Center for Biologisk Sekvensanalyse Nikolaj Blom Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark

Slides:



Advertisements
Similar presentations
GS 540 week 5. What discussion topics would you like? Past topics: General programming tips C/C++ tips and standard library BLAST Frequentist vs. Bayesian.
Advertisements

An Introduction to Bioinformatics Finding genes in prokaryotes.
Genomics: READING genome sequences ASSEMBLY of the sequence ANNOTATION of the sequence carry out dideoxy sequencing connect seqs. to make whole chromosomes.
Ab initio gene prediction Genome 559, Winter 2011.
1 Computational Molecular Biology MPI for Molecular Genetics DNA sequence analysis Gene prediction Gene prediction methods Gene indices Mapping cDNA on.
Finding Eukaryotic Open reading frames.
Gene Prediction Methods G P S Raghava. Prokaryotic gene structure ORF (open reading frame) Start codon Stop codon TATA box ATGACAGATTACAGATTACAGATTACAGGATAG.
Gene prediction and HMM Computational Genomics 2005/6 Lecture 9b Slides taken from (and rapidly mixed) Larry Hunter, Tom Madej, William Stafford Noble,
1 Gene Finding Charles Yan. 2 Gene Finding Genomes of many organisms have been sequenced. We need to translate the raw sequences into knowledge. Where.
Gene Identification Lab
BME 130 – Genomes Lecture 7 Genome Annotation I – Gene finding & function predictions.
CSE182-L10 Gene Finding.
Comparative ab initio prediction of gene structures using pair HMMs
Center for Biologisk Sekvensanalyse Nikolaj Blom Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark
Eukaryotic Gene Finding
Lecture 12 Splicing and gene prediction in eukaryotes
CSE182-L10 MS Spec Applications + Gene Finding + Projects.
Eukaryotic Gene Finding
Gene Finding Genome Annotation. Gene finding is a cornerstone of genomic analysis Genome content and organization Differential expression analysis Epigenomics.
Biological Motivation Gene Finding in Eukaryotic Genomes
Genome Analysis & Gene Prediction. Overview about Genes Gene : whole nucleic acid sequence necessary for the synthesis of a functional protein (or functional.
Gene Structure and Identification
Chapter 6 Gene Prediction: Finding Genes in the Human Genome.
Fine Structure and Analysis of Eukaryotic Genes
International Livestock Research Institute, Nairobi, Kenya. Introduction to Bioinformatics: NOV David Lynn (M.Sc., Ph.D.) Trinity College Dublin.
Applications of HMMs Yves Moreau Overview Profile HMMs Estimation Database search Alignment Gene finding Elements of gene prediction Prokaryotes.
Genome Annotation BBSI July 14, 2005 Rita Shiang.
BME 110L / BIOL 181L Computational Biology Tools October 29: Quickly that demo: how to align a protein family (10/27)
BME 110L / BIOL 181L Computational Biology Tools February 19: In-class exercise: a phylogenetic tree for that.
Genome Analysis & Gene Prediction. Overview about Genes Gene : whole nucleic acid sequence necessary for the synthesis of a functional protein (or functional.
DNA sequencing. Dideoxy analogs of normal nucleotide triphosphates (ddNTP) cause premature termination of a growing chain of nucleotides. ACAGTCGATTG ACAddG.
Gene finding and gene structure prediction M. Fatih BÜYÜKAKÇALI Computational Bioinformatics 2012.
Genome Annotation Rosana O. Babu.
Gene Prediction: Similarity-Based Methods (Lecture for CS498-CXZ Algorithms in Bioinformatics) Sept. 15, 2005 ChengXiang Zhai Department of Computer Science.
Mark D. Adams Dept. of Genetics 9/10/04
From Genomes to Genes Rui Alves.
Eukaryotic Gene Prediction Rui Alves. How are eukaryotic genes different? DNA RNA Pol mRNA Ryb Protein.
Gene, Proteins, and Genetic Code. Protein Synthesis in a Cell.
Eukaryotic Gene Structure. 2 Terminology Genome – entire genetic material of an individual Transcriptome – set of transcribed sequences Proteome – set.
Genes and Genomes. Genome On Line Database (GOLD) 243 Published complete genomes 536 Prokaryotic ongoing genomes 434 Eukaryotic ongoing genomes December.
Research about Alternative Splicing recently 楊佳熒.
JIGSAW: a better way to combine predictions J.E. Allen, W.H. Majoros, M. Pertea, and S.L. Salzberg. JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the.
Applied Bioinformatics
Genome Annotation Assessment in Drosophila melanogaster by Reese, M. G., et al. Summary by: Joe Reardon Swathi Appachi Max Masnick Summary of.
(H)MMs in gene prediction and similarity searches.
Finding genes in the genome
Exercise 3 Inspecting the primary structure of a gene.
Annotation of eukaryotic genomes
BIOINFORMATICS Ayesha M. Khan Spring 2013 Lec-8.
Identification of Coding Sequences Bert Gold, Ph.D., F.A.C.M.G.
Gene prediction 10. June 2004 Irmtraud Meyer University of Oxford
Center for Biologisk Sekvensanalyse Nikolaj Blom Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark
Biological Motivation Gene Finding in Eukaryotic Genomes Rhys Price Jones Anne R. Haake.
1 Gene Finding. 2 “The Central Dogma” TranscriptionTranslation RNA Protein.
bacteria and eukaryotes
Genome Annotation (protein coding genes)
”Gene Finding in Eukaryotic Genomes”
Eukaryotic Gene Structure
Genes, Genomes, and Genomics
Eukaryotic Gene Finding
Ab initio gene prediction
Recitation 7 2/4/09 PSSMs+Gene finding
4. HMMs for gene finding HMM Ability to model grammar
Genome Annotation and the Human Genome
Modeling of Spliceosome
”Gene Finding in Eukaryotic Genomes”
Introduction to Alternative Splicing and my research report
Gene Structure.
The Toy Exon Finder.
Gene Structure.
Presentation transcript:

Center for Biologisk Sekvensanalyse Nikolaj Blom Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark ”Gene Finding in Eukaryotic Genomes” DTU course #27614

Center for Biologisk Sekvensanalyse Outline Gene finding in eukaryotic genomes Why look for genes? Genes as products Orphan genes What is the problem? Needles in haystacks Signal and background Gene finding by hand Gene features Strategies Ab initio gene prediction Gene prediction methods Isolated Integrated

Center for Biologisk Sekvensanalyse Gene Finding - Gene Hunting – Gene Discovery Why Look for Genes? Genes may: Explain Basic Biological Functions Protein kinases, Cyclins, etc. Explain Medical Conditions Cystic fibrosis gene Be Used for Treatment of Disease Contain commercial value As enzymes (Lipases, Amylases, ’washing detergent’) As drug targets (Ion channels, Receptors) As therapeutic factors

Center for Biologisk Sekvensanalyse Genes/Proteins(Biologics) as Pharmaceutical Products ’Blockbusters’ >1 billion US$ yearly Avonex Interferon-beta from Biogen inc. Multiple schlerosis EPOgen EPO (Erythropoetin) from Amgen inc. Anemia

Center for Biologisk Sekvensanalyse At Least 40% Orphan Proteins in the Human Genome Venter et al., Science, 2001 Uncharted territory Novel genes Novel opportunities Novel biological functions Novel biomarkers and therapeutic factors

Center for Biologisk Sekvensanalyse Human Genome Published HUGO: Nature, 15.feb.2001 Celera: Science, 16.feb.2001

Center for Biologisk Sekvensanalyse We Have the Human Genome Sequence...now what? Are there still novel genes to be discovered? Yes! What is the challenge? We don’t know how many genes there are! We don’t know where they (all) are! We don’t know what they (all) do!

