Module 1: Gene Lists 1 Canadian Bioinformatics Workshops

Module 1: Gene Lists 2

3 Module 1: Introduction to Gene Lists Gary Bader University of Toronto

Module 1: Gene Lists 4 Gene Lists Overview Interpreting gene lists Gene attributes –Gene Ontology Ontology Structure Annotation –BioMart + other sources Gene identifiers and mapping

Module 1: Gene Lists 5 Interpreting Gene Lists My cool new screen worked and produced 1000 hits! …Now what? Genome-Scale Analysis (Omics) –Genomics, Proteomics GenMAPP.org ? Ranking or clustering

Module 1: Gene Lists 6 Interpreting Gene Lists My cool new screen worked and produced 1000 hits! …Now what? Genome-Scale Analysis (Omics) –Genomics, Proteomics GenMAPP.org Ranking or clustering Eureka! New heart disease gene!

Module 1: Gene Lists 7 Cardiomyopathy: Downregulated Genes GenMAPP.org

Module 1: Gene Lists 8 Cardiomyopathy: Downregulated Genes Fatty Acid Degradation? Other pathways / processes? GenMAPP.org Anecdote vs. significance?

Module 1: Gene Lists 9 Where Do Gene Lists Come From? Molecular profiling e.g. mRNA, protein –Identification  Gene list –Quantification  Gene list + values –Ranking, Clustering Interactions: Protein interactions, Transcription factor binding sites (ChIP) Genetic screen e.g. of knock out library Association studies (Genome-wide) –Single nucleotide polymorphisms (SNPs) –Copy number variants (CNVs) Other examples?

Module 1: Gene Lists 10 What Do Gene Lists Mean? Biological system: complex, pathway Similar gene function e.g. protein kinase Similar cell or tissue location Chromosomal location (linkage, CNVs) Data

Module 1: Gene Lists 11 Biological Questions Step 1: What do you want to accomplish with your list (hopefully part of experiment design! ) –Summarize biological processes or other aspects of gene function –Controller for a process (TF) –Find new pathways –Discover new gene function –Correlation to a disease or phenotype (candidate gene prioritization) –Differential analysis – what’s different between samples?

Module 1: Gene Lists 12 Biological Answers Computational analysis methods –Gene set analysis: summarize –Gene regulation network analysis –Pathway and network analysis –Gene function prediction But first! Gene list basics…

Module 1: Gene Lists 13 Gene Lists Overview Interpreting gene lists Gene attributes –Gene Ontology Ontology Structure Annotation –BioMart + other sources Gene identifiers and mapping

Module 1: Gene Lists 14 Gene Attributes Available in databases Function annotation –Biological process, molecular function, cell location Chromosome position Disease association DNA properties –TF binding sites, gene structure (intron/exon), SNPs Transcript properties –Splicing, 3’ UTR, microRNA binding sites Protein properties –Domains, secondary and tertiary structure, PTM sites Interactions with other genes

Module 1: Gene Lists 15 Gene Attributes Available in databases Function annotation –Biological process, molecular function, cell location Chromosome position Disease association DNA properties –TF binding sites, gene structure (intron/exon), SNPs Transcript properties –Splicing, 3’ UTR, microRNA binding sites Protein properties –Domains, secondary and tertiary structure, PTM sites Interactions with other genes

Module 1: Gene Lists 16 What is the Gene Ontology (GO)? Set of biological phrases (terms) which are applied to genes: –protein kinase –apoptosis –Membrane Ontology: A formal system for describing knowledge Jane EBI

Module 1: Gene Lists 17 GO Structure Terms are related within a hierarchy –is-a –part-of Describes multiple levels of detail of gene function Terms can have more than one parent or child

Module 1: Gene Lists 18 GO Structure cell membrane chloroplast mitochondrial chloroplast membrane is-a part-of

Module 1: Gene Lists 19 GO is Species Independent Some terms, especially lower-level, detailed terms may be specific to a certain group –e.g. photosynthesis But when collapsed up to the higher levels, terms are not dependent on species

Module 1: Gene Lists 20 What GO Covers? GO terms divided into three parts: –cellular component –molecular function –biological process

Module 1: Gene Lists 21 Cellular Component Where a gene product acts

Module 1: Gene Lists Molecular Function Activities or “jobs” of a gene product glucose-6-phosphate isomerase activity

