Lecture 4:Gene Annotation& Gene Ontology

June 11, 2015

Gene lists in a manuscript

• Official gene ID, symbol and name• Fold-change• Additional annotation

– Role in cell, protein domain, predicted function, ect

p38MAPK-dependent factors are expressed in the TME of breast cancer (BC) lesions.

Elise Alspach et al. Cancer Discovery 2014;4:716-729

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?

Sequence databases

Genbank EMBL-EBI

GenPep trEMBL

Joe/Jill lab geek

Hans/Heidi lab geek

automatic translation

error correction & limited annotation

RefSeq UniprotKB/ trEMBL

Expert annotation from literature

Gene DB UniprotKB/SwissProt

Removal of seqs >90% identical

NCBI NR

DNA sequence

DNA sequence

Proteins

ENSEMBL

Most common database IDs

• Refseq records (NCBI)– NM_ (mRNA) & NP_ (proteins), ~61 million records

• ENSEMBL records (EBI)– ENSG (gene), ENST (transcript), ENSP (proteins)

• Gene database (NCBI or Entrez)– Gene DB IDs are all digits, ranging in length from 2->10 – Started with human genes, ~11 million records

• Uniprot database (EMBL-EBI, SIB and PIR)– Q####, P####, ect; – Focused on high-quality annotations of proteins~550,000 out of ~50 million proteins in TrEMBL

Identifying the genes

• For most downstream analyses, you will want to use a database ID rather than gene symbols

• Why?– Database IDs (Gene, Refseq, Uniprot) more stable– Less likely to be misinterpreted – Same gene symbol used in more than one organism

• Which database ID?– NCBI Gene DB (or Entrez Gene ID)– Uniprot (only proteins)– Refseq (redundancy from transcript isoforms)– ENSEMBL, less commonly used

Converting database IDs

bioDBnet (db2db) http://biodbnet.abcc.ncifcrf.gov/All types of gene products

EBI BioMart (Tools -> id conversion)http://www.biomart.org/Biased towards protein-coding

DAVID bioinformatics (Gene ID conversion)http://david.abcc.ncifcrf.gov/Biased towards protein-coding

Types of genes

• RNAseq not biased towards protein-coding genes, so you will get data from non-coding RNA, pseudo-genes and others.

• Can obtain data from bioDBnet and use Excel to categorize your list by type of gene product

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

• Attempt to standardize the representation of genes and 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 Component

Cellular components of a virus different than a cell

Cellular Component

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

Molecular Function

Activities or “jobs” of a gene product

glucose-6-phosphate isomerase activity

Molecular Function

insulin bindinginsulin 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

Biological Process

regulation of gluconeogenesis

Biological Process

limb development

Biological Process

courtship behavior

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

From the Ex 2 gene list• BEST1

– Bestrophin 1– What is its role in the cell?

• Gene ontology biological process:– Chloride transmembrane transport– Regulation calcium ion transport– Visual perception

• GO molecular function:– Chloride channel activity

• GO cellular component– Basolateral plasma membrane– Chloride channel complex

CCL23 from Ex2 list

• Chemokine (C-C motif) ligand 23• Function:

– chemokine receptor binding• Processes include:

– G-protein coupled receptor signaling– Cellular calcium homeostasis– Monocyte chemotaxis

• Component:– Extracellular space

• Generally biological process terms are more useful for putting gene lists into a context

• There are 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 Structure

• Terms are linked by two relationships– is-a – part-of

Ontology Structure

cell

membrane chloroplast

mitochondrial chloroplastmembrane membrane

is-apart-of

is_a

is_a

DNA binding is a type of nucleic acid binding.

GO structure

• GO isn’t just a flat list of biological terms

• terms are related within a hierarchy

Nucleic acid binding is atype of binding.

GO structure

gene A

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

• a name

term: transcription initiation

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 definition

id: GO:0006352

• an ID number

Eachconcept has:

GO terms assigned to BEST1

Types of evidence codes

Experimental:

Computational:

Types of evidence codes

Other evidence codes

Types of evidence codes

Manual annotation

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…

Molecular function

Cellular componentBiological process

GO terms assigned to BEST1

Gene record for BEST1

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

GO & analysis of gene lists

• Many tools exist that use GO to find common biological functions from a list of genes

• WebGestalt, gProfiler, Onto-Express, and GSEA to name a few

• Partek Genomics Suite has built-in GO enrichment• We’ll use PGS and either the web-based

WebGestalt or gProfiler as a comparison

GO 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

microarray

1000 genes experiment100 genes differentially regulated

mitosis – 80/100apoptosis – 40/100Cell proliferation – 30/100glucose transport – 20/100

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 80

Apoptosis 400/1000 40 40

Cell proliferation

100/1000 10 30

Glucose transport

50/1000 5 20

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 & next Tuesday in computer lab

• Managing gene lists with various online database tools

• Filtering your gene list from Ex. 2 so that you have only protein-coding genes and the database IDs or accession numbers you need for later analyses

• Tutorial on different tools for GO enrichment analysis

• Conduct GO enrichment on your list of genes using PGS, DAVID and one other GO tool (web based)