Center for Biologisk Sekvensanalyse The cellular machinery recognize genes without access to GenBank, SwissProt or computers – can we?

Center for Biologisk Sekvensanalyse

Why is Gene Finding Difficult? Because genes are embedded in the genome sequence are needles hiding in genome haystacks... constitute only 2% of human genome (the coding regions) are often split, ie. have exon-intron structure Can we distinguish the gene features from the background?

Center for Biologisk Sekvensanalyse Can U spot Spot?

Center for Biologisk Sekvensanalyse TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTATGCTGAACAGGCCAGAGAATTCATCTAAATAGCCTAAGCAGGCTGGGTGC TGTGGCTCACCTGTAATCCCAACACTTGGGAGGCCGAGGTGGGCAGATCACCTGAGGTCAGGAGTTCAAGGCTAGCCTAGCCAACATGACAAAACCCCA TCTCTACTAAAAAAATACAAAAATTAGCCAGGCATAGTGGCGCCTATAGTTCCAGCTACTTGGGGGCTGAGGTAGGAAGATCGCTAGAGCCTGGGAGGT TAAGGCTGCGGTGAGCTGTGATTGTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGGACTCTTCTGTTTGATGGTGGTCTTCCTCATCCTCTTCA TCATGTGAAGCTCCATGGAGATCACCTACCCATACCTGCTTCTGTGACCTCATTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGCCA TTCCTGGTGTTGGAATGTGCCAAGGTTTGCCATTAAACACACATTTCTCATTTCATAATTTCATATATATTATATATATGTGTGTGTGTGTGTGTTTAT ATATGCGTGTGTGTTGTGTGTGTTATATATATAAAATATATAGGAAGAGGCACCAGAGAGCTCTCTGCATAGTCACAGAGGAAAGGTCATGTGAGGACA GCCAGAAGGCAGATGTCACAAGCCTCACCAGCAACCTACCATACCCTGCTTGTACCTCCATCCTGGAAGTCCAGCTTCTAAAATTAGAAGAAAATAGTC GGGTGTGTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGTGGCTCGCACCTATAATCCCAGCACTTTGGGAGGCTGATGTGGG AGGATCATTTGAGGTCAAGAGTTTGAAACCAGCCTAGGCAACATAGGGAGACCCTGTCTTTAAAAAAAATTTTTTTTTGTTTTAATTAGCTGGGTGTGA TGGTGCACACCTGAGTCCTAGCTACTTGGGAGGCTGAGGTAGGAGGATCCCCTGAGCCCAGGGAAGTGGAGGCTGCAGTGAGCCATGATCACACCTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTCCTTGTCAGGTTTTCACCCCATGCTCCTCCATTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGGGCTAGTCTGCTCTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGCTTCCCGTCTTACTGGAAGACCA GCAGCATTTGACAGAGTTGGTCACTCTCTCCTCCTTGGACACCTTTTCTTCACTTGGTTTCCAGAACAGCATTATCTCCTGCTTATTGTCTTCCTCAGT CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTT

Center for Biologisk Sekvensanalyse TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTATGCTGAACAGGCCAGAGAATTCATCTAAATAGCCTAAGCAGGCTGGGTGC TGTGGCTCACCTGTAATCCCAACACTTGGGAGGCCGAGGTGGGCAGATCACCTGAGGTCAGGAGTTCAAGGCTAGCCTAGCCAACATGACAAAACCCCA TCTCTACTAAAAAAATACAAAAATTAGCCAGGCATAGTGGCGCCTATAGTTCCAGCTACTTGGGGGCTGAGGTAGGAAGATCGCTAGAGCCTGGGAGGT TAAGGCTGCGGTGAGCTGTGATTGTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGGACTCTTCTGTTTGATGGTGGTCTTCCTCATCCTCTTCA TCATGTGAAGCTCCATGGAGATCACCTACCCATACCTGCTTCTGTGACCTCATTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGCCA TTCCTGGTGTTGGAATGTGCCAAGGTTTGCCATTAAACACACATTTCTCATTTCATAATTTCATATATATTATATATATGTGTGTGTGTGTGTGTTTAT ATATGCGTGTGTGTTGTGTGTGTTATATATATAAAATATATAGGAAGAGGCACCAGAGAGCTCTCTGCATAGTCACAGAGGAAAGGTCATGTGAGGACA GCCAGAAGGCAGATGTCACAAGCCTCACCAGCAACCTACCATACCCTGCTTGTACCTCCATCCTGGAAGTCCAGCTTCTAAAATTAGAAGAAAATAGTC GGGTGTGTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGTGGCTCGCACCTATAATCCCAGCACTTTGGGAGGCTGATGTGGG AGGATCATTTGAGGTCAAGAGTTTGAAACCAGCCTAGGCAACATAGGGAGACCCTGTCTTTAAAAAAAATTTTTTTTTGTTTTAATTAGCTGGGTGTGA TGGTGCACACCTGAGTCCTAGCTACTTGGGAGGCTGAGGTAGGAGGATCCCCTGAGCCCAGGGAAGTGGAGGCTGCAGTGAGCCATGATCACACCTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTCCTTGTCAGGTTTTCACCCCATGCTCCTCCATTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGGGCTAGTCTGCTCTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGCTTCCCGTCTTACTGGAAGACCA GCAGCATTTGACAGAGTTGGTCACTCTCTCCTCCTTGGACACCTTTTCTTCACTTGGTTTCCAGAACAGCATTATCTCCTGCTTATTGTCTTCCTCAGT CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT TTT

