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Nikolaj BlomCenter for Biological Sequence Analysis
BioCentrum-DTUTechnical University of Denmark
”Gene Finding in Eukaryotic Genomes”
PhD course #27803
Spring 2003
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Human Genome
Published
HUGO: Nature, 15.feb.2001
Celera: Science,
16.feb.2001
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seWe Have the Human Genome Sequence...now what?
So, what is the problem?• Well...• We don’t know how
many genes there are!• We don’t know where
they are!• We don’t know what
they do!
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The cellular machinery recognize genes without access to GenBank, SwissProt or computers – can we?
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seNeedles in Haystacks...
Only 2% of human genome is coding regionsIntron-exon structure of genes• Large introns (average 3365 bp )• Small exons (average 145 bp)• Long genes (average 27 kb)
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AAGAGGTAATTAAAGCTAAATGAAGTTGTAAGAGTGGCCCTATCGCATAGGACTAGTGTCCCTATAAGAACACGAAGAAATCACCTTAGAAAGGCTGAGAAAGGGCTGCAGGGCAGTGGGAGTGCAGACTGAAAGATGCAGACCACTGGGCTTCTACTTCTGTTTCCATTTCTGATCCGGCCTGCATCTGCCTCCTTCCTGAACAGGCCAGAGAATTCATCTAAATAGCCTAAGCAGGCTGGGTGCTGTGGCTCACCTGTAATCCCAACACTTGGGAGGCCGAGGTGGGCAGATCACCTGAGGTCAGGAGTTCAAGGCTAGCCTAGCCAACATGACAAAACCCCATCTCTACTAAAAAAATACAAAAATTAGCCAGGCATAGTGGCGCCTATAGTTCCAGCTACTTGGGGGCTGAGGTAGGAAGATCGCTAGAGCCTGGGAGGTTAAGGCTGCGGTGAGCTGTGATTGTGCCACTGCACTCCAGCCTGGGTGACAGAGCAAGACCCTGCCTCAAAAATAAATAAATAAATAAATAAATAAAAATAAGAGTGCTTGGCAGCTTGATCAAGCTATGCCAGGAACCCATCTCTCAAGCAGCAGCTCTTCTCCTGTGCCATTGTCAGCTTTGTCCTGTCTGAGTCCATGGGACTCTTCTGTTTGATGGTGGTCTTCCTCATCCTCTTCATCATGTGAAGCTCCATGGAGATCACCTACCCATACCTGCTTCTGTGACCTCATGCCATTCCTGGTGTTGGAATGTGCCAAGGTTTGCCATTAAACACACATTTCTCATTTCATAATTTCATATATATTATATATATGTGTGTGTGTGTGTGTTTATATATGCGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTATATATATATATATATATATATATATATATATATATATATAAAATATATAGGAAGAGGCACCAGAGAGCTCTCTGCATAGTCACAGAGGAAAGGTCATGTGAGGACAGCCAGAAGGCAGATGTCACAAGCCTCACCAGCAACCTACCATACCCTGCTTGTACCTCCATCCTGGAAGTCCAGCTTCTAAAATTAGAAGAAAATAGTCGGGTGTGGTGGCTCGCACCTATAATCCCAGCACTTTGGGAGGCTGATGTGGGAGGATCATTTGAGGTCAAGAGTTTGAAACCAGCCTAGGCAACATAGGGAGACCCTGTCTTTAAAAAAAATTTTTTTTTGTTTTAATTAGCTGGGTGTGATGGTGCACACCTGAGTCCTAGCTACTTGGGAGGCTGAGGTAGGAGGATCCCCTGAGCCCAGGGAAGTGGAGGCTGCAGTGAGCCATGATCACACCACTGCAATACAGCCTGGGTGACAGAGCAAGACCTTATCTCAAAATAAACAAACAAACAAAAAAGATGACAAAATAAATGTCTGTCGTTTAAGTCACCCATTCTGTGATATCTTGTTACGGCAGCCTGAACTGACCAATACACTTCCTCACCCAGTTTAAATTCCATGCTCAATCATAATCAGCCATTGCAATTACCCTCAACTGTATTATCAACCCTCAATTTGTATTAGTTGCTTGGCAAAACCCAAACCCTTGTGAAATCCAGTTCTTCTATATCTACATCGATGCTGCCGAATATGGCTGAAGAAAAGCAACTGTGTTGACTGGACTGCTTTAAATTCATGACCACTTACCTCAAGTGGGCACTTAACTTCCTGGCAATTATTCTACATTTTTCTAGTCCATTAACTCTCCTCCTCTCTGAGTTAATTATTTCACAGCTTTTCCTCCCTCTTTATACATGTTCCATCCTAACTCTCTGCTGATGACCTTGTTTCTTATTTCACTAATGGAGGCCACCAGGAGAGAACTCCCACAGCCATCAAATTCACCAAGCCAACAGCATCCTTACACAAATCCTCTGCCTTCTCTCTGGGCTGGCTGTGCCCTCTCTTTGCTCCTGCAATTTCCCTAACTCTCCTATACTGTTGTTATTCACTCTCCAGTGGATAATCACCATCAGGATGCAAAGATGCTGTACTAGCTTCTGAACTCTCCAAAAACCCAGGAAACAAAAAGGCAAAGGCTAAGCTTTTTCTTATTCCCCCTTCCAGCTATTGTACTGTTTCTCTGCTTTTAATTTATTTTTATTTATTTATTTATTTATTTATTTATTTATTTTTGAGATGGAGCTTCACTCTTGTTGCCCAGGCTGGAGCGCAATGGCGCGATCTCAGCTCACCGCAACCTCTACTTCCCGAATTCAAGTGATTGTCCTGCCTCAGCCTCCCGAGTAGCCGGGATTACAGGCATGCGCCACCACGCCTGGCTAATTTTGTACTTTTAGTAGAGACGGGGTTTCTCCATGTTGCTCAGCCTGGTCACAAACTCCCGATCTCAGGTGATCTGCCTGCCTCGGCCTCCCAAAGTGCTGGGATTACAGGCGTGAGCCACCACGCCCCACCGTCTCTGTTCTCTTTTAAAGCACAATCCCTCAACACAAGTGTCTATACTCAGCGTCTCCACTTTCCCTCCATCTGGTCTTCCCAGTGCCCCCTTGTCAGGTTTTCACCCCATGCTCCTCCAGGGCTAGTCTGCTCTTGCTTCCCGTCTTACTGGAAGACCAGCAGCATTTGACAGAGTTGGTCACTCTCTCCTCCTTGGACACCTTTTCTTCACTTGGTTTCCAGAACAGCATTATCTCCTGCTTATTGTCTTCCTCAGTCTACCTCAGTGAAAAGCTTTACTGGTTCCTCCACATCTCCCAGACCTCCAGTAATAACAGGAATGTACCATGCCATTGCTCTCTCTCTCTCCTTTTTTTTTTTTTTTTTTTTTTTTTGTTGAGACAGAGTCTCAATTTTATCACCCAGACTGAAGCACAATGGCATGATCATAGCTCATTGCAGTCTCGAACTCGTGGGCTCAAGCAATCCTCCCACCTCAGCCTCCTGAATAGCTGGGACTACAAGCAACACCACCATGCCCAGCTAACTTTCTATTTTTTATTTTTATTTTTTGTAGAGATGAGGTTTTACTATGTTGCCTAGGCTAGTCTTGAACTCCTGGGCCCAAATGATCCTCCCACCTTGGTCTCCCAAAGTGCTGGGATTATAGGCGTGAGCCACCGTGTCCAACTTCTCTTTCTTAATGGAATTTAGGCAAAAGTTATTACTCATGGCCTTGGAATGCTCTTTCCTCAGATAGCCACATGGCTCACCATTACTTCCTTCCAGCTTTCTTCAAAGATCCACTTCTCAGTGAAGCTTTGTCCTGACCACCCAGCTGAAAATTGCAATCCTCTTCTGTCTACCATGTACATACTCTCTATTTGCTTTCCTTCCTTTATTTCTCTCTGTAGGTGTGACCTAACATAACATATAATTTACTTCTGTACCTTGTTTGCTTTCTGTCTTCCCCTTTAGAACATAAGCTCCATGAGGGAAGGCGTTTTTGCCTGCTTTAGTCACTTTATCTCCAGCAACTACAACTATATGTATATATACACACACATATATATACACACACATATATATACACACACATATATATATACATATATATATATAGTAGGCACTCAATAAACATTCACTGAATGAATGAACAGTAATGCTCACTTGCCCATAAATACAAGTACCTCATCTTTTACCACAAAGGGTATTTGTAAATATTTAGGTTGTTTCTACCCAGATTATGGCTTGGTAATTCTTTTTTTTTTTTTCTAATTTTTATTTTTTTTCTAGGGACAGGGTCTCACTATGTTGCCCAGGATGGTCTTGAACTCCTGGGCTCAAGCATTCTGCCTGCCTTGGCCTCCTAAAGTGCTGAGATTACAGGCATGAGCCACCGTGCCTGCCTTCATGTATGTTTTTAGAACACAGAGAAAATGTGTTCTAAATGTGCTCATTGCTCAGCAATGAGCAAAGGCTTATGCAGTCACCACCAATCAAAAACTTTTTTTTTTTTTTTTGAGACAAGATCTTGCTCTGTTGCCCAGGCTGGAGTGCAGTGGCAGGATCATAGCAAGCTGCAGTCTTGACCTCATAGGCCTAAATCATCCTCCCACCTCAGCCTCACAAGTAGCTAAGACCACAGGTACAAGCCACCGTATCTAGCTAACTTTCAAAATTTTTTGAATTTTTAAATTTAAAAATTTTGAGGCCAGGCTGGCCTCAAACTCCTGAGCTCAAGCAATCCTCCCACCTTGGCTTCCCAAAGTGCTGGGATTATAGGCGTGAGCAACTGTACCTGGCAAAAACTTTTTAAGAGCTTCGCTTCCAGGATTAGGCAACTTTAACCTTCAACAGTGATCATAACCCTTAGTTTTCAGATCCGATTAAGGGAAATGTGTAATGTCTTACTGACACACTAATCCCATCACTGCTCACACCACCCACAATTAGCTGAG
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AAGAGGTAATTAAAGCTAAATGAAGTTGTAAGAGTGGCCCTATCGCATAGGACTAGTGTCCCTATAAGAACACGAAGAAATCACCTTAGAAAGGCTGAGAAAGGGCTGCAGGGCAGTGGGAGTGCAGACTGAAAGATGCAGACCACTGGGCTTCTACTTCTGTTTCCATTTCTGATCCGGCCTGCATCTGCCTCCTTCCTGAACAGGCCAGAGAATTCATCTAAATAGCCTAAGCAGGCTGGGTGCTGTGGCTCACCTGTAATCCCAACACTTGGGAGGCCGAGGTGGGCAGATCACCTGAGGTCAGGAGTTCAAGGCTAGCCTAGCCAACATGACAAAACCCCATCTCTACTAAAAAAATACAAAAATTAGCCAGGCATAGTGGCGCCTATAGTTCCAGCTACTTGGGGGCTGAGGTAGGAAGATCGCTAGAGCCTGGGAGGTTAAGGCTGCGGTGAGCTGTGATTGTGCCACTGCACTCCAGCCTGGGTGACAGAGCAAGACCCTGCCTCAAAAATAAATAAATAAATAAATAAATAAAAATAAGAGTGCTTGGCAGCTTGATCAAGCTATGCCAGGAACCCATCTCTCAAGCAGCAGCTCTTCTCCTGTGCCATTGTCAGCTTTGTCCTGTCTGAGTCCATGGGACTCTTCTGTTTGATGGTGGTCTTCCTCATCCTCTTCATCATGTGAAGCTCCATGGAGATCACCTACCCATACCTGCTTCTGTGACCTCATGCCATTCCTGGTGTTGGAATGTGCCAAGGTTTGCCATTAAACACACATTTCTCATTTCATAATTTCATATATATTATATATATGTGTGTGTGTGTGTGTTTATATATGCGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTATATATATATATATATATATATATATATATATATATATATAAAATATATAGGAAGAGGCACCAGAGAGCTCTCTGCATAGTCACAGAGGAAAGGTCATGTGAGGACAGCCAGAAGGCAGATGTCACAAGCCTCACCAGCAACCTACCATACCCTGCTTGTACCTCCATCCTGGAAGTCCAGCTTCTAAAATTAGAAGAAAATAGTCGGGTGTGGTGGCTCGCACCTATAATCCCAGCACTTTGGGAGGCTGATGTGGGAGGATCATTTGAGGTCAAGAGTTTGAAACCAGCCTAGGCAACATAGGGAGACCCTGTCTTTAAAAAAAATTTTTTTTTGTTTTAATTAGCTGGGTGTGATGGTGCACACCTGAGTCCTAGCTACTTGGGAGGCTGAGGTAGGAGGATCCCCTGAGCCCAGGGAAGTGGAGGCTGCAGTGAGCCATGATCACACCACTGCAATACAGCCTGGGTGACAGAGCAAGACCTTATCTCAAAATAAACAAACAAACAAAAAAGATGACAAAATAAATGTCTGTCGTTTAAGTCACCCATTCTGTGATATCTTGTTACGGCAGCCTGAACTGACCAATACACTTCCTCACCCAGTTTAAATTCCATGCTCAATCATAATCAGCCATTGCAATTACCCTCAACTGTATTATCAACCCTCAATTTGTATTAGTTGCTTGGCAAAACCCAAACCCTTGTGAAATCCAGTTCTTCTATATCTACATCGATGCTGCCGAATATGGCTGAAGAAAAGCAACTGTGTTGACTGGACTGCTTTAAATTCATGACCACTTACCTCAAGTGGGCACTTAACTTCCTGGCAATTATTCTACATTTTTCTAGTCCATTAACTCTCCTCCTCTCTGAGTTAATTATTTCACAGCTTTTCCTCCCTCTTTATACATGTTCCATCCTAACTCTCTGCTGATGACCTTGTTTCTTATTTCACTAATGGAGGCCACCAGGAGAGAACTCCCACAGCCATCAAATTCACCAAGCCAACAGCATCCTTACACAAATCCTCTGCCTTCTCTCTGGGCTGGCTGTGCCCTCTCTTTGCTCCTGCAATTTCCCTAACTCTCCTATACTGTTGTTATTCACTCTCCAGTGGATAATCACCATCAGGATGCAAAGATGCTGTACTAGCTTCTGAACTCTCCAAAAACCCAGGAAACAAAAAGGCAAAGGCTAAGCTTTTTCTTATTCCCCCTTCCAGCTATTGTACTGTTTCTCTGCTTTTAATTTATTTTTATTTATTTATTTATTTATTTATTTATTTATTTTTGAGATGGAGCTTCACTCTTGTTGCCCAGGCTGGAGCGCAATGGCGCGATCTCAGCTCACCGCAACCTCTACTTCCCGAATTCAAGTGATTGTCCTGCCTCAGCCTCCCGAGTAGCCGGGATTACAGGCATGCGCCACCACGCCTGGCTAATTTTGTACTTTTAGTAGAGACGGGGTTTCTCCATGTTGCTCAGCCTGGTCACAAACTCCCGATCTCAGGTGATCTGCCTGCCTCGGCCTCCCAAAGTGCTGGGATTACAGGCGTGAGCCACCACGCCCCACCGTCTCTGTTCTCTTTTAAAGCACAATCCCTCAACACAAGTGTCTATACTCAGCGTCTCCACTTTCCCTCCATCTGGTCTTCCCAGTGCCCCCTTGTCAGGTTTTCACCCCATGCTCCTCCAGGGCTAGTCTGCTCTTGCTTCCCGTCTTACTGGAAGACCAGCAGCATTTGACAGAGTTGGTCACTCTCTCCTCCTTGGACACCTTTTCTTCACTTGGTTTCCAGAACAGCATTATCTCCTGCTTATTGTCTTCCTCAGTCTACCTCAGTGAAAAGCTTTACTGGTTCCTCCACATCTCCCAGACCTCCAGTAATAACAGGAATGTACCATGCCATTGCTCTCTCTCTCTCCTTTTTTTTTTTTTTTTTTTTTTTTTGTTGAGACAGAGTCTCAATTTTATCACCCAGACTGAAGCACAATGGCATGATCATAGCTCATTGCAGTCTCGAACTCGTGGGCTCAAGCAATCCTCCCACCTCAGCCTCCTGAATAGCTGGGACTACAAGCAACACCACCATGCCCAGCTAACTTTCTATTTTTTATTTTTATTTTTTGTAGAGATGAGGTTTTACTATGTTGCCTAGGCTAGTCTTGAACTCCTGGGCCCAAATGATCCTCCCACCTTGGTCTCCCAAAGTGCTGGGATTATAGGCGTGAGCCACCGTGTCCAACTTCTCTTTCTTAATGGAATTTAGGCAAAAGTTATTACTCATGGCCTTGGAATGCTCTTTCCTCAGATAGCCACATGGCTCACCATTACTTCCTTCCAGCTTTCTTCAAAGATCCACTTCTCAGTGAAGCTTTGTCCTGACCACCCAGCTGAAAATTGCAATCCTCTTCTGTCTACCATGTACATACTCTCTATTTGCTTTCCTTCCTTTATTTCTCTCTGTAGGTGTGACCTAACATAACATATAATTTACTTCTGTACCTTGTTTGCTTTCTGTCTTCCCCTTTAGAACATAAGCTCCATGAGGGAAGGCGTTTTTGCCTGCTTTAGTCACTTTATCTCCAGCAACTACAACTATATGTATATATACACACACATATATATACACACACATATATATACACACACATATATATATACATATATATATATAGTAGGCACTCAATAAACATTCACTGAATGAATGAACAGTAATGCTCACTTGCCCATAAATACAAGTACCTCATCTTTTACCACAAAGGGTATTTGTAAATATTTAGGTTGTTTCTACCCAGATTATGGCTTGGTAATTCTTTTTTTTTTTTTCTAATTTTTATTTTTTTTCTAGGGACAGGGTCTCACTATGTTGCCCAGGATGGTCTTGAACTCCTGGGCTCAAGCATTCTGCCTGCCTTGGCCTCCTAAAGTGCTGAGATTACAGGCATGAGCCACCGTGCCTGCCTTCATGTATGTTTTTAGAACACAGAGAAAATGTGTTCTAAATGTGCTCATTGCTCAGCAATGAGCAAAGGCTTATGCAGTCACCACCAATCAAAAACTTTTTTTTTTTTTTTTGAGACAAGATCTTGCTCTGTTGCCCAGGCTGGAGTGCAGTGGCAGGATCATAGCAAGCTGCAGTCTTGACCTCATAGGCCTAAATCATCCTCCCACCTCAGCCTCACAAGTAGCTAAGACCACAGGTACAAGCCACCGTATCTAGCTAACTTTCAAAATTTTTTGAATTTTTAAATTTAAAAATTTTGAGGCCAGGCTGGCCTCAAACTCCTGAGCTCAAGCAATCCTCCCACCTTGGCTTCCCAAAGTGCTGGGATTATAGGCGTGAGCAACTGTACCTGGCAAAAACTTTTTAAGAGCTTCGCTTCCAGGATTAGGCAACTTTAACCTTCAACAGTGATCATAACCCTTAGTTTTCAGATCCGATTAAGGGAAATGTGTAATGTCTTACTGACACACTAATCCCATCACTGCTCACACCACCCACAATTAGCTGAG
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seGenes and Signals
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seGene Features
Codon frequency/bias• Organism dependent• Hexamer statistics
Transcriptional• Promoters/enhancers
Exon/introns• Length distributions• ORFs
Splicing• Donor/acceptor sites• Branchpoints
Translational• Ribosome binding sites
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seCodon Bias
Gene Finders are often organism specificCoding regions often modelled by 5th order Markov chain (hexamers/di-codons)
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seExon Size
0
5
10
15
20
25
30
35
1-100
100-200
200-300
300-500
>500
Fungi
Verterbrate
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seIntron Size
0
10
20
30
40
50
60
70
<100 <200 <1kbp
1 to5
>5
Fungi
Verterbrate
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seIntron Prevalence
0
10
20
3040
50
60
7080
90
100
0 1 >1
Yeast
Fungi
Mammal
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seGene Finding Challenges
Need the correct reading frame• Introns can interrupt an exon in mid-
codon
There is no hard and fast rule for identifying donor and acceptor splice sites• Signals are very weak
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seOverpredicting Genes
Easy to predict all exonsReport all sequences flanked by ..AG and GT.. as exonsSensitivity = 100%Specificity ~ 0%
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seSensor-based methods
Similarity searches misses some/many genescDNA/EST libraries are not perfect Ab initio Gene Finders• HMM-based
• GenScan• HMMgene
• Neural network-based• GRAIL• NetGene2 (splice sites)
