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CAAAGATTATAACTAACCACATTTGCAATAACGCATAGGTCTCAAGTTTACTCATATCAATAGAATAATCAACTAGCAAGCCATAATAATAAATCTCGGATGACAACACTTTCTCAAAAC AAACATTTGCTTGTCCTCAAGCAATTCAGTTGACAAACTGAAAGTGGTAAAGAAAAACTTTTACAAACTCTGTTTTCTCTTGTTGTTGTAAATATGTAAAGCCAACATTCAAGTTTTCAG AATCATAATATGATATAACAAGATGGTATCTCGCTAGCCCTTTCTGAGACCGCAGAACATAAATGCAGAGCACCTTTAAAGACCAAGGACTGACTACACATTGTAATTCATGGTAAAAGA GATCCAGTCAAGCCATACTCAATGTAAACTAACAGTAATGAAAGCAAATGACAGCGGTGCTCTCTAACTGATGCTTTTTAATAAGAGGATGATGACTCAGGATAAAAGTAAATAGATAGG CCCTTCGCAGAGGCAAGCGGGGATTTGTAGAGGTGCCACAGCTCGGTTTTGAAATAGAGGTGAATAATATTTTGAGCGGTATACTTTCATTGTCAACATAACAACCAAGAGATGGCGATA TCTTCCATGCTACACACATTATAGGCGGTTCCCAAACAGAATGGTAAAGTTTATACTCCCCCTTCCACCAACAAGCATCAATCCATGGCTTGCTCGAAACAACGAGTGCCTCCAACTAAC AAGAGTCCCAGGGGGAGTTTTGTTTGCAATTATTTTGATTTAGTTTGCATAAAGCATGTGACTGGGCATCCCGGTGACCAGCCATTTATCTCGTGAGTGAGCAGCGGAGTCCACTCCTCT TGAGAATAACCCGCCTAACATGGAAGATACAGACAGCCCTAGCTGATACATGAGCTATTCGAGCATACAAAACAGGATACTTATTTGAAGGTTTAGAGTTTGGCACATACAAATTTACTT GGGATGGCAGGTAGATACCGTATATAGGTAGGTATAGTGGACTCATGTGGAATAACTTTGGGATTTAAGGGATTGGATGCACAAGCAGTATTCCCGCTTAGTACAGGTGAAGGCTAGCAA AAGACTGGGAAGCGACCAGCTAAAGAGCGACAACATTCATGAACATGGATTAAAATTAATCAACACCGAATGCAAGCATGAGTAGGATATAATCCACCATGAACATAAATATCGTGAAGG CTATGTTGATTTTGTTTCAACTACATGCGTGAACATGTACCAAGTCAAGTCACTTAAATCATTCAGAGGAGGATACCACCCTATCATACCACATCATAACTATCTCAATAGCATGTTGGC ACGCAAGGTAAACCATTATAACTCATAGCTAATCAAGCATGGCACAAGAAACTATGATCTCTAATTGTCATTGCAAACATGTTTATTCATAATTGGCTGAATCAGGAACGATGAACTAAT CATATTTACAAAAACAAAAGAGGTCGAGTTCATACCAGCTTTTCTCATCTCAGCCAGTCCATCATATATCATCATAATTGCCTTCACTTGCACGACTGAATAGTGTGAATAATAATAATA GTGCACGTGCATTGGACTAAGCTGGAATGTGCAAGCATTCAATAAACAGGAGAGGACAAGGCAATATGGGCTCTTTTGTCAGATAAACAATAATGCATAAAAGACCACTTCAACAATTTA ATTATGGTCTTCTCCTATCGACCCCCAAAGAAAAGAAAAGAAATAAAACTATTTACACAGGAAAGCTCCCAACAAGCAAAAGAAGAACAGGAAATCTTTTTGGGTATTATTTTTAATTAC