。 is_a关系是一种简单的包含关系，比如A is_a B表示A是B的一个子集。比如nuclear chro

4、mosome is_a chromosome。 part_of关系要稍微复杂一点，C part_of_D意味着如果C出现，那么它就肯定是D的一部分，但C不一定总会出现。比如nucleus part_of cell，核肯定是细胞的一部分，但有的细胞没有核。,Ontology的结构是一个有向无环图，有点类似于分类树，不同点在于Ontology的结构中一个term可以有不止一个parent。 比如 biological process term hexose biosynthesis 有两个parents，它们分别是hexose metabolism和monosaccharide biosynthe

5、sis，这是因为生物合成是代谢的一种，而己糖又是单糖的一种。,毒理基因组学,Not found,Not found,Not found,MAPPFinder and GenMAPP,一、问题的提出,解决方法,One way of accelerating the pace of data analysis is to approach the data from a higher level of organization. This can be done using data-driven methods, such as hierarchical clustering and selfo

6、rganizingmaps A complementary approach is to view the data at the level of known biological processes or pathways.,We have developed a tool called MAPPFinder that dynamically links gene-expression data to the GO hierarchy. One tool that assists in the identification of important biological processes

7、 is GenMAPP (Gene MicroArray Pathway Profiler) , a program for viewing and analyzing microarray data on microarray pathway profiles (MAPPs) representing biological pathways or any other functional grouping of genes.,输入数据,库文件,结果输出,Genemerge,问题提出： While freely available through public databases, diffe

8、rent sets of genomic data are often difficult to integrate into a given study because no common platform exists for such analysis.,For example,A great deal of genomic and proteomic information is available. For many genes, something is known about their molecular and biological function, pathway mem

9、bership, physical chromosomal location, level of polymorphism, RNAi phenotypes, disease phenotypes, and rate of molecular evolution.,提出需求： Simple and flexible software that can take advantage of diverse genomic and proteomic data for both data mining and hypothesis testing is required.,软件功能： GeneMer

10、ge can perform analyses on a wide variety of data quickly and easily and facilitates both data mining and hypothesis testing.,Given a set of study genes,GeneMerge retrieves functional genomic data for each gene and provides statistical rank scores for over-representation of functions or categories w

11、ithin the set of study genes.,GeneMerge uses 4 input files:,1. Study set gene file 2. Population set gene file 3. Gene-association file 4. Description file,Study set gene file,It is comprised of genes that are currently under investigation Study set gene file format： genename; genename; ,Population

12、set gene file,It is comprised of those genes from which the study set was drawn, often all genes on a given microarray. Population set gene file format： genename; genename; ,Gene association file,Association file Gene-association file format genename tab functionID; genename tab functionID; genename

13、 tab functionID;functionID; .,Gene-association Data,KEGG metabolic pathway KEGG developmental pathway GO molecular function GO biological process GO cellular component RNAi phenotype chromosomal location knock-out phenotype disease phenotypes, local recombination rate transcription binding site tran

14、scription binding site family DNA methylation acetylation GC content male specific female specific ortholog in clade X rate of molecular evolution tissue-specific expression over/under-expression in experiment X,Description file,description file containshuman-readable descriptions of gene-associatio

15、n IDs. Description file format functionID tab description_of_function functionID tab description_of_function functionID tab description_of_function,Output text file,GMRG_Term ： GeneMerge term, for example a GO identifier GO:0001234“ Pop_freq： fraction of genes in the population with this term Pop_fr

16、ac： fraction of genes in the population with this term (whole numbers),Study_frac： fraction of genes in the study set with this term (whole numbers) Raw:： es P-value e-score： Bonferroni corrected P-value,Description ： GeneMerge terms English description Contributing_genes ： All the genes that are as

17、sociated with this term in the study set,统计方法：超几何分布,n n is the population set, the set from which k is drawn k: k is always the study set of genes,p: p isthe fraction of genes in the population n associated with the particular identifier under investigation. r: The number of genes with a particular

18、identifier is r,Example Data,Population Gene Set Yeast Genome,Study Gene Sets,Up-regulated in Snf/Swi Mutants (Minimal Media),Gene-Association Files,Yeast GO Biological Process,Description Files,GO Biological Process Descriptions,Results,Online gengmerge,GoMiner: (Zeeberg et al., Genome Biology, Mar

19、ch 2003),For Tour of GoMiner: Advance using forward arrow,GoMiner: (Zeeberg et al., Genome Biology, March 2003),GoMiner is a tool for biological interpretation of omic data including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that diff

20、er in expression between samples, raising the question, “What the h-ll does it all mean biologically?”,To answer that question, GoMiner leverages the Gene Ontology (GO) to classify the genes into biologically coherent categories and assess those categories statistically.,For biological interpretatio

21、n of a microarray experiment, the user enters the list of genes on the array and has the option of flagging them as up-regulated, down-regulated, or neither with respect to some index standard.,This animation presents a tour of the highlights of GoMiners functionality and user interface.,GoMiner: (Z

22、eeberg et al., Genome Biology, March 2003),An Unchanged Gene,An Overexpressed Gene,An Underexpressed Gene,Total Genes in Category,Fishers Exact p-Value for All Changed Genes in Category,Relative Enrichment of Underexpressed Genes In Category,Relative Enrichment of All Changed Genes in Category,Relative Enrichment of Overexpressed G