Center for Biologisk Sekvensanalyse

AAGAGGTAATTAAAGCTAAATGAAGTTGTAAGAGTGGCCCTATCGCATAGGACTAGTGTCCCTATAAGAACACGAAGAAATCACCTTAGAAAGGCTGAGAAA GGGCTGCAGGGCAGTGGGAGTGCAGACTGAAAGATGCAGACCACTGGGCTTCTACTTCTGTTTCCATTTCTGATCCGGCCTGCATCTGCCTCCTTCCTG AACAGGCCAGAGAATTCATCTAAATAGCCTAAGCAGGCTGGGTGCTGTGGCTCACCTGTAATCCCAACACTTGGGAGGCCGAGGTGGGCAGATCACCTG AGGTCAGGAGTTCAAGGCTAGCCTAGCCAACATGACAAAACCCCATCTCTACTAAAAAAATACAAAAATTAGCCAGGCATAGTGGCGCCTATAGTTCCA GCTACTTGGGGGCTGAGGTAGGAAGATCGCTAGAGCCTGGGAGGTTAAGGCTGCGGTGAGCTGTGATTGTGCCACTGCACTCCAGCCTGGGTGACAGAG CAAGACCCTGCCTCAAAAATAAATAAATAAATAAATAAATAAAAATAAGAGTGCTTGGCAGCTTGATCAAGCTATGCCAGGAACCCATCTCTCAAGCAG CAGCTCTTCTCCTGTGCCATTGTCAGCTTTGTCCTGTCTGAGTCCATGGGACTCTTCTGTTTGATGGTGGTCTTCCTCATCCTCTTCATCATGTGAAGC TCCATGGAGATCACCTACCCATACCTGCTTCTGTGACCTCATGCCATTCCTGGTGTTGGAATGTGCCAAGGTTTGCCATTAAACACACATTTCTCATTT CATAATTTCATATATATTATATATATGTGTGTGTGTGTGTGTTTATATATGCGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTATATATATATATATA TATATATATATATATATATATATATAAAATATATAGGAAGAGGCACCAGAGAGCTCTCTGCATAGTCACAGAGGAAAGGTCATGTGAGGACAGCCAGAA GGCAGATGTCACAAGCCTCACCAGCAACCTACCATACCCTGCTTGTACCTCCATCCTGGAAGTCCAGCTTCTAAAATTAGAAGAAAATAGTCGGGTGTG GTGGCTCGCACCTATAATCCCAGCACTTTGGGAGGCTGATGTGGGAGGATCATTTGAGGTCAAGAGTTTGAAACCAGCCTAGGCAACATAGGGAGACCC TGTCTTTAAAAAAAATTTTTTTTTGTTTTAATTAGCTGGGTGTGATGGTGCACACCTGAGTCCTAGCTACTTGGGAGGCTGAGGTAGGAGGATCCCCTG AGCCCAGGGAAGTGGAGGCTGCAGTGAGCCATGATCACACCACTGCAATACAGCCTGGGTGACAGAGCAAGACCTTATCTCAAAATAAACAAACAAACA AAAAAGATGACAAAATAAATGTCTGTCGTTTAAGTCACCCATTCTGTGATATCTTGTTACGGCAGCCTGAACTGACCAATACACTTCCTCACCCAGTTT AAATTCCATGCTCAATCATAATCAGCCATTGCAATTACCCTCAACTGTATTATCAACCCTCAATTTGTATTAGTTGCTTGGCAAAACCCAAACCCTTGT GAAATCCAGTTCTTCTATATCTACATCGATGCTGCCGAATATGGCTGAAGAAAAGCAACTGTGTTGACTGGACTGCTTTAAATTCATGACCACTTACCT CAAGTGGGCACTTAACTTCCTGGCAATTATTCTACATTTTTCTAGTCCATTAACTCTCCTCCTCTCTGAGTTAATTATTTCACAGCTTTTCCTCCCTCT TTATACATGTTCCATCCTAACTCTCTGCTGATGACCTTGTTTCTTATTTCACTAATGGAGGCCACCAGGAGAGAACTCCCACAGCCATCAAATTCACCA AGCCAACAGCATCCTTACACAAATCCTCTGCCTTCTCTCTGGGCTGGCTGTGCCCTCTCTTTGCTCCTGCAATTTCCCTAACTCTCCTATACTGTTGTT ATTCACTCTCCAGTGGATAATCACCATCAGGATGCAAAGATGCTGTACTAGCTTCTGAACTCTCCAAAAACCCAGGAAACAAAAAGGCAAAGGCTAAGC TTTTTCTTATTCCCCCTTATATACATATATATATATAGTAGGCACTCAATAAACATTCACTGAATGAATGAACAGTAATGCTCACTTGCCCATAAATAC AAGTACCTCATCTTTTACCACAAAGGGTATTTGTAAATATTTAGGTTGTTTCTACCCAGATTATGGCTTGGTAATTCTTTTTTTTTTTTTCTAATTTTT ATTTTTTTTCTAGGGACAGGGTCTCACTATGTTGCCCAGGATGGTCTTGAACTCCTGGGCTCAAGCATTCTGCCTGCCTTGGCCTCCTAAAGTGCTGAG ATTACAGGCATGAGCCACCGTGCCTGCCTTCATGTATGTTTTTAGAACACAGAGAAAATGTGTTCTAAATGTGCTCATTGCTCAGCAATGAGCAAAGGC TTATGCAGTCACCACCAATCAAAAACTTTTTTTTTTTTTTTTGAGACAAGATCTTGCTCTGTTGCCCAGGCTGGAGTGCAGTGGCAGGATCATAGCAAG CTGCAGTCTTGACCTCATAGGCCTAAATCATCCTCCCACCTCAGCCTCACAAGTAGCTAAGACCACAGGTACAAGCCACCGTATCTAGCTAACTTTCAA AATTTTTTGAATTTTTAAATTTAAAAATTTTGAGGCCAGGCTGGCCTCAAACTCCTGAGCTCAAGCAATCCTCCCACCTTGGCTTCCCAAAGTGCTGGG ATTATAGGCGTGAGCAACTGTACCTGGCAAAAACTTTTTAAGAGCTTCGCTTCCAGATTTAGGTTGTTTCTACCCAGATTATGGCTTGGTAATTCTTTT TTTTTTTTTCTAATTTTTATTTTTTTTCTAGGGACAGGGTCTCACTATGTTGCCCAGGATGGTCTTGAACTCCTGGGCTCAAGCATTCTGCCTGCCTTG GCCTCCTAAAGTGCTGAGATTACAGGCATGAGCCACCGTGCCTGCCTTCATGTATGTTTTTAGAACACAGAGAAAATGTGTTCTAAATGTGCTCATTGC TCAGCAATGAGCAAAGGCTTATGCAGTCACCACCAATCAAAAACTTTTTTTTTTTTTTTTGAGACAAGATCTTGCTCTGTTGCCCAGGCTGGAGTGCAG TGGCAGGATCATAGCAAGCTGCAGTCTTGACCTCATAGGCCTAAATCATCCTCCCACCTCAGCCTCACAAGTAGCTAAGACCACAGGTACAAGCCACCG TATCTAGCTAACTTTCAAAATTTTTTGAATTTTTAAATTTAAAAATTTTGAGGCCAGGCTGGCCTCAGATTAGGCAACTTTAACCTTCAACAGTGATCA TAACCCTTAGTTTTCAGATCCGATTAAGGGAAATGTGTAATGTCTTACTGACACACTAATCCCATCACTGCTCACACCACCCACAATTAGCTGAG Can U spot the Gin? Can U spot the Gene? Ooops

Center for Biologisk Sekvensanalyse AAGAGGTAATTAAAGCTAAATGAAGTTGTAAGAGTGGCCCTATCGCATAGGACTAGTGTCCCTATAAGAACACGAAGAAATCACCTTAGAAAGGCTGAGAAA GGGCTGCAGGGCAGTGGGAGTGCAGACTGAAAGATGCAGACCACTGGGCTTCTACTTCTGTTTCCATTTCTGATCCGGCCTGCATCTGCCTCCTTCCTG AACAGGCCAGAGAATTCATCTAAATAGCCTAAGCAGGCTGGGTGCTGTGGCTCACCTGTAATCCCAACACTTGGGAGGCCGAGGTGGGCAGATCACCTG AGGTCAGGAGTTCAAGGCTAGCCTAGCCAACATGACAAAACCCCATCTCTACTAAAAAAATACAAAAATTAGCCAGGCATAGTGGCGCCTATAGTTCCA GCTACTTGGGGGCTGAGGTAGGAAGATCGCTAGAGCCTGGGAGGTTAAGGCTGCGGTGAGCTGTGATTGTGCCACTGCACTCCAGCCTGGGTGACAGAG CAAGACCCTGCCTCAAAAATAAATAAATAAATAAATAAATAAAAATAAGAGTGCTTGGCAGCTTGATCAAGCTATGCCAGGAACCCATCTCTCAAGCAG CAGCTCTTCTCCTGTGCCATTGTCAGCTTTGTCCTGTCTGAGTCCATGGGACTCTTCTGTTTGATGGTGGTCTTCCTCATCCTCTTCATCATGTGAAGC TCCATGGAGATCACCTACCCATACCTGCTTCTGTGACCTCATGCCATTCCTGGTGTTGGAATGTGCCAAGGTTTGCCATTAAACACACATTTCTCATTT CATAATTTCATATATATTATATATATGTGTGTGTGTGTGTGTTTATATATGCGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTATATATATATATATA TATATATATATATATATATATATATAAAATATATAGGAAGAGGCACCAGAGAGCTCTCTGCATAGTCACAGAGGAAAGGTCATGTGAGGACAGCCAGAA GGCAGATGTCACAAGCCTCACCAGCAACCTACCATACCCTGCTTGTACCTCCATCCTGGAAGTCCAGCTTCTAAAATTAGAAGAAAATAGTCGGGTGTG GTGGCTCGCACCTATAATCCCAGCACTTTGGGAGGCTGATGTGGGAGGATCATTTGAGGTCAAGAGTTTGAAACCAGCCTAGGCAACATAGGGAGACCC TGTCTTTAAAAAAAATTTTTTTTTGTTTTAATTAGCTGGGTGTGATGGTGCACACCTGAGTCCTAGCTACTTGGGAGGCTGAGGTAGGAGGATCCCCTG AGCCCAGGGAAGTGGAGGCTGCAGTGAGCCATGATCACACCACTGCAATACAGCCTGGGTGACAGAGCAAGACCTTATCTCAAAATAAACAAACAAACA AAAAAGATGACAAAATAAATGTCTGTCGTTTAAGTCACCCATTCTGTGATATCTTGTTACGGCAGCCTGAACTGACCAATACACTTCCTCACCCAGTTT AAATTCCATGCTCAATCATAATCAGCCATTGCAATTACCCTCAACTGTATTATCAACCCTCAATTTGTATTAGTTGCTTGGCAAAACCCAAACCCTTGT GAAATCCAGTTCTTCTATATCTACATCGATGCTGCCGAATATGGCTGAAGAAAAGCAACTGTGTTGACTGGACTGCTTTAAATTCATGACCACTTACCT CAAGTGGGCACTTAACTTCCTGGCAATTATTCTACATTTTTCTAGTCCATTAACTCTCCTCCTCTCTGAGTTAATTATTTCACAGCTTTTCCTCCCTCT TTATACATGTTCCATCCTAACTCTCTGCTGATGACCTTGTTTCTTATTTCACTAATGGAGGCCACCAGGAGAGAACTCCCACAGCCATCAAATTCACCA AGCCAACAGCATCCTTACACAAATCCTCTGCCTTCTCTCTGGGCTGGCTGTGCCCTCTCTTTGCTCCTGCAATTTCCCTAACTCTCCTATACTGTTGTT ATTCACTCTCCAGTGGATAATCACCATCAGGATGCAAAGATGCTGTACTAGCTTCTGAACTCTCCAAAAACCCAGGAAACAAAAAGGCAAAGGCTAAGC TTTTTCTTATTCCCCCTTATATACATATATATATATAGTAGGCACTCAATAAACATTCACTGAATGAATGAACAGTAATGCTCACTTGCCCATAAATAC AAGTACCTCATCTTTTACCACAAAGGGTATTTGTAAATATTTAGGTTGTTTCTACCCAGATTATGGCTTGGTAATTCTTTTTTTTTTTTTCTAATTTTT ATTTTTTTTCTAGGGACAGGGTCTCACTATGTTGCCCAGGATGGTCTTGAACTCCTGGGCTCAAGCATTCTGCCTGCCTTGGCCTCCTAAAGTGCTGAG ATTACAGGCATGAGCCACCGTGCCTGCCTTCATGTATGTTTTTAGAACACAGAGAAAATGTGTTCTAAATGTGCTCATTGCTCAGCAATGAGCAAAGGC TTATGCAGTCACCACCAATCAAAAACTTTTTTTTTTTTTTTTGAGACAAGATCTTGCTCTGTTGCCCAGGCTGGAGTGCAGTGGCAGGATCATAGCAAG CTGCAGTCTTGACCTCATAGGCCTAAATCATCCTCCCACCTCAGCCTCACAAGTAGCTAAGACCACAGGTACAAGCCACCGTATCTAGCTAACTTTCAA AATTTTTTGAATTTTTAAATTTAAAAATTTTGAGGCCAGGCTGGCCTCAAACTCCTGAGCTCAAGCAATCCTCCCACCTTGGCTTCCCAAAGTGCTGGG ATTATAGGCGTGAGCAACTGTACCTGGCAAAAACTTTTTAAGAGCTTCGCTTCCAGATTTAGGTTGTTTCTACCCAGATTATGGCTTGGTAATTCTTTT TTTTTTTTTCTAATTTTTATTTTTTTTCTAGGGACAGGGTCTCACTATGTTGCCCAGGATGGTCTTGAACTCCTGGGCTCAAGCATTCTGCCTGCCTTG GCCTCCTAAAGTGCTGAGATTACAGGCATGAGCCACCGTGCCTGCCTTCATGTATGTTTTTAGAACACAGAGAAAATGTGTTCTAAATGTGCTCATTGC TCAGCAATGAGCAAAGGCTTATGCAGTCACCACCAATCAAAAACTTTTTTTTTTTTTTTTGAGACAAGATCTTGCTCTGTTGCCCAGGCTGGAGTGCAG TGGCAGGATCATAGCAAGCTGCAGTCTTGACCTCATAGGCCTAAATCATCCTCCCACCTCAGCCTCACAAGTAGCTAAGACCACAGGTACAAGCCACCG TATCTAGCTAACTTTCAAAATTTTTTGAATTTTTAAATTTAAAAATTTTGAGGCCAGGCTGGCCTCAGATTAGGCAACTTTAACCTTCAACAGTGATCA TAACCCTTAGTTTTCAGATCCGATTAAGGGAAATGTGTAATGTCTTACTGACACACTAATCCCATCACTGCTCACACCACCCACAATTAGCTGAG

