1 / 31

Annotation, Databases, GO, Pathways,and all those things

Annotation, Databases, GO, Pathways,and all those things. Information on microarray data. Consists of different types of informations Genes annotations Samples annotations Genes expression levels Covariates Experimental design etc. Annotation: Relating probesets to genes.

merlin
Download Presentation

Annotation, Databases, GO, Pathways,and all those things

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Annotation, Databases, GO, Pathways,and all those things

  2. Information on microarray data • Consists of different types of informations • Genes annotations • Samples annotations • Genes expression levels • Covariates • Experimental design • etc

  3. Annotation: Relating probesets to genes

  4. Use of microarray clone annnotation • Often, the result of microarray data analysis is a list of genes. • The list has to be summarized with respect to its biological meaning. • For this, information about the genes and the related proteins has to be gathered. • If the list is small (let’s say, 1–30), this is easily done by reading database information and/or the available literature. • Sometimes, lists are longer (100s or even 1000s of genes). Automatic parsing and extracting of information is needed.

  5. From clone information to genes and proteins (1) • Microarrays are produced using information on expressed sequences as EST clones, cDNAs, partial cDNAs etc. • At the other end, functional information is generated (and available) for proteins. • Hence, there is a need to map a clone sequence ID to a protein ID. • This is a non-trivial task

  6. From clone information to genes and proteins: a non-trivial task • First, there are usually hundreds of ESTs (and several cDNA sequences) that map to the same gene. • The Database Unigene tries to resolve this multiplicity problem by sequence clustering. • An alternative approach is taken by Locus Link. This is a quite stable repository of genomic loci, supposed to be a single gene. • Since the emphasis is on well-characterised loci, Locus Link is not complete.

  7. From clone information to genes and proteins: multiple ways to go • There are other projects like RefSeq (NCBI) or TIGR Gene Indices. • According to the cross-references available for a certain microarray, one or the other may be advantageous

  8. An example: The human genome • With the completion of the human genome sequence, you’d think that such ambiguities can be resolved. In fact, that is not the case. • Part of the problem is due to the fact that it is hard to predict gene structure (intron/exon) without knowing the entire mRNA sequence, which happens for about two-thirds of all genes. • Then, there are errors in the assembly (putting together the sequence snippets). A typical symptom is that a gene appears to map to multiple loci on the same chromosome, with very high sequence similarity. • But there are also sequences that are nearly indentical, but duplicated. This has happened not long ago in evolution by means of transposable elements.

  9. The human genome: Some figures • Currently, it’s estimated that the human genome contains about 25,000 – 30,000 genes that code for 50,000 – 100,000 different transcripts (and thus, proteins). • Unigene (human section) contains 105,680 clusters, but 45,999 • of them are of size 2 or less. • RefSeq DNA contains 28,097 human sequences. • ENSEMBL contains 21,787 predicted genes, 31,609 predicted transcripts. • Fully computational methods like Genscan produce more than 65,000 predictions. • LocusLink contains 15,248 genes with known function, and further 6038 genes without function annotation

  10. Function annotation • Probably, the most important thing you want to know is what the genes or their products are concerned with, i.e. their function. • Function annotation is difficult: • Different people use different words for the same function, or • may mean different things by the same word. • The context in which a gene was found (e.g. “TGF-induced gene”) may not be particularly associated with its function. • Inference of function from sequence alone is error-prone and sometimes unreliable. • The best function annotation systems (GO,SwissProt) use human beings who read the literature before assigning a function to a gene

  11. The Gene Ontology • To overcome some of the problems, an annotation system has been created: The Gene Ontology (http://www.geneontology.org). • It represents a unified, consistent system, i.e. terms occur only once, and there is a dictionary of allowed words. • Furthermore, terms are related to each other: the hierarchy goes from very general terms to very detailed ones.

  12. Cross-references with GO • The GO database exists independently from other annotation databases • There exist cross-references (GOA) enabling to relate other annotations with those contained in GO

  13. Bioconductor and annotations • Annotation information is managed in Bioconductor through metadata packages • These packages contain one-to-one and one-to-many mappings for frequently used chips, especially Affymetrix • Information available includes gene names, gene symbol, database accession numbers, Gene Ontology function description, enzmye classification number (EC), relations to PubMed abstracts, and others. • There are several packages implementing functionalities to deal with annotation information: annotate, Annbuilder, ontoTools, GOstats and many more

  14. Static vs. Dynamic Annotation Static Annotation: • Bioconductor packages containing annotation information that are installed locally on a computer • well-defined structure • reproducible analyses • no need for network connection Dynamic Annotation: • stored in a remote database • more frequent updates  possibly different result when repeating analyses • more information • one needs to know about the structure of the database, the API of the webservice etc.

