Title: Gene Prediction and Genome Annotation
1Gene PredictionandGenome Annotation
The Genome Access Course February, 2003
2How can we get from here
3to here,
4he r e,
5and he r e?
6Without
resorting
to this
7What is a gene?
- Stretch of DNA that contains the information for
the building of protein(s) - Dynamic concept, consider
- Prokaryotic vs. eukaryotic gene models
- Introns/exons
- Posttranscriptional modifications
- Alternative splicing
- Differential expression
- Genes-in-genes
- Genes-ad-genes
- Posttranslational modifications
- Multi-subunit proteins
8Prokaryotic gene model
- 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/CMRGen
omes.spl NCBI http//www.ncbi.nlm.nih.gov/PMGifs/
Genomes/micr.html
9Eukaryotic gene model
- Large genomes, low gene density
- Homo sapiens genome 25 genic
- Posttranscriptional modification
- 5-CAP, polyA tail, splicing
- Open reading frames
- One ORF per exon, dont all contain starts/stops
- Mature mRNA contains ORF par definitionem
- Multiple translates
- One gene many proteins via alternative splicing
- Posttranscriptional modification
- 5-CAP, polyA tail, splicing
- Open reading frames
- One ORF per exon, dont all contain starts/stops
- Mature mRNA contains ORF par definitionem
- Multiple translates
- One gene many proteins via alternative splicing
10Where do genes live?
- In genomes
- Example human genome
- Ca. 3,200,000,000 base pairs
- 25 chromosomes 1-22, X, Y, mt
- 28,000-45,000 genes (current estimate)
- 128 nucleotides (RNA gene) 2,800 kb (DMD)
- Ca. 25 of genome are genes (introns, exons)
- Ca. 1 of genome codes for amino acids (CDS)
- 30 kb gene length (average)
- 1.4 kb ORF length (average)
- 3 transcripts per gene (average)
11Sample genomes
 List of 68 eukaryotes, 141 bacteria, and 17
archaea at http//www.ncbi.nlm.nih.gov/PMGifs/Geno
mes/links2a.html
12The value of genome sequences lies in their
annotation
- Annotation Characterizing genomic features
using computational and experimental 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?
13Genomic sequence features I
- Repeats
- Transposable elements, simple repeats
- RepeatMasker
- Uses Smith Waterman algorithm to align sequences
to known repeats - Advantage avoid spurious matches to repetitive
elements - Disadvantage mask sequences that gene
prediction programs may need for statistics
14Genomic sequence features II
- Non-coding RNAs (ncRNA)
- tRNA tRNASCAN-SE
- Identifies candidates by scanning for pol III
promoters. Then it uses an algorithm to determine
the RNA. - rRNA, snRNA, miRNA, etc. COVE
- Identified by performing similarity searches and
RNA structure analysis using covariance models.
15Genomic sequence features III
- Genes
- Vary in density, length, structure, number of
introns, number of splice forms, etc. - Identification method depends on evidence,
expertise and methods available. - Gene identification usually requires concerted
application of bioinformatics methods and wet
experimentation. - Pseudo genes
- Look-a-likes of genes not transcribed. Obstruct
gene finding efforts.
16Gene identification
- Homology-based gene prediction
- Similarity Searches (e.g. BLAST, BLAT)
- Genome Browsers
- RNA evidence (ESTs)
- Ab initio gene prediction
- Gene prediction programs
- Prokaryotes
- ORF identification
- Eukaryotes
- Promoter prediction
- PolyA-signal prediction
- Splice site, start/stop-codon predictions
17Gene prediction through comparative genomics
- Purifying selection Conserved regions between
two genomes are useful or else they would have
diverged. - If genomes are too close in the phylogenetic
tree, there may be too much noise. - If genomes are too far apart, analogous regions
may be missed.
18Genome Browsers
NCBI Map Viewer www.ncbi.nlm.nih.gov/mapview/
Generic Genome Browser (CSHL) www.wormbase.org/db
/seq/gbrowse
Ensembl Genome Browser www.ensembl.org/
UCSC Genome Browser genome.ucsc.edu/cgi-bin/hgGate
way?orghuman
Apollo Genome Browser www.bdgp.org/annot/apollo/
19Gene discovery using ESTs
- Expressed Sequence Tags (ESTs) represent
sequences from expressed genes. - If region matches EST with high stringency then
region is probably a gene or pseudo gene. - EST overlapping exon boundary gives an accurate
prediction of exon boundary.
20Tools for EST analysis
- BLAST (Basic Local Alignment Search Tool)
- Smaller exons will be missed due to smaller
score. - SIM4 (http//pbil.univ-lyon1.fr/sim4.html)
- Useful tool to map a gene to genomic sequence
- Allows for large gaps (introns)
21Some limitations of ESTs
- Usually ESTs are not full length, posing
challenges to identifying complete gene. - Genes with low levels of expression or expression
limited to certain conditions may not be
represented in EST library. - Smaller exons will still be missed because match
is not significant enough. - Alternative splice forms may obstruct
identification of exon extents.
