Gene Annotation & Gene Ontology June 1, 2017

Gene lists from RNAseq analysis What do you do with a list of 100s of genes that contain only the following information? Gene name or symbol Ratio between groups (UP or DOWN) One or more database IDs (accession numbers) How do you figure out the role of the genes in the model you are studying?

Gene annotation Process of assigning descriptions to a transcript or gene product. Includes: Official gene symbol & name Protein features: domains, functional elements such as nuclear localization signals Predicted molecular function, biological process and cellular location Experimentally derived information function, process and cellular location References ....

Who does the gene annotation? Refseq & Gene databases NCBI staff Ensemble databases http://useast.ensembl.org EMBL & Welcome Trust at Sanger Institute Uniprot Staff at European Bioinformatics Institute (EBI), Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR) Yeast DB, FlyBase, Mouse Genome Informatics (MGI) & other organism specific databases

Gene record for BEST1

Ensembl Gene record for BEST1

Uniprot record for BEST1

Gene, Ensembl or Uniprot? What information are you looking for? Comfort level with the interface All have a little to LOTS of information Use as a starting point

Dealing with gene lists How can you efficiently categorize the genes in in some biologically meaningful way? Batch download data from Gene or Uniprot and do a lot of reading? PubMed? One approach is to use meta-data in the form of terms assigned to each gene that describe its molecular function, participation in a biological process and its location in a cellular component

Gene Ontology Set of standard biological phrases (terms) which are applied to genes/proteins: protein kinase apoptosis Membrane Standardize the representation of gene product attributes across species and databases Maintained by Gene Ontology consortium http://geneontology.org/ Individual groups contribute taxonomic specific terms

Cellular Component Where a gene product acts Mitochondria

Cellular components not all same between organisms

Cellular Component Ribosome Enzyme complexes in the component ontology refer to places, not activities.

glucose-6-phosphate isomerase activity Molecular Function Activities or “jobs” of a gene product glucose-6-phosphate isomerase activity

insulin receptor activity Molecular Function insulin binding insulin receptor activity

Molecular Function A gene product may have several functions Sets of functions make up a biological process.

Biological Process a commonly recognized series of events cell division

Biological Process transcription

regulation of gluconeogenesis Biological Process regulation of gluconeogenesis

Biological Process limb development

Why use gene ontology? Allows biologists to make queries across large numbers of genes without researching each one individually Can find all the PI3 kinases in a given genome or find all proteins involved in oxidative stress response without prior knowledge of every gene

TARDBP (TDP-43) GO biological process: GO molecular function: 3’UTR mediated mRNA stabilization RNA splicing mRNA processing GO molecular function: RNA binding Double-stranded DNA binding mRNA 3’-UTR binding GO cellular component Cytoplasm Interchromatin granule Nuclear speck

Gene Ontology for analysis Ontology annotation is NOT complete Biological process terms are more useful for putting gene lists into a context More GO terms assigned to process than to function or component Fewest terms assigned to component Function in the absence of any process information can imply a biological role i.e. you are looking for transcription factors responsible for some response Ontology annotation is NOT complete

Ontology Structure Terms are linked by two relationships is-a  part-of 

mitochondrial chloroplast Ontology Structure cell membrane chloroplast mitochondrial chloroplast membrane membrane is-a part-of

Nucleic acid binding is a GO structure Nucleic acid binding is a type of binding. GO isn’t just a flat list of biological terms terms are related within a hierarchy is_a is_a DNA binding is a type of nucleic acid binding.

GO structure gene A A gene (A) that is associated with a term ‘DNA replication’ is automatically annotated to all that terms parent terms. A single gene associated with with a particular term is automatically annotated to all of the parent terms

GO structure This means genes can be grouped according to user-defined levels Allows broad overview of gene set or genome You can use the level of granularity that makes most sense

GO terms term: transcription initiation Each concept has: id: GO:0006352 definition: Processes involved in the assembly of the RNA polymerase complex at the promoter region of a DNA template resulting in the subsequent synthesis of RNA from that promoter. a name an ID number a definition

GO terms assigned to TARDBP

Types of evidence codes Experimental: Inferred from Experiment (EXP) Inferred from Direct Assay (IDA) Inferred from Physical Interaction (IPI) Inferred from Mutant Phenotype (IMP) Inferred from Genetic Interaction (IGI) Inferred from Expression Pattern (IEP)