Center for Biologisk Sekvensanalyse Manual Genefinding Start codon:ATG Stop codons:TAA, TAG, TGA Donor splice site: ^GT[AG]AG Acceptor splice site: [CT]AG^ >U70368 (950 bp) 1 CTCCCTTAGA AGACTCCAGC AAGTTATTTG AAGAGGTCTT TGGAGACATG 51 GTGAGTTCTC TTTCCTTCCC AGAAGGTAAG TCTCACTGTA AGGTCTTTAT 101 GTCTTGTGTG TCCCCCAGCA GCCTTGTCAT CTCCGGCTGC CCTAGACCTG 151 CATAAGGACA GATTGAGTGT GCTGGGATAG ACTTTTGTTG ACAAAGGGGC 201 TGCTCTGCCC TTCTAAGAGG TTGAGTCTCA TCATAAGGCC TTTTGCAGCT 251 TGCATGTGTA GTGCCAGGAA AGAGTAGTCA TCCCCCAAAA CCAGACAGGA 301 ACTGACGAGA TGCAATCACT GTGTGGACTT TTTACCAGCT AGCTAGGGCA 351 CTACCATGAG CCACTGTCTA GCAGGGAGGC TTTGGGGATG GTGTGCCCCG 401 AATATCTCTC AGGGTAAGAG TTTACAGTAA GCAGCAAGCA GAGGGGTGTG 451 GGTGAGTGTG CAAGTATCTA ATTGGCTAGT TTTTGTGGCC TGTAACATAT 501 TGGTGGGTGT TGGGAGTCAT AAGCTAAATG TTTGCTTTCC TCTGCATTGG 551 TGGTCATTAG GGAGGGGGCA GATTATGAAC CTAGGTTGCA GATCTGTTGG 601 AGTAATAACA AGACACTGGT CTTGTTGGGG GTATAACCTA GAGACTCGAT 651 TTATGTTCAT GTTTGGTTTG GGATGGGTTT TATGTGAGTG TTTTCTTTTT 701 TGGGGAGGGG GTCGGTTAAC TTGGAAAGTA ATGCTAGGTA CTGTCCTGTT 751 CATTTCCCTG AGGTGAAAGT TAGGTCAGGT TTTCTAGAAT GGAGTCTGAA 801 GGTAAAACAT TTGGCCACTG GCATGCCCTA AAGTCTTTTT GTGTTCTTGT 851 CCCCTAGCAG ATCCAGCCCT ATCATCTCCT GGTGCCCAAC AGCTGCATCA 901 GGATGAAGCT CAGGTAGTGG TGGAGCTAAC TGCCAATGAC AAGCCCAGTC Find, mark and count all ATG How many ATGs do you expect?

Center for Biologisk Sekvensanalyse Manual Genefinding Start codon:ATG p(ATG)=p(A) x p(T) x p(G) ~ ¼ x ¼ x ¼ = 1/64 (in 950 bp = 14.8 ATG expected)

Center for Biologisk Sekvensanalyse Manual Genefinding Start codon:ATG p(ATG)=p(A) x p(T) x p(G) ~ ¼ x ¼ x ¼ = 1/64 (in 950 bp = 14.8 ATG expected; observed = 16) >U70368 (950 bp) 1 CTCCCTTAGA AGACTCCAGC AAGTTATTTG AAGAGGTCTT TGGAGACATG 51 GTGAGTTCTC TTTCCTTCCC AGAAGGTAAG TCTCACTGTA AGGTCTTTAT 101 GTCTTGTGTG TCCCCCAGCA GCCTTGTCAT CTCCGGCTGC CCTAGACCTG 151 CATAAGGACA GATTGAGTGT GCTGGGATAG ACTTTTGTTG ACAAAGGGGC 201 TGCTCTGCCC TTCTAAGAGG TTGAGTCTCA TCATAAGGCC TTTTGCAGCT 251 TGCATGTGTA GTGCCAGGAA AGAGTAGTCA TCCCCCAAAA CCAGACAGGA 301 ACTGACGAGA TGCAATCACT GTGTGGACTT TTTACCAGCT AGCTAGGGCA 351 CTACCATGAG CCACTGTCTA GCAGGGAGGC TTTGGGGATG GTGTGCCCCG 401 AATATCTCTC AGGGTAAGAG TTTACAGTAA GCAGCAAGCA GAGGGGTGTG 451 GGTGAGTGTG CAAGTATCTA ATTGGCTAGT TTTTGTGGCC TGTAACATAT 501 TGGTGGGTGT TGGGAGTCAT AAGCTAAATG TTTGCTTTCC TCTGCATTGG 551 TGGTCATTAG GGAGGGGGCA GATTATGAAC CTAGGTTGCA GATCTGTTGG 601 AGTAATAACA AGACACTGGT CTTGTTGGGG GTATAACCTA GAGACTCGAT 651 TTATGTTCAT GTTTGGTTTG GGATGGGTTT TATGTGAGTG TTTTCTTTTT 701 TGGGGAGGGG GTCGGTTAAC TTGGAAAGTA ATGCTAGGTA CTGTCCTGTT 751 CATTTCCCTG AGGTGAAAGT TAGGTCAGGT TTTCTAGAAT GGAGTCTGAA 801 GGTAAAACAT TTGGCCACTG GCATGCCCTA AAGTCTTTTT GTGTTCTTGT 851 CCCCTAGCAG ATCCAGCCCT ATCATCTCCT GGTGCCCAAC AGCTGCATCA 901 GGATGAAGCT CAGGTAGTGG TGGAGCTAAC TGCCAATGAC AAGCCCAGTC

Center for Biologisk Sekvensanalyse Manual Genefinding Start codon:ATG p(ATG)=p(A) x p(T) x p(G) ~ ¼ x ¼ x ¼ = 1/64 (in 950 bp = 14.8 ATG expected; observed = 16 17) >U70368 (950 bp) 1 CTCCCTTAGA AGACTCCAGC AAGTTATTTG AAGAGGTCTT TGGAGACATG 51 GTGAGTTCTC TTTCCTTCCC AGAAGGTAAG TCTCACTGTA AGGTCTTTAT 101 GTCTTGTGTG TCCCCCAGCA GCCTTGTCAT CTCCGGCTGC CCTAGACCTG 151 CATAAGGACA GATTGAGTGT GCTGGGATAG ACTTTTGTTG ACAAAGGGGC 201 TGCTCTGCCC TTCTAAGAGG TTGAGTCTCA TCATAAGGCC TTTTGCAGCT 251 TGCATGTGTA GTGCCAGGAA AGAGTAGTCA TCCCCCAAAA CCAGACAGGA 301 ACTGACGAGA TGCAATCACT GTGTGGACTT TTTACCAGCT AGCTAGGGCA 351 CTACCATGAG CCACTGTCTA GCAGGGAGGC TTTGGGGATG GTGTGCCCCG 401 AATATCTCTC AGGGTAAGAG TTTACAGTAA GCAGCAAGCA GAGGGGTGTG 451 GGTGAGTGTG CAAGTATCTA ATTGGCTAGT TTTTGTGGCC TGTAACATAT 501 TGGTGGGTGT TGGGAGTCAT AAGCTAAATG TTTGCTTTCC TCTGCATTGG 551 TGGTCATTAG GGAGGGGGCA GATTATGAAC CTAGGTTGCA GATCTGTTGG 601 AGTAATAACA AGACACTGGT CTTGTTGGGG GTATAACCTA GAGACTCGAT 651 TTATGTTCAT GTTTGGTTTG GGATGGGTTT TATGTGAGTG TTTTCTTTTT 701 TGGGGAGGGG GTCGGTTAAC TTGGAAAGTA ATGCTAGGTA CTGTCCTGTT 751 CATTTCCCTG AGGTGAAAGT TAGGTCAGGT TTTCTAGAAT GGAGTCTGAA 801 GGTAAAACAT TTGGCCACTG GCATGCCCTA AAGTCTTTTT GTGTTCTTGT 851 CCCCTAGCAG ATCCAGCCCT ATCATCTCCT GGTGCCCAAC AGCTGCATCA 901 GGATGAAGCT CAGGTAGTGG TGGAGCTAAC TGCCAATGAC AAGCCCAGTC

