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DESCRIPTIONGenome 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 - PowerPoint PPT Presentation
• 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
Gene-Finding StrategiesGenomic Sequence
Bulk properties ofsequence:• Open reading frames• Codon usage• Repeat periodicity• Compositional
Absolute properties ofsequence:• Consensus sequences• Donor and acceptor
splice sites• Transcription factor
binding sites• Polyadenylation
signals• “Right” ATG start• Stop codons
Inferences basedon sequence homology:• Protein sequence
with similarity totranslated productof query
• Modular structure of proteins usually
precludes findingcomplete gene
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
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: the important ones
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”
• All protein modifications• Ontologies • Interactions (complexes, pathways, networks)•Expression (where and when, and how much)•Evolutionary relationships
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 .