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Genome Annotation

Din

es

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www.genomesonline.org

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protein-coding genes, nonprotein-coding genes

easier to find than other functionalelements

why?genes are transcribedwhich meansthat we can identify them by looking atRNA

traditionally this has been done bycDNA or EST sequencing, morerecently by microarray, SAGE,MPSS, etc.

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protein-coding genes, nonprotein-coding genes

we can also find genes ab initio usingcomputational methods

this is most suited to protein-coding genes

why?

protein-coding genes have recognizablefeatures

open reading frames (ORFs)

codon bias known transcription and translationalstart and stop motifs (promoters, 3 poly-

A sites) -

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ab initio gene discovery

Protein-coding genes have recognizable features

We can design software to scan the genome andidentify these features

Some of these programs work quite well, especially inbacteria and simpler eukaryotes with smaller and

more compact genomesIts a lot harder for the higher eukaryotes where thereare a lot of long introns, genes can be found withinintrons of other genes, etc.

We tend to do OK finding protein coding regions,but miss a lot of non-coding 5 exons and thelike

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ab initio gene discoveryvalidatingpredictions and refining gene models

Standard types of evidence for validation ofpredictions include:

match to previously annotated cDNA

match to EST from same organism

similarity of nucleotide or conceptuallytranslated protein sequence to sequences inGenBank

(translation works betterwhy?)

protein structure prediction match to a PFAM domain

associated with recognized promoter sequences, ie TATAbox, CpG island

known phenotype from mutation of the locus

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Finding nonprotein-coding genes

e.g., tRNA, rRNA, snoRNA, miRNA, various otherncRNAs

Harder to find than protein-coding genes

Why?

often not poly-A taileddont end up in cDNAlibraries

no ORF

constraint on sequence divergence at nucleotidenot protein level, so homology is harder todetect

So, how do we find these?

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Finding nonprotein-coding genes

secondary structure

homology, especially alignment of related species

experimentally

isolation through non-polyA dependent cloningmethods

microarrays

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ab initio gene discoveryapproaches

Most gene-discovery programs makes use of someform ofmachine learning algorithm. A machinelearning algorithm requires a training setof inputdata that the computer uses to learn how to find apattern.

Two common machine learning approaches used ingene discovery (and many other bioinformaticsapplications) are artificial neural networks (ANNs)and hidden Markov models (HMMs).

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ab initio gene discoveryHMMs

An example state diagram for an HMM for genediscovery is this simplified version of one used byGenescan:

begin

generegion

start

translation

donor

splicesite

acceptor

splicesite

stop

translation

end

generegion

single exon

exonfinalexon

initialexon

5 UTR 3 UTR

intron

Each box and arrow has associated transitionprobabilities, and emission probabilities for emissionof nucleotides (dotted arrow). These are learned fromexamples of known gene models and provide the

probability that a stretch of sequence is a gene.

A,T,G,C

adapted from Gibson and Muse,A Primer ofGenome Science

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Despite good progress in identifying bothprotein coding and non-protein codinggenes, much work remains to be donebefore even the best-studied genomes are

fully annotated. For the higher eukaryotes,only a tiny percentage of features such asTFBSs, CRMs, and other non-gene featureshave so far been indentified.

Genome Annotationmuch work remains

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The value of genome sequences liesin their annotation

Annotation Characterizing genomicfeatures using computational andexperimental methods

Genes: Four levels of annotation

Gene Prediction Where are genes?

What do they look like?

Domains What do the proteins do?

Role What pathway(s) involved in?

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How many genes?

Consortium: 35,000 genes?

Celera: 30,000 genes?

Affymetrix: 60,000 human genes on

GeneChips?

Incyte and HGS: over 120,000 genes?

GenBank: 49,000 unique gene coding

sequences?

UniGene: > 89,000 clusters of unique

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Current consensus (in flux )

20-25000 protein-coding genes

19,599 known genes, another 2,188 DNA segments

predicted to be protein coding genes.

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How to we get from here

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Complete DNA segments responsible tomake functional products

Products

Proteins

Functional RNA molecules

RNAi (interfering RNA)

rRNA (ribosomal RNA)

snRNA (small nuclear)

snoRNA (small nucleolar)

tRNA (transfer RNA)

What are genes? - 1

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What are genes? - 2

Definition vs. dynamic concept

Consider

Prokaryotic vs. eukaryotic gene models

Introns/exons

Posttranscriptional modifications

Alternative splicing

Differential expression

Genes-in-genes

Genes-ad-genes

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Prokaryotic gene model: ORF-genes

Small genomes, high gene density

Haemophilus influenza genome 85% genic

Operons

One transcript, many genes

No introns.

One gene, one protein

Open reading frames

One ORF per gene

ORFs begin with start,

end with stop codon (def.)TIGR: http://www.tigr.org/tigr-scripts/CMR2/CMRGenomes.spl

NCBI: http://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.html

http://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.tigr.org/tigr-scripts/CMR2/CMRGenomes.splhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.tigr.org/tigr-scripts/CMR2/CMRGenomes.spl

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Eukaryotic gene model: spliced genes

n Posttranscriptional modificationu 5-CAP, polyA tail, splicing

n Open reading framesu Mature mRNA contains ORFu All internal exons contain open read-

throughu Pre-start and post-stop sequences are

UTRs

http://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.html

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Expansions and Clarifications

ORFs

Start triplets stop

Prokaryotes: gene = ORF

Eukaryotes: spliced genes or ORF genes

Exons

Remain after introns have been removed

Flanking parts contain non-coding sequence(5- and 3-UTRs)

http://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.htmlhttp://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.html

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http://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.html