Center for Biologisk Sekvensanalyse Manual Genefinding Start codon:ATG Stop codons:TAA, TAG, TGA >U70368 (950 bp) 1 CTCCCTTAGA AGACTCCAGC AAGTTATTTG AAGAGGTCTT TGGAGACATG 51 GTGAGTTCTC TTTCCTTCCC AGAAGGTAAG TCTCACTGTA AGGTCTTTAT 101 GTCTTGTGTG TCCCCCAGCA GCCTTGTCAT CTCCGGCTGC CCTAGACCTG 151 CATAAGGACA GATTGAGTGT GCTGGGATAG ACTTTTGTTG ACAAAGGGGC 201 TGCTCTGCCC TTCTAAGAGG TTGAGTCTCA TCATAAGGCC TTTTGCAGCT 251 TGCATGTGTA GTGCCAGGAA AGAGTAGTCA TCCCCCAAAA CCAGACAGGA 301 ACTGACGAGA TGCAATCACT GTGTGGACTT TTTACCAGCT AGCTAGGGCA 351 CTACCATGAG CCACTGTCTA GCAGGGAGGC TTTGGGGATG GTGTGCCCCG 401 AATATCTCTC AGGGTAAGAG TTTACAGTAA GCAGCAAGCA GAGGGGTGTG 451 GGTGAGTGTG CAAGTATCTA ATTGGCTAGT TTTTGTGGCC TGTAACATAT 501 TGGTGGGTGT TGGGAGTCAT AAGCTAAATG TTTGCTTTCC TCTGCATTGG 551 TGGTCATTAG GGAGGGGGCA GATTATGAAC CTAGGTTGCA GATCTGTTGG 601 AGTAATAACA AGACACTGGT CTTGTTGGGG GTATAACCTA GAGACTCGAT 651 TTATGTTCAT GTTTGGTTTG GGATGGGTTT TATGTGAGTG TTTTCTTTTT 701 TGGGGAGGGG GTCGGTTAAC TTGGAAAGTA ATGCTAGGTA CTGTCCTGTT 751 CATTTCCCTG AGGTGAAAGT TAGGTCAGGT TTTCTAGAAT GGAGTCTGAA 801 GGTAAAACAT TTGGCCACTG GCATGCCCTA AAGTCTTTTT GTGTTCTTGT 851 CCCCTAGCAG ATCCAGCCCT ATCATCTCCT GGTGCCCAAC AGCTGCATCA 901 GGATGAAGCT CAGGTAGTGG TGGAGCTAAC TGCCAATGAC AAGCCCAGTC Mark codons until first in- frame Stop codon

Center for Biologisk Sekvensanalyse Manual Genefinding Start codon:ATG Stop codons:TAA, TAG, TGA >U70368 (950 bp) 1 CTCCCTTAGA AGACTCCAGC AAGTTATTTG AAGAGGTCTT TGGAGACATG 51 GTGAGTTCTC TTTCCTTCCC AGAAGGTAAG TCTCACTGTA AGGTCTTTAT 101 GTCTTGTGTG TCCCCCAGCA GCCTTGTCAT CTCCGGCTGC CCTAGACCTG 151 CATAAGGACA GATTGAGTGT GCTGGGATAG ACTTTTGTTG ACAAAGGGGC 201 TGCTCTGCCC TTCTAAGAGG TTGAGTCTCA TCATAAGGCC TTTTGCAGCT 251 TGCATGTGTA GTGCCAGGAA AGAGTAGTCA TCCCCCAAAA CCAGACAGGA 301 ACTGACGAGA TGCAATCACT GTGTGGACTT TTTACCAGCT AGCTAGGGCA 351 CTACCATGAG CCACTGTCTA GCAGGGAGGC TTTGGGGATG GTGTGCCCCG 401 AATATCTCTC AGGGTAAGAG TTTACAGTAA GCAGCAAGCA GAGGGGTGTG 451 GGTGAGTGTG CAAGTATCTA ATTGGCTAGT TTTTGTGGCC TGTAACATAT 501 TGGTGGGTGT TGGGAGTCAT AAGCTAAATG TTTGCTTTCC TCTGCATTGG 551 TGGTCATTAG GGAGGGGGCA GATTATGAAC CTAGGTTGCA GATCTGTTGG 601 AGTAATAACA AGACACTGGT CTTGTTGGGG GTATAACCTA GAGACTCGAT 651 TTATGTTCAT GTTTGGTTTG GGATGGGTTT TATGTGAGTG TTTTCTTTTT 701 TGGGGAGGGG GTCGGTTAAC TTGGAAAGTA ATGCTAGGTA CTGTCCTGTT 751 CATTTCCCTG AGGTGAAAGT TAGGTCAGGT TTTCTAGAAT GGAGTCTGAA 801 GGTAAAACAT TTGGCCACTG GCATGCCCTA AAGTCTTTTT GTGTTCTTGT 851 CCCCTAGCAG ATCCAGCCCT ATCATCTCCT GGTGCCCAAC AGCTGCATCA 901 GGATGAAGCT CAGGTAGTGG TGGAGCTAAC TGCCAATGAC AAGCCCAGTC Mark codons until first in- frame Stop codon

Center for Biologisk Sekvensanalyse Genes and Signals

Center for Biologisk Sekvensanalyse

Manual Genefinding Start codon:ATG Stop codons:TAA, TAG, TGA Donor splice site: ^GT[AG]AG Acceptor splice site: [CT]AG^ >U70368 (950 bp) 1 CTCCCTTAGA AGACTCCAGC AAGTTATTTG AAGAGGTCTT TGGAGACATG 51 GTGAGTTCTC TTTCCTTCCC AGAAGGTAAG TCTCACTGTA AGGTCTTTAT 101 GTCTTGTGTG TCCCCCAGCA GCCTTGTCAT CTCCGGCTGC CCTAGACCTG 151 CATAAGGACA GATTGAGTGT GCTGGGATAG ACTTTTGTTG ACAAAGGGGC 201 TGCTCTGCCC TTCTAAGAGG TTGAGTCTCA TCATAAGGCC TTTTGCAGCT 251 TGCATGTGTA GTGCCAGGAA AGAGTAGTCA TCCCCCAAAA CCAGACAGGA 301 ACTGACGAGA TGCAATCACT GTGTGGACTT TTTACCAGCT AGCTAGGGCA 351 CTACCATGAG CCACTGTCTA GCAGGGAGGC TTTGGGGATG GTGTGCCCCG 401 AATATCTCTC AGGGTAAGAG TTTACAGTAA GCAGCAAGCA GAGGGGTGTG 451 GGTGAGTGTG CAAGTATCTA ATTGGCTAGT TTTTGTGGCC TGTAACATAT 501 TGGTGGGTGT TGGGAGTCAT AAGCTAAATG TTTGCTTTCC TCTGCATTGG 551 TGGTCATTAG GGAGGGGGCA GATTATGAAC CTAGGTTGCA GATCTGTTGG 601 AGTAATAACA AGACACTGGT CTTGTTGGGG GTATAACCTA GAGACTCGAT 651 TTATGTTCAT GTTTGGTTTG GGATGGGTTT TATGTGAGTG TTTTCTTTTT 701 TGGGGAGGGG GTCGGTTAAC TTGGAAAGTA ATGCTAGGTA CTGTCCTGTT 751 CATTTCCCTG AGGTGAAAGT TAGGTCAGGT TTTCTAGAAT GGAGTCTGAA 801 GGTAAAACAT TTGGCCACTG GCATGCCCTA AAGTCTTTTT GTGTTCTTGT 851 CCCCTAGCAG ATCCAGCCCT ATCATCTCCT GGTGCCCAAC AGCTGCATCA 901 GGATGAAGCT CAGGTAGTGG TGGAGCTAAC TGCCAATGAC AAGCCCAGTC Find and mark potential donor splice sites in first exon First exon Second exon (Coding exons!)