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seGene Prediction
”Isolated” methods• Predict individual features
• E.g. splice sites, coding regions• NetGene (Neural network)
– http://www.cbs.dtu.dk/services/NetGene2/
”Integrated” methods• Predict genes in context
• ”Grammar” of genes• Certain elements in specific order are required
– HMMgene http://www.cbs.dtu.dk/services/HMMgene/
– GenScan (HMM-based) http://genes.mit.edu/GENSCAN.html
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seGene Grammar
HAPPYEUGENEAWASGUYFINDER
Isolated features
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seGene Grammar
HAPPYEUGENEAWASGUYFINDER
Isolated features
Intron 3’UTR Exon Promoter Exon RBS
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seGene Grammar
EUGENEFINDERWASAHAPPYGUY
Integrated features
HAPPYEUGENEAWASGUYFINDER
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seGene Grammar
EUGENEFINDERWASAHAPPYGUY
Integrated features
PromRBSExonIntronExon3’UTR
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seGene Grammar
”Isolated” methods (e.g.NN):
HAPPYEUGENEAWASGUYFINDER
”Integrated” methods (e.g.HMM):
EUGENEFINDERWASAHAPPYGUY
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seHMMs for genefinding
GenScan principle• E=exon• I=intron• F=5’ UTR• T=3’ UTR• P=promoter• N=intergenic
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seGenscan http://genes.mit.edu/GENSCAN.html
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seGenscan
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seGenscan http://genes.mit.edu/GENSCAN.html
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seGenscan
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seGenscan
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seHMMgene http://www.cbs.dtu.dk/services/HMMgene/
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seHMMgene http://www.cbs.dtu.dk/services/HMMgene/
Columns1.Sequence identifier 2.Program name 3.Prediction (see table below for the meaning). 4.Beginning 5.End 6.Score between 0 and 1 7.Strand: $+$ for direct and $-$ for complementary 8.Frame (for exons it is the position of the donor in the frame) 9.Group to which prediction belong. If several CDS's are found they will be called cds_1, cds_2, etc. `bestparse:' is there because alternative predictions will also be available (see below).
Name Meaning firstex The coding part of the first coding exon starting with the first base of the start codon. exon_N The N'th predicted internal coding exon. lastex The coding part of the last coding exon ending with the last base of the stop codon. singleex The coding part of an exon in a gene with only one coding exon. CDS Coding region composed of the exon predictions prior to this line.
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seDefining the term ’exon’
Gene Prediction programs often useExon = CDS (coding sequence)
Real exons may contain 5’ or 3’ UTRs (untranslated regions)
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seGene Prediction – NetGene 2
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seGene Prediction – NetGene 2
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seGene Prediction – NetGene 2
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seGene Prediction – NetGene 2
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seNIX – Visualizing Gene Predictions
http://www.hgmp.mrc.ac.uk/NIX/
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seGene Prediction – Performance of Genscan
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sePerformance of Genscan – Exon Length
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seRepeatmasker
Repetitive sequences in human/eukaryotic genomes are a problemRun gene predictions on large genomic regions before and after masking of repetitive sequence: • http://ftp.genome.washington.edu/cgi-bin/
RepeatMasker
Up to 45% of human genomic sequence derived from transposable/repetitive elements
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seRepeatmasker
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seFuture Challenges
Bootstrapping: prediction improves as more genes become known• ’Extreme’ genes (long/short) still difficult• Initial and terminal exons are predicted with lower
confidence
Combine with Sequence Similarity MatchesNon-coding RNAs• Most gene prediction programs only predict protein-
coding genes• tRNA and rRNA genes are not predicted
Prokaryotic gene finding• Much easier (no introns), but still not perfect• Especially short genes (<300 bp) difficult
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seGene Prediction
Take home messages• Human genome sequence is known• Number of human genes is unknown!
• Before 2001: est.30,000-140,000• Anno 2003: 30,000-40,000
• Location, structure and function of many human genes is unknown!
• Genes may be discovered by different means and methods
• ...
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seGene Prediction
Take home messages• Genes may be predicted by computer
programs• Masking of repetitive sequences may be
required for large genomic sequences• ’Unusual’ genes are difficult (high GC%,
short or terminal exons)• HMM-based gene prediction programs are
suitable for “Gene Grammar”
Prediction methods are not perfect!
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The End
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Gene Prediction Exercises
I. Gene Finding in Prokaryotic SequenceII. Gene Finding in Eukaryotic Sequence
Exercises at:
http://www.cbs.dtu.dk/phdcourse/programme.htmlhttp://www.cbs.dtu.dk/phdcourse/cookbooks/genefinding/pro.htmlhttp://www.cbs.dtu.dk/phdcourse/cookbooks/genefinding/euk.html
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seGene Prediction Exercise
Sequence GenBank Genscan HMMgene NetGene2
Seq#1 (HoxA10)
320..12262401..2675
320 1226 0.871 2401 2675 0.988
320 1226 0.744 2401 2675 0.971
Donor 1227 0.95HAcc. 2400 1.00H
Seq#2 (Dub-2)
398..4251208..2817
-1208 2817 0.800
398 425 0.418 1208 2817 0.735
Donor 426 0.87 Acc. 1207 0.42 Acc. 1210 0.71
http://www.cbs.dtu.dk/dtucourse/cookbooks/nikob/exercises/gf_exercise_solution.html
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seGene Prediction – Performance of Genscan
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seGenome Browsing - Exercise #1
How many exons are encoded by the hoxA10 gene?• 2 exons
How many basepairs is the transcript length ?• 2542 bp
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seGenome Browsing - Exercise #1
On what chromosome is the hoxA10 gene?• Human chr.7
On which arm (short/p or long/q) ?• p
What gene is located ca. 500 kb downstream of HoxA10 ?• Scap2
On what mouse chromosome is the ortholog/homolog of human HoxA10 located?• Mouse chr.6
In the overview panel, there is a gene located ca. 300 kb downstream of HoxA10, what is the name?• Scap2
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se
http://www.cbs.dtu.dk/dtucourse/cookbooks/nikob/exercises/gf_exercise_solution.html