Types of evidence codes Computational: Inferred from Sequence or structural Similarity (ISS) Inferred from Sequence Orthology (ISO) Inferred from Sequence Alignment (ISA) Inferred from Sequence Model (ISM) Inferred from Genomic Context (IGC) Inferred from Biological aspect of Ancestor (IBA) Inferred from Biological aspect of Descendant (IBD) Inferred from Key Residues (IKR) Inferred from Rapid Divergence(IRD) Inferred from Reviewed Computational Analysis (RCA)

Types of evidence codes Other: Author Statement Evidence Codes Traceable Author Statement (TAS) Non-traceable Author Statement (NAS) Curator Statement Evidence Codes Inferred by Curator (IC) No biological Data available (ND) Automatically-assigned Inferred from Electronic Annotation (IEA)

Manual annotation Molecular function Biological process In this study, we report the isolation and molecular characterization of the B. napus PERK1 cDNA, that is predicted to encode a novel receptor-like kinase. We have shown that like other plant RLKs, the kinase domain of PERK1 has serine/threonine kinase activity, In addition, the location of a PERK1-GTP fusion protein to the plasma membrane supports the prediction that PERK1 is an integral membrane protein…these kinases have been implicated in early stages of wound response… This is an example of how a curator might approach a paper to find GO terms. Biological process Cellular component

Electronic Annotation Annotation derived without human validation mappings file e.g. interpro2go, ec2go. Blast search ‘hits’ Lower ‘quality’ than manual codes Used in non-model organisms Define a similarity cut-off (E-value of 10-25) So electronic annotation is where a human hasn’t looked at an annotation, it’s been done entirely automatically. This can be from a mappings file e.g. InterPro2go, spkw2go, from non-validated sequence similarity, or from a combination of different methods. These electronic methods produce very large numbers of annotations, but because they are not individually validated by a curator, can be thought of as having a lower quality than curator approved annotations.

Quality of annotation varies by organism

GO & analysis of gene lists www.geneontology.org Maintains the databases of GO terms, serves a clearing house for terms as they are assigned in new organisms Tools for exploring gene lists using GO: WebGestalt, gProfiler, Onto-Express, and GSEA to name a few DAVID is a suite of tools for gene enrichment analysis that also includes GO. We’ll use both DAVID and WebGestalt to explore our gene list insert slides from sorin’s talk

Gene Ontology tools input a gene list shows which GO categories have most genes associated with them or are “enriched” provides a statistical measure to determine whether enrichment is significant

Using GO in practice statistical measure how likely your differentially regulated genes fall into that category by chance mitosis – 80/100 apoptosis – 40/100 Cell proliferation – 30/100 glucose transport – 20/100 The better ones include an statistical measure of how likely your differentially regulated genes fall into that category by chance So why is that necessary So imagine you do a microarray with a 1000 genes, and you find that 100 are differentially regualted And these are the GO processes that those differentially regualted genes fall into - it looks like mitosis is overrepresented…. microarray 1000 genes 100 genes differentially regulated experiment

Using GO in practice However, when you look at the distribution of all genes on the microarray: Proportions analysis Chi-squared or Fisher’s exact test Process Genes on array # genes expected (out of 100) # genes observed Mitosis 800/1000 80 Apoptosis 400/1000 40 Cell proliferation 100/1000 10 30 Glucose transport 50/1000 5 20 you can see that 80% of them were involved in mitosis, so the number upregulated is what you’d expect by chance. The category positive regulation of cell proliferation actually contains more differentially regualted genes than you would expect by chance Need a statistical test e.g. Chi-squared to see if this overrepresentation or enrichment of a certain class is statistically significant.

Other sources of annotation Uniprot (Swiss-Prot) keywords Protein domain databases PFAM, Panther, PDB, PROSITE, ect GeneDB summaries from NCBI Protein-protein interactions databases Pathway databases KEGG, BioCarta, BBID, Reactome DAVID incorporates annotation from all of these and clusters the redundant terms

Today in computer lab Tutorial on using DAVID Tutorial on using WebGestalt Analysis of gene lists using DAVID and WebGestalt