Module 1: Gene Lists 23 Biological Process A commonly recognized series of events cell division

Module 1: Gene Lists 24 Terms Where do GO terms come from? –GO terms are added by editors at EBI and gene annotation database groups –Terms added by request –Experts help with major development –25206 terms, 98.1% with definitions biological_process 2101 cellular_component 8280 molecular_function As of June 5, 2008

Module 1: Gene Lists 25 Genes are linked, or associated, with GO terms by trained curators at genome databases –Known as ‘gene associations’ or GO annotations –Multiple annotations per gene Some GO annotations created automatically Annotations

Module 1: Gene Lists 26 Contributing Databases –Berkeley Drosophila Genome Project (BDGP)Berkeley Drosophila Genome Project (BDGP –dictyBase (Dictyostelium discoideum)dictyBase –FlyBase (Drosophila melanogaster)FlyBase –GeneDB (Schizosaccharomyces pombe, Plasmodium falciparum, Leishmania major and Trypanosoma brucei)GeneDBSchizosaccharomyces pombe –UniProt Knowledgebase (Swiss-Prot/TrEMBL/PIR-PSD) and InterPro databasesUniProt KnowledgebaseInterPro –Gramene (grains, including rice, Oryza)Gramene –Mouse Genome Database (MGD) and Gene Expression Database (GXD) (Mus musculus)Mouse Genome Database (MGD) and Gene Expression Database (GXD) –Rat Genome Database (RGD) (Rattus norvegicus) –ReactomeReactome –Saccharomyces Genome Database (SGD) (Saccharomyces cerevisiae)Saccharomyces Genome Database (SGD) –The Arabidopsis Information Resource (TAIR) (Arabidopsis thaliana)The Arabidopsis Information Resource (TAIR) –The Institute for Genomic Research (TIGR): databases on several bacterial speciesThe Institute for Genomic Research (TIGR) –WormBase (Caenorhabditis elegans)WormBase –Zebrafish Information Network (ZFIN): (Danio rerio)Zebrafish Information Network (ZFIN)

Module 1: Gene Lists 27 Species Coverage All major eukaryotic model organism species Human via GOA group at UniProt Several bacterial and parasite species through TIGR and GeneDB at Sanger New species annotations in development

Module 1: Gene Lists 28 Anatomy of a GO Annotation Three key parts: –Gene name/id –GO term(s) –Evidence for association Electronic Manual

Module 1: Gene Lists 29 Annotation Sources Manual annotation –Created by scientific curators High quality Small number (time-consuming to create) Electronic annotation –Annotation derived without human validation Computational predictions (accuracy varies) Lower ‘quality’ than manual codes Key point: be aware of annotation origin

Module 1: Gene Lists 30 Evidence Types ISS: Inferred from Sequence/structural Similarity IDA: Inferred from Direct Assay IPI: Inferred from Physical Interaction IMP: Inferred from Mutant Phenotype IGI: Inferred from Genetic Interaction IEP: Inferred from Expression Pattern TAS: Traceable Author Statement NAS: Non-traceable Author Statement IC: Inferred by Curator ND: No Data available IEA: Inferred from electronic annotation

Module 1: Gene Lists 31 Variable Coverage

Module 1: Gene Lists 32 GO Slim Sets GO has too many terms for some uses –Summaries (e.g. Pie charts) GO Slim is an official reduced set of GO terms –Generic, plant, yeast Crockett DK et al. Lab Invest Nov;85(11):

Module 1: Gene Lists 33 GO Software Tools GO resources are freely available to anyone without restriction –Includes the ontologies, gene associations and tools developed by GO Other groups have used GO to create tools for many purposes –

Module 1: Gene Lists 34 Accessing GO: QuickGO

Module 1: Gene Lists 35 Other Ontologies

Module 1: Gene Lists 36 Gene Attributes Function annotation –Biological process, molecular function, cell location Chromosome position Disease association DNA properties –TF binding sites, gene structure (intron/exon), SNPs Transcript properties –Splicing, 3’ UTR, microRNA binding sites Protein properties –Domains, secondary and tertiary structure, PTM sites Interactions with other genes