Center for Biologisk Sekvensanalyse Manual Genefinding Start codon:ATG Stop codons:TAA, TAG, TGA Donor splice site: ^GT[AG]AG Acceptor splice site: [CT]AG^ >U70368 (950 bp) 1 CTCCCTTAGA AGACTCCAGC AAGTTATTTG AAGAGGTCTT TGGAGACATG 51 GTGAGTTCTC TTTCCTTCCC AGAAGGTAAG TCTCACTGTA AGGTCTTTAT 101 GTCTTGTGTG TCCCCCAGCA GCCTTGTCAT CTCCGGCTGC CCTAGACCTG 151 CATAAGGACA GATTGAGTGT GCTGGGATAG ACTTTTGTTG ACAAAGGGGC 201 TGCTCTGCCC TTCTAAGAGG TTGAGTCTCA TCATAAGGCC TTTTGCAGCT 251 TGCATGTGTA GTGCCAGGAA AGAGTAGTCA TCCCCCAAAA CCAGACAGGA 301 ACTGACGAGA TGCAATCACT GTGTGGACTT TTTACCAGCT AGCTAGGGCA 351 CTACCATGAG CCACTGTCTA GCAGGGAGGC TTTGGGGATG GTGTGCCCCG 401 AATATCTCTC AGGGTAAGAG TTTACAGTAA GCAGCAAGCA GAGGGGTGTG 451 GGTGAGTGTG CAAGTATCTA ATTGGCTAGT TTTTGTGGCC TGTAACATAT 501 TGGTGGGTGT TGGGAGTCAT AAGCTAAATG TTTGCTTTCC TCTGCATTGG 551 TGGTCATTAG GGAGGGGGCA GATTATGAAC CTAGGTTGCA GATCTGTTGG 601 AGTAATAACA AGACACTGGT CTTGTTGGGG GTATAACCTA GAGACTCGAT 651 TTATGTTCAT GTTTGGTTTG GGATGGGTTT TATGTGAGTG TTTTCTTTTT 701 TGGGGAGGGG GTCGGTTAAC TTGGAAAGTA ATGCTAGGTA CTGTCCTGTT 751 CATTTCCCTG AGGTGAAAGT TAGGTCAGGT TTTCTAGAAT GGAGTCTGAA 801 GGTAAAACAT TTGGCCACTG GCATGCCCTA AAGTCTTTTT GTGTTCTTGT 851 CCCCTAGCAG ATCCAGCCCT ATCATCTCCT GGTGCCCAAC AGCTGCATCA 901 GGATGAAGCT CAGGTAGTGG TGGAGCTAAC TGCCAATGAC AAGCCCAGTC Find and mark potential donor splice sites in first exon First exon Second exon (Coding exons!)

Center for Biologisk Sekvensanalyse Manual Genefinding Start codon:ATG Stop codons:TAA, TAG, TGA Donor splice site: ^GT[AG]AG Acceptor splice site: [CT]AG^ >U70368 (950 bp) 1 CTCCCTTAGA AGACTCCAGC AAGTTATTTG AAGAGGTCTT TGGAGACATG 51 GTGAGTTCTC TTTCCTTCCC AGAAGGTAAG TCTCACTGTA AGGTCTTTAT 101 GTCTTGTGTG TCCCCCAGCA GCCTTGTCAT CTCCGGCTGC CCTAGACCTG 151 CATAAGGACA GATTGAGTGT GCTGGGATAG ACTTTTGTTG ACAAAGGGGC 201 TGCTCTGCCC TTCTAAGAGG TTGAGTCTCA TCATAAGGCC TTTTGCAGCT 251 TGCATGTGTA GTGCCAGGAA AGAGTAGTCA TCCCCCAAAA CCAGACAGGA 301 ACTGACGAGA TGCAATCACT GTGTGGACTT TTTACCAGCT AGCTAGGGCA 351 CTACCATGAG CCACTGTCTA GCAGGGAGGC TTTGGGGATG GTGTGCCCCG 401 AATATCTCTC AGGGTAAGAG TTTACAGTAA GCAGCAAGCA GAGGGGTGTG 451 GGTGAGTGTG CAAGTATCTA ATTGGCTAGT TTTTGTGGCC TGTAACATAT 501 TGGTGGGTGT TGGGAGTCAT AAGCTAAATG TTTGCTTTCC TCTGCATTGG 551 TGGTCATTAG GGAGGGGGCA GATTATGAAC CTAGGTTGCA GATCTGTTGG 601 AGTAATAACA AGACACTGGT CTTGTTGGGG GTATAACCTA GAGACTCGAT 651 TTATGTTCAT GTTTGGTTTG GGATGGGTTT TATGTGAGTG TTTTCTTTTT 701 TGGGGAGGGG GTCGGTTAAC TTGGAAAGTA ATGCTAGGTA CTGTCCTGTT 751 CATTTCCCTG AGGTGAAAGT TAGGTCAGGT TTTCTAGAAT GGAGTCTGAA 801 GGTAAAACAT TTGGCCACTG GCATGCCCTA AAGTCTTTTT GTGTTCTTGT 851 CCCCTAGCAG ATCCAGCCCT ATCATCTCCT GGTGCCCAAC AGCTGCATCA 901 GGATGAAGCT CAGGTAGTGG TGGAGCTAAC TGCCAATGAC AAGCCCAGTC First exon Second exon (Coding exons!) Not in frame Alternative splice forms(?)