22Ab initio gene prediction
- Prokaryotes
- ORF-Detectors
- Eukaryotes
- Position, extent direction through promoter
and polyA-signal predictors - Structure through splice site predictors
- Exact location of coding sequences through
determination of relationships between potential
start codons, splice sites, ORFs, and stop codons
23Tools
- ORF detectors
- NCBI http//www.ncbi.nih.gov/gorf/gorf.html
- Promoter predictors
- CSHL http//rulai.cshl.org/software/index1.htm
- BDGP fruitfly.org/seq_tools/promoter.html
- ICG TATA-Box predictor
- PolyA signal predictors
- CSHL argon.cshl.org/tabaska/polyadq_form.html
- Splice site predictors
- BDGP http//www.fruitfly.org/seq_tools/splice.htm
l - Start-/stop-codon identifiers
- DNALC Translator/ORF-Finder
- BCM Searchlauncher
24How it works I Motif identification
- Exon-Intron Borders Splice Sites
Exon Intron
Exon gaggcatcaggtttgtagactgtgtttcag
tgcacccact ccgccgctgagtgagccgtgtc
tattctaggacgcgcggg tgtgaattaggtaagaggtt
atatctccagatggagatca ccatgaggaggtgagtg
ccattatttccaggtatgagacg
Splice site Splice site
Exon Intron
Exon gaggcatcagGTttgtagactgtgtttcAG
tgcacccact ccgccgctgaGTgagccgtgtc
tattctAGgacgcgcggg tgtgaattagGTaagaggtt
atatctccAGatggagatca ccatgaggagGTgagtg
ccattatttccAGgtatgagacg
Splice site Splice site
Motif Extraction Programs at http//www-btls.jst.g
o.jp/
25How it works II - Movies
Pribnow-Box Finder 0/1 Pribnow-Box Finder all
26How it works III The (ugly) truth
27Gene prediction programs
- Rule-based programs
- Use explicit set of rules to make decisions.
- Example GeneFinder
- Neural Network-based programs
- Use data set to build rules.
- Examples Grail, GrailEXP
- Hidden Markov Model-based programs
- Use probabilities of states and transitions
between these states to predict features. - Examples Genscan, GenomeScan
28Rule Based - GeneFinder
- Compares expected vs. observed frequencies to
score features such as codon bias via Log
Likelihood Ratios (LLR). - Each position of a sequence is scored in respect
to its potential of being a splice site or
translational start site. - LLR scores and ORF identification determine
maximum-length coding segments. - Total score for a gene is the sum of exon scores
minus the gap penalty. - Rather bad for first and last exons.
29Neural Networks - Grail, GrailEXP
- Utilizes sensors trained on a set of known genes
of the organism. - Sensors examine
- Frame Bias Matrix - Uses codon bias to determine
ORFs. - Fickett Pentamer position weight matrices.
- Dinucleotide Fractal Dimensions - Transition of
sequential dinucleotides is represented as
fractal dimension. CDS differ from nCDS. - GrailExp incorporates similarity-based method by
adding a blastn component to its prediction
algorithm. Runs reliably on unmasked sequences.
30HMM Genscan, Genomescan
- Genscan uses known transcriptional and
translational signals and then uses HMM to model
coding and non-coding regions. - Genomescan incorporates similarity-based method
by adding a blastX component to its prediction
algorithm, using the translated sequence to
search protein db.
31Burge, C. and S. Karlin, Prediction of complete
gene structures in human genomic DNA. J Mol Biol,
1997. 268(1) p. 78-94
32Evaluating prediction programs
- Sensitivity vs. Specificity
- Sensitivity
- How many genes were found out of all present?
- Sn TP/(TPFN)
- Specificity
- How many predicted genes are indeed genes?
- Sp TP/(TPFP)
- Programs that combine statistical evaluations
with similarity searches most powerful.
33Gene prediction accuracies
- Nucleotide level 95Sn, 90Sp (Lows less than
50) - Exon level 75Sn, 68Sp (Lows less than 30)
- Gene Level 40 Sn, 30Sp (Lows less than 10)
- Selected readings
- Parra et al. (2003). Comparative Gene Prediction
in Human and Mouse. Genome Research 13108-117. - Rogic et al. (2001). Evaluation of Gene-Finding
Programs in Mammalian Sequences. Genome Research
11817-832. - Guigo et al. (2000). An Assessment of Gene
Prediction Accuracy in Large DNA Sequences.
Genome Research 101631-1642. - Reese et al. (2000). Genome Annotation Assessment
in Drosophila melanogaster. Genome Research
10483-501. - Burge and Karlin (1997). Prediction of Complete
Gene Structures in Human Genomic DNA Tab. 1. JMB
26878-94.
34Common difficulties
- First and last exons difficult to annotate
because they contain UTRs. - Smaller genes are not statistically significant
so they are thrown out. - Algorithms are trained with sequences from known
genes which biases them against genes about which
nothing is known. - Masking repeats frequently removes chunks from
the untranslated regions of genes that contain
repetitive elements.
35The annotation pipeline
- Mask repeats using RepeatMasker.
- Run sequence through several programs.
- Take predicted genes and do similarity search
against ESTs and genes from other organisms. - Do similarity search for non-coding sequences to
find ncRNA.
36Annotation nomenclature
- Known Gene Predicted gene matches the entire
length of a known gene. - Putative Gene Predicted gene contains region
conserved with known gene. Also referred to as
like or similar to. - Unknown Gene Predicted gene matches a gene or
EST of which the function is not known. - Hypothetical Gene Predicted gene that does not
contain significant similarity to any known gene
or EST.
37CSHL Generic Genome Browser
http//www.wormbase.org/db/seq/gbrowse
38NCBI Map Viewer
www.ncbi.nlm.nih.gov/mapview/static/MVstart.html
39Ensembl Genome Browser
www.ensembl.org/
40UCSC Genome Browser
genome.ucsc.edu/cgi-bin/hgGateway?orghuman
41Apollo Genome Browser
http//www.bdgp.org/annot/apollo/