Module 1: Gene Lists 37 Gene Attributes Function annotation –Biological process, molecular function, cell location Chromosome position Disease association DNA properties –TF binding sites, gene structure (intron/exon), SNPs Transcript properties –Splicing, 3’ UTR, microRNA binding sites Protein properties –Domains, secondary and tertiary structure, PTM sites Interactions with other genes

Module 1: Gene Lists 38 Sources of Gene Attributes Ensembl BioMart (eukaryotes) – Entrez Gene (general) – b=gene Model organism databases –E.g. SGD: Many others: discuss during lab

Module 1: Gene Lists 39 Ensembl BioMart Convenient access to gene list annotation Select genome Select filters Select features to download

Module 1: Gene Lists 40 What Have We Learned? Many gene attributes in databases –Gene Ontology (GO) provides gene function annotation GO is a classification system and dictionary for biological concepts Annotations are contributed by many groups More than one annotation term allowed per gene Some genomes are annotated more than others Annotation comes from manual and electronic sources GO can be simplified for certain uses (GO Slim) Many other gene attributes available from Ensembl and Entrez Gene

Module 1: Gene Lists 41 Gene Lists Overview Interpreting gene lists Gene function attributes –Gene Ontology Ontology Structure Annotation –BioMart + other sources Gene identifiers and mapping

Module 1: Gene Lists 42 Gene and Protein Identifiers Identifiers (IDs) are names or numbers that help track database records –E.g. Social Insurance Number, Entrez Gene ID Gene and protein information stored in many databases –  Genes have many IDs Records for: Gene, DNA, RNA, Protein –Important to use the correct record type –E.g. Entrez Gene records don’t store sequence. They link to DNA regions, RNA transcripts and proteins.

Module 1: Gene Lists 43 NCBI Database Links NCBI: U.S. National Center for Biotechnology Information Part of National Library of Medicine (NLM)

Module 1: Gene Lists 44 Common Identifiers Species-specific HUGO HGNC BRCA2 MGI MGI: RGD 2219 ZFIN ZDB-GENE FlyBase CG9097 WormBase WBGene or ZK SGD S or YDL029W Annotations InterPro IPR OMIM Pfam PF09104 Gene Ontology GO: SNPs rs Experimental Platform Affymetrix _3p_s_at Agilent A_23_P99452 CodeLink GE60169 Illumina GI_ S Gene Ensembl ENSG Entrez Gene 675 Unigene Hs RNA transcript GenBank BC RefSeq NM_ Ensembl ENST Protein Ensembl ENSP RefSeq NP_ UniProt BRCA2_HUMAN or A1YBP1_HUMAN IPI IPI EMBL AF PDB 1MIU Red = Recommended

Module 1: Gene Lists 45 Identifier Mapping So many IDs! –Mapping (conversion) is a headache Four main uses –Searching for a favorite gene name –Link to related resources –Identifier translation E.g. Genes to proteins, Entrez Gene to Affy –Unification during dataset merging Equivalent records

Module 1: Gene Lists 46 ID Mapping Services Synergizer – synergizer/translate Ensembl BioMart – PIR – search/idmapping.shtml

Module 1: Gene Lists 47 ID Mapping Challenges Gene name ambiguity –Not a good ID Excel error-introduction –OCT4 is changed to October-4 Problems reaching 100% coverage –E.g. due to version issues –Use multiple sources to increase coverage Zeeberg BR et al. Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics BMC Bioinformatics Jun 23;5:80

Module 1: Gene Lists 48 What Have We Learned? Genes and their products and attributes have many identifiers (IDs) Genomics requirement to convert or map IDs from one type to another ID mapping services are available Use standard, commonly used IDs to avoid ID mapping challenges

Module 1: Gene Lists minute break Back at 10:50 am Next: Lab – Working with Gene Lists

Module 1: Gene Lists 50 Lab: Getting Gene Attributes Use yeast demo gene list –Learn about gene identifiers –Learn to use Synergizer and BioMart If time, do it again with your own gene list –If compatible with covered tools, run the analysis. If not, instructors will recommend tools for you.

Module 1: Gene Lists 51 Lab Use yeast demo dataset Convert Gene IDs to Entrez Gene –Use Synergizer Gene list with GO annotation + evidence codes –Use Ensembl BioMart Summarize annotation and evidence codes in a spreadsheet

Module 1: Gene Lists 52 Lunch On your own E.g. Cafeteria Downstairs Back at 1:30pm