  15. Available Metadata • EntrezGene • is a catalog of genetic loci that connects curated sequence information to official nomenclature. It replaced LocusLink. • UniGene • defines sequence clusters. UniGene focuses on protein-coding genes of the nuclear genome (excluding rRNA and mitochondrial sequences). • RefSeq • is a non-redundant set of transcripts and proteins of known genes for many species, including human, mouse and rat. • Enzyme Commission (EC) • numbers are assigned to different enzymes and linked to genes through EntrezGene.

  16. Available Metadata • Gene Ontology (GO) • is a structured vocabulary of terms describing gene products according to molecular function, biological process, or cellular component • PubMed • is a service of the U.S. National Library of Medicine. PubMed provides a rich resource of data and tools for papers in journals related to medicine and health. While large, the data source is not comprehensive, and not all papers have been abstracted

  17. Available Metadata • OMIM • Online Mendelian Inheritance in Man is a catalog of human genes and genetic disorders. • NetAffx • Affymetrix’ NetAffx Analysis Center provides annotation resources for Affymetrix GeneChip technology. • KEGG • Kyoto Encyclopedia of Genes and Genomes; a collection of data resources including a rich collection of pathway data. • IntAct • Protein Interaction data, mainly derived from experiments. • Pfam • Pfam is a large collection of multiple sequence alignments and hidden Markov models covering manycommon protein domains and families.

  18. Available Metadata • Chromosomal Location • Genes are identified with chromosomes, and where appropriate with strand. • Data Archives • The NCBI coordinates the Gene Expression Omnibus (GEO); TIGR provides the Resourcerer database, and the EBI runs ArrayExpress.

  19. Annotation Packages • An early design decision was to provide metadata on a per chip-type basis (e.g. hgu133a, hgu95av2) • Each annotation package contains objects that provide mappings between identifiers (genes, probes, …) and different types of annotation data • One can list the content of a package: > library("hgu133a") > ls("package:hgu133a") [1] "hgu133a" "hgu133aACCNUM" [3] "hgu133aCHR" "hgu133aCHRLENGTHS" [5] "hgu133aCHRLOC" "hgu133aENTREZID" [7] "hgu133aENZYME" "hgu133aENZYME2PROBE" [9] "hgu133aGENENAME" "hgu133aGO" [11] "hgu133aGO2ALLPROBES" "hgu133aGO2PROBE" [13] "hgu133aLOCUSID" "hgu133aMAP" [15] "hgu133aMAPCOUNTS" "hgu133aOMIM" [17] "hgu133aORGANISM" "hgu133aPATH" [19] "hgu133aPATH2PROBE" "hgu133aPFAM" [21] "hgu133aPMID" "hgu133aPMID2PROBE" [23] "hgu133aPROSITE" "hgu133aQC" [25] "hgu133aREFSEQ" "hgu133aSUMFUNC_DEPRECATED" [27] "hgu133aSYMBOL" "hgu133aUNIGENE"

  20. A little bit of history... (the pre-SQL era) before: hgu95av2 now: hgu95av2.db

  21. Annotation Packages • Objects in annotation packages used to be environments, hash tables for mapping  now things are stored in SQLite DB • Mapping only from one identifier to another, hard to reverse • quite unflexible • The user interface still supports many of the old environment-specific interactions: You can access the data directly using any of the standard subsetting or extraction tools for environments: get, mget, $ and [[. > get("201473_at", hgu133aSYMBOL) [1] "JUNB" > mget(c("201473_at","201476_s_at"), hgu133aSYMBOL) $`201473_at` [1] "JUNB" $`201476_s_at` [1] "RRM1" > hgu133aSYMBOL$"201473_at" [1] "JUNB" > hgu133aSYMBOL[["201473_at"]] [1] "JUNB"

  22. Working with Metadata Suppose we are interested in the gene BAD. > gsyms <- unlist(as.list(hgu133aSYMBOL)) > whBAD <- grep("^BAD$", gsyms) > gsyms[whBAD] 1861_at 209364_at "BAD" "BAD" > hgu133aGENENAME$"1861_at" [1] "BCL2-antagonist of cell death"