Center for Biologisk Sekvensanalyse Manual Genefinding Start codon:ATG Stop codons:TAA, TAG, TGA Donor splice site: ^GT[AG]AG Acceptor splice site: [CT]AG^ >U70368 (950 bp) 1 CTCCCTTAGA AGACTCCAGC AAGTTATTTG AAGAGGTCTT TGGAGACATG 51 GTGAGTTCTC TTTCCTTCCC AGAAGGTAAG TCTCACTGTA AGGTCTTTAT 101 GTCTTGTGTG TCCCCCAGCA GCCTTGTCAT CTCCGGCTGC CCTAGACCTG 151 CATAAGGACA GATTGAGTGT GCTGGGATAG ACTTTTGTTG ACAAAGGGGC 201 TGCTCTGCCC TTCTAAGAGG TTGAGTCTCA TCATAAGGCC TTTTGCAGCT 251 TGCATGTGTA GTGCCAGGAA AGAGTAGTCA TCCCCCAAAA CCAGACAGGA 301 ACTGACGAGA TGCAATCACT GTGTGGACTT TTTACCAGCT AGCTAGGGCA 351 CTACCATGAG CCACTGTCTA GCAGGGAGGC TTTGGGGATG GTGTGCCCCG 401 AATATCTCTC AGGGTAAGAG TTTACAGTAA GCAGCAAGCA GAGGGGTGTG 451 GGTGAGTGTG CAAGTATCTA ATTGGCTAGT TTTTGTGGCC TGTAACATAT 501 TGGTGGGTGT TGGGAGTCAT AAGCTAAATG TTTGCTTTCC TCTGCATTGG 551 TGGTCATTAG GGAGGGGGCA GATTATGAAC CTAGGTTGCA GATCTGTTGG 601 AGTAATAACA AGACACTGGT CTTGTTGGGG GTATAACCTA GAGACTCGAT 651 TTATGTTCAT GTTTGGTTTG GGATGGGTTT TATGTGAGTG TTTTCTTTTT 701 TGGGGAGGGG GTCGGTTAAC TTGGAAAGTA ATGCTAGGTA CTGTCCTGTT 751 CATTTCCCTG AGGTGAAAGT TAGGTCAGGT TTTCTAGAAT GGAGTCTGAA 801 GGTAAAACAT TTGGCCACTG GCATGCCCTA AAGTCTTTTT GTGTTCTTGT 851 CCCCTAGCAG ATCCAGCCCT ATCATCTCCT GGTGCCCAAC AGCTGCATCA 901 GGATGAAGCT CAGGTAGTGG TGGAGCTAAC TGCCAATGAC AAGCCCAGTC First exon Second exon (Coding exons!) Alternative splice forms(?)

Center for Biologisk Sekvensanalyse How to Approach a Novel Genome First hunt for similar genes Align all known genes and ESTs from all other organisms against genome sequence Some exons more conserved than others Will not result in complete gene structures Will indicate regions potentially encoding genes Some genes will have no homology to any known genes Second hunt includes ab initio gene prediction Predict full gene structure from genomic DNA

Center for Biologisk Sekvensanalyse Gene Prediction Eukaryotic Gene Prediction Prediction relies on integration of several gene features Each gene feature carries a low signal E.g. ATG, splice sites, etc. Combinatorial explosion Some are mutually exclusive (e.g. reading frame) Sensor based HMMs well suited for gene prediction

Center for Biologisk Sekvensanalyse Sensor-based methods Ab initio Gene Finders HMM-based GenScan HMMgene Neural network-based GRAIL NetGene2 (splice sites)

Center for Biologisk Sekvensanalyse Gene Features Codon frequency/bias Organism dependent Hexamer statistics Transcriptional Promoters/enhancers Exon/introns Length distributions ORFs Splicing Donor/acceptor sites Branchpoints Translational Start codon context

Center for Biologisk Sekvensanalyse Codon Bias tRNA availability Expression level Gene Finders are often organism specific Coding regions often modelled by 5th order Markov chain (hexamers/di- codons)

Center for Biologisk Sekvensanalyse Needles Hiding in Genome Haystacks... Intron-exon structure of genes Large introns (average 3365 bp ) Small exons (average 145 bp) Long genes (average 27 kb)

Center for Biologisk Sekvensanalyse Human genes: Short exons Long introns

Center for Biologisk Sekvensanalyse Intron lengths Human genes: Introns lengths have broad distribution Min. Length ca. 60 bp

Center for Biologisk Sekvensanalyse Intron Prevalence

Center for Biologisk Sekvensanalyse Gene Prediction ”Isolated” methods Predict individual features E.g. splice sites, coding regions NetGene (Neural network) – ”Integrated” methods Predict genes in context ”Grammar” of genes Certain elements in specific order are required – HMMgene – GenScan (HMM-based)

Center for Biologisk Sekvensanalyse Gene Grammar HAPPYEUGENEAWASGUYFINDER Isolated features

Center for Biologisk Sekvensanalyse Gene Grammar HAPPYEUGENEAWASGUYFINDER Isolated features Intron 3’UTR Exon Promoter Exon RBS

Center for Biologisk Sekvensanalyse Gene Grammar EUGENEFINDERWASAHAPPYGUY Integrated features HAPPYEUGENEAWASGUYFINDER

Center for Biologisk Sekvensanalyse Gene Grammar EUGENEFINDERWASAHAPPYGUY Integrated features Prom  RBS  Exon  Intron  Exon  3’UTR

Center for Biologisk Sekvensanalyse Gene Grammar ”Isolated” methods (e.g.NN): HAPPYEUGENEAWASGUYFINDER ”Integrated” methods (e.g.HMM): EUGENEFINDERWASAHAPPYGUY

Center for Biologisk Sekvensanalyse HMMs for genefinding GenScan principle E=exon I=intron F=5’ UTR T=3’ UTR P=promoter N=intergenic

Center for Biologisk Sekvensanalyse Gene Prediction Programs ”Integrated” methods HMMgene GenScan (HMM-based) ”Isolated” methods NetGene (Neural network)

Center for Biologisk Sekvensanalyse Genscan

Center for Biologisk Sekvensanalyse Genscan

Center for Biologisk Sekvensanalyse Genscan

Center for Biologisk Sekvensanalyse Genscan

Center for Biologisk Sekvensanalyse Genscan

Center for Biologisk Sekvensanalyse HMMgene

Center for Biologisk Sekvensanalyse Defining the term ’exon’ Gene Prediction programs often use Exon = CDS (coding sequence) Real exons may contain 5’ or 3’ UTRs (untranslated regions)

Center for Biologisk Sekvensanalyse Gene Prediction – NetGene2

Center for Biologisk Sekvensanalyse Gene Prediction – NetGene2

Center for Biologisk Sekvensanalyse Gene Prediction – NetGene2

Center for Biologisk Sekvensanalyse Gene Prediction – NetGene2

Center for Biologisk Sekvensanalyse NIX – Visualizing Gene Predictions NO method is always best!

Center for Biologisk Sekvensanalyse Future 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 Matches Non-coding RNAs Most gene prediction programs only predict protein- coding genes tRNA and rRNA genes are not predicted Predict alternatice splicing, enhancers and silencers Predict matrix- and scaffold-attachment regions, insulators and boundary elements

Center for Biologisk Sekvensanalyse 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” No single method is always best Prediction methods are not perfect!

Center for Biologisk Sekvensanalyse The End

Gene Prediction Exercise SequenceGenBankGenscanHMMgeneNetGene2 Seq#1 (HoxA10) Donor H Acc H Seq#2 (Dub-2) Donor Acc Acc /exercises/gf_exercise_solution.html

Center for Biologisk Sekvensanalyse HMMgene Columns 1.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). NameMeaning 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.