TACTACAAGCACAGAAATTAAACTGATTAAAAGCTACAACTATTTTTTTTGGTTTTTCTTAAGGTTTCTTAAACACACAAGAAGAAAGCATAAAAAGGAAATTAAAATAGCATGGATGAT ACAATGAAAAAGTATGAGCACCGACATCTAGCAATGAGTGTGTGAACATAAATGTAATATCGGTGGGAAATACGTACTCCCCCAAGCTTATGCTTTTAGCCTAAGTTGGTCTATGGCCAC GGCTGGCCTGGCGGATATCCATAATAATAGTTGGGGTCGTAATGCAATGAGGAAGAAGAGGGCTCCGATTGCCACTGGCTGGCAATCATCTCTGGATCCCACTAGTAATTAGACTGTCGT GAGGGGTCAAATTGTGGTTCTGGCTCTGGCTCCTCGGCTGGTGCAGGGTTCCGGTAGGCTTGAATAGCCGTCACCGGAACAAGGTACGTGCCTGAAGATAGATCAAACAAAGAAGGAGCA GGCAGGGTAATAGTCCCAGGATGATGTTTATTAAAAAAAACAATTGATATTTAAGCTCTCCTGCCCTATTCTTAACAATAAAATCATGTGCCACCATACTTTTATAATCTAGATAAACAC GGGGCAGCACCTTTTCTTCCTTCTCAGCATGCCTAATAGGTATGTTAAAATGTGCAGCTAGGCGTGAGGCATAGATGCCTCCAAAGATGGGGCCCTTGGTATGGTTCAGACTTAACCGTT TAGCAATAATACCGCCCATGCTAAAAGTGTTATCACTATATAAACCTTGAAGCAAAATAATAATATCAGGGATACTAAGGTTTCCATAGTTTCCGCGACCAATTAAGAAACGATTAGCAA ATAATGCAAAGTAACGTAAAACAGGAAAATGTATGCTAGTGATTCGTGCATCGGAAACTTTCCTAGTTTCCCCTACAGCGATAGTATCAATAAAACCCTCCACATCATCACGATGTGGTT CTTCTGTCTTGCCCTCAGAAGGGACTAGACAAATCTGACAAAAATCATAAAGTGACATCTCCTTATGCTCATCATATAGATGAAACTCCACCGTAGGTGGTGAGTTCCTAGAATGAAAAT GAAAGTTTTGCACAAAGGTATTTGTGAGCAAGAGATACTGATCGCATTGGTCATGGAGGAAAGCGGTGAGGCCTGCATTATGAGCCAATTCATGAAAATCTTCATAAATCCCGGCTGCTC TCAAGAATTCATCGGAAGGCCATTCACACGCCCGAATCTCCGTGGTGCGCGGCAAATTATACATAGGCTATGGTGCCTTTTCCTTCGAGCTTTGGCTCGATGAGCCCCTCAATAGTCTCC TCGTCATTTTCTGAAAAATTCTGAAAATTTTAGTAACTTCAAAATAAAAGTAAACCAAACTCAATAATATTGATAGCAACTACTCCTACAAGTGCCTAGGGCCTATATCATGCATCAAAA CTACTTTTGACCATATAAATTTGACATGCAAGCTCAAGAACAGGGTCACCTAAGCAGCAAAAATTTGCAATGAATAAAGCACTAGAACAAAAACTAATTGGACCAATGGAGGAGTCACAT ACCAAGGAACAATCTCCCCAAGCAGTTTTGTGAGAGGTGCTTTGAGCAAGGAGATCGAAAATGGCAGCAAGATGAGCTAGAACTCGGGTTTGATCTGGTTATTCGTGTTTGTGGGAGGAA GATGAAGTGTGTGGGTGCAGGAATAAGTGGAGGAGGGCCACCGTGGGCCCATGAGGCAGCGGGCGCGCCCTACACCCTCGTGGGCAAGTGGTTGACCCCCCTGGTGTGTTTTCAGTTCCA TAAATCCTCAAATATTCTAGAAAAAATATATTTAAATTTCAAGGCATTTGGAGAACTTTTATTTTCGