Genome Annotation

• Now that you’ve assembled your genome, what is next?

• GENOME ANNOTATION

• What is that?• Why is it important?• How do you do it?

Challenges to Genome Annotation?

• Finding genes involves computational methods as well as experimental validation

• Computational methods are often inadequate, and often generate erroneous ‘gene’ (false positive) sequences which:– Are missing exons– Have incorrect exons– Over predict genes– Where the 5’ and 3’ UTR are missing

What kinds of things are we looking to annotate?

• CDS - coding sequences• mRNA• Alternative RNA• Promoter and Poly-A Signal• Pseudogenes• ncRNA

Pseudogenes• Could be as high as 20-30% of all Genomic sequence

predictions could be pseudogene• Non-functional copy of a gene

– Processed pseudogene• Retro-transposon derived• No 5’ promoters• No introns• Often includes polyA tail

– Non-processed pseudogene• Gene duplication derived

– Both include events that make the gene non-funtional• Frameshift• Stop codons

• We assume pseudogenes have no function, but we really don’t know!

Noncoding RNA (ncRNA)• ncRNA represent 98% of all transcripts in a mammalian cell• ncRNA have not been taken into account in gene counts

• cDNA• ORF computational prediction• Comparative genomics looking at ORF

• ncRNA can be:– Structural– Catalytic– Regulatory

• tRNA – transfer RNA: involved in translation• rRNA – ribosomal RNA: structural component of ribosome, where

translation takes place• snoRNA – small nucleolar RNA: functional/catalytic in RNA

maturation• Antisense RNA: gene regulation/silencing?

Covariance model searches are extremely compute intensive. A small model (like tRNA) can search a sequence database at a rate of around 300 bases/sec. The compute time scales roughly to the 4th power of the length of the RNA, so larger models quickly become infeasible without significant compute resources.

BLAST• Seeks high-scoring segment pairs (HSP)

– pair of sequences that can be aligned without gaps– when aligned, have maximal aggregate score

(score cannot be improved by extension or trimming)– score must be above score threshold S

• Public Search engines– WWW search form

http://www.ncbi.nlm.nih.gov/BLAST– Unix command line

blastall -p progname -d db -i query > outfile

Which Matrix?• Triple-PAM strategy (Altschul, 1991)

– PAM 40 Short alignments, highly similar• tblastn against ESTs

– PAM 120– PAM 250 Longer, weaker local alignments

• Looking in the twilight zone• BLOSUM (Henikoff, 1993)

– BLOSUM 90 Short alignments, highly similar– BLOSUM 62 Most effective in detecting known

members of a protein family• Standard on NCBI server – works in most cases

– BLOSUM 30 Longer, weaker local alignments

Protein coding genes in prokaryotes, and simple eukaryotes

• Use ORF finderhttp://www.ncbi.nlm.nih.gov/gorf/orfig.cgi

• Simple ATG/Stop• Simple link to FASTA formatted files and BLAST.• Problems:– In frame Methionine– Small protein

• Solution: comparative genomics

Figure 11 from: Methods in comparative genomics: genome correspondence, gene identification and regulatory motif discovery. Kellis M, Patterson N, Birren B, Berger B, Lander ES. J Comput Biol. 2004;11(2-3):319-55.

Saccharomyces cerevisiae.Saccharomyces paradoxus, Saccharomyces mikatae,Saccharomyces bayanus

Ab initio gene identification

• Goals– Identify coding exons– Seek gene structure information– Get a protein sequence for further analysis

• Relevance– Characterization of anonymous DNA genomic

sequences– Works on all DNA sequences

14Lecture 4.2

Gene-Finding StrategiesGenomic Sequence

ComparativeSite-BasedContent-Based

Bulk properties ofsequence:• Open reading frames• Codon usage• Repeat periodicity• Compositional

complexity

Absolute properties ofsequence:• Consensus sequences• Donor and acceptor

splice sites• Transcription factor

binding sites• Polyadenylation

signals• “Right” ATG start• Stop codons

out-of-context

Inferences basedon sequence homology:• Protein sequence

with similarity totranslated productof query

• Modular structure of proteins usually

precludes findingcomplete gene

Gene-Finding Methods

Genomic Sequence

Neural NetworkRule-Based

Cutoff method:• Criteria applied sequentially

to identify possible exons• Rank or eliminate candidates

from consideration based onpre-determined cutoff ateach step

Composite method:• Criteria applied in parallel• Training sets used to optimize performance• Weight scores in order of

importance

Evaluation Statistics

Actual

Predicted

TP FP TN FN TP FN TN

Sensitivity Fraction of actual coding regions that are correctlypredicted as coding