  23. Working with Metadata Find the pathways that BAD is associated with. > BADpath <- hgu133aPATH$"1861_at" > kegg <- mget(BADpath, KEGGPATHID2NAME) > unlist(kegg) 01510 "Neurodegenerative Disorders" 04012 "ErbB signaling pathway" 04210 "Apoptosis" 04370 … "Colorectal cancer" 05212 "Pancreatic cancer" 05213 "Endometrial cancer" 05215

  24. Working with Metadata We can get the GeneChip probes and the unique EntrezGene loci in each of these pathways. First, we obtain the Affymetrix IDs > allProbes <- mget(BADpath, hgu133aPATH2PROBE) > length(allProbes) [1] 15 > allProbes[[1]][1:10] [1] "206679_at" "209462_at" "203381_s_at" "203382_s_at" [5] "212874_at" "212883_at" "212884_x_at" "200602_at" [9] "211277_x_at" "214953_s_at" > sapply(allProbes, length) 01510 04012 04210 04370 04510 04910 05030 05210 05212 05213 85 169 162 137 413 243 39 167 156 111 05215 05218 05220 05221 05223 194 137 160 117 110

  25. Working with Metadata And then we can map these to their Entrez Gene values. > getEG = function(x) unique(unlist(mget(x, hgu133aENTREZID))) > allEG = sapply(allProbes, getEG) > sapply(allEG, length) 01510 04012 04210 04370 04510 04910 05030 05210 05212 05213 37 84 81 67 187 130 18 82 72 51 05215 05218 05220 05221 05223 85 68 74 53 53

  26. .db Packages • Data in the new .db annotation packages is stored in SQLite databases  much more efficient and flexible • old environment-style access provided by objects of class Bimap (package AnnotationDbi) left object right object left object right object left object right object

  27. .db Packages • Data in the new .db annotation packages is stored in SQLite databases  much more efficient and flexible • old environment-style access provided by objects of class Bimap (package AnnotationDbi) left object right object attr1 = value1 attr2 0 value2 left object right object name left object right object  bipartite graph

  28. DBI • collection of classes and methods for database interaction • they abstract the particular implementations of common standard operations on different types of databases • resultSet: operations are performed on the database, the user controls how much information is returned • dbSendQuery create result set • dbGetQuery get all results dbGetQuery(connection, sql query)

  29. .db Packages Notice that there are a few more entries here. They give you access to a connection to the database. > library("hgu133a.db") > ls("package:hgu133a.db") [1] "hgu133aACCNUM" "hgu133aALIAS2PROBE" [3] "hgu133aCHR" "hgu133aCHRLENGTHS" [5] "hgu133aCHRLOC" "hgu133aENTREZID" [7] "hgu133aENZYME" "hgu133aENZYME2PROBE" [9] "hgu133aGENENAME" "hgu133aGO" [11] "hgu133aGO2ALLPROBES" "hgu133aGO2PROBE" [13] "hgu133aMAP" "hgu133aMAPCOUNTS" [15] "hgu133aOMIM" "hgu133aORGANISM" [17] "hgu133aPATH" "hgu133aPATH2PROBE" [19] "hgu133aPFAM" "hgu133aPMID" [21] "hgu133aPMID2PROBE" "hgu133aPROSITE" [23] "hgu133aREFSEQ" "hgu133aSYMBOL" [25] "hgu133aUNIGENE" "hgu133a_dbInfo" [27] "hgu133a_dbconn" "hgu133a_dbfile" [29] "hgu133a_dbschema"

  30. > con <- hgu133a_dbconn() > q1 <- "select symbol from gene_info“ > head(dbGetQuery(con ,q1)) symbol 1 A2M 2 NAT1 3 NAT2 4 SERPINA3 extract information from a database table as data.frame > toTable(hgu133aSYMBOL)[1:3,] probe_id symbol 1 217757_at A2M 2 214440_at NAT1 3 206797_at NAT2 reverse mapping > revmap(hgu133aSYMBOL)$BAD [1] "1861_at" "209364_at"

  31. Lkeys, Rkeys: Get left and right keys of a Bimap object > head(Lkeys(hgu133aSYMBOL)) [1] "1007_s_at" "1053_at" "117_at" "121_at" "1255_g_at" "1294_at" > head(Rkeys(hgu133aSYMBOL)) [1] "A2M" "NAT1" "NAT2" "SERPINA3" "AADAC" "AAMP" nhit: number of hits for every left key in a Bimap object > table(nhit(revmap(hgu133aSYMBOL))) 1 2 3 4 5 6 7 8 9 10 11 12 13 18 19 8101 2814 1273 475 205 77 19 15 5 3 4 1 2 1 1

More Related