Specificity Fraction of the prediction that is actually correct

Correlation Combined measure of sensitivity and specificity,Coefficient ranging from –1 (always wrong)

to +1 (always right)

The Process• Compute the prediction• Confirm with biological sequences (also with computational

tools)• Integrate all of this• Annotate genome (often via a GUI: Graphical User Interface)• Validate• Re-annotate/Update• Check it twice• Submit to GenBank

Lots of Software:• EnsEMBL (EBI)• Sequin (NCBI)• PseudoCAP (SFU)• GMOD (CSHL)• Pegasys (UBiC)• Apollo (EBI/Berkeley)• GeneMark (Georgia Institute of Tech)• GeneScan (MIT)• GenomeThreader (University of Hamberg)• HMMgene (Technical University of Denmark)

GenBank Features

-10_signal-35_signal3'clip3'UTR5'clip5'UTRattenuatorCAAT_signalCDSconflictC_regionD-loopD_segmentenhancerexon

GC_signalgeneiDNAintronJ_segmentLTRmat_peptidemisc_bindingmisc_differencemisc_featuremisc_recombmisc_RNAmisc_signalmisc_structuremodified_base

mRNAN_regionold_sequencepolyA_signalpolyA_siteprecursor_RNAprimer_bindprim_transcriptpromoterprotein_bindRBSrepeat_regionrepeat_unitrep_originrRNA

satellitescRNAsig_peptidesnoRNAsnRNAS_regionstem_loopSTSTATA_signalterminatortransit_peptidetRNAunsurevariationV_regionV_segment

GenBank Features: the important ones

-10_signal-35_signal3'clip3'UTR5'clip5'UTRattenuatorCAAT_signalCDSconflictC_regionD-loopD_segmentenhancerexon

GC_signalgeneiDNAintronJ_segmentLTRmat_peptidemisc_bindingmisc_differencemisc_featuremisc_recombmisc_RNAmisc_signalmisc_structuremodified_base

mRNAN_regionold_sequencepolyA_signalpolyA_siteprecursor_RNAprimer_bindprim_transcriptpromoterprotein_bindRBSrepeat_regionrepeat_unitrep_originrRNA

satellitescRNAsig_peptidesnoRNAsnRNAS_regionstem_loopSTSTATA_signalterminatortransit_peptidetRNAunsurevariationV_regionV_segment

Gene Prediction Caveats• Predictions are of protein coding regions

– Do not detect non-coding areas (5’ and 3’ UTR)– Non-coding RNA genes are missed

• Predictions are for “typical” genes– Must predict a beginning and an end– Partial or multiple genes are often missed– Training sets may be biased– Methods are sensitive to G+C content– Weighting of factors may be inordinately biased

Genome annotation problems:• Assembling the genome• Analysis & interpretation• Lack of consistency from gene to gene• Lack of consistency from person to person • Lack of controlled vocabulary• Parts we don’t know• Bacteria vs mammals• Graphical user interface• Gene expression/molecular interactions• Dimensions• Updates and maintenance

The ideal annotation of “MyGene”

MyGene

All mRNAs

All proteins

All structures

All SNPs

All clones

• All protein modifications• Ontologies • Interactions (complexes, pathways, networks)•Expression (where and when, and how much)•Evolutionary relationships

Promoter(s)

Some Concluding remarks

• Trust but verify• Beware of gene prediction tools!• Always use more than one gene prediction

tool and more than one genome when possible.

• Active area of bioinformatics research, so be mindful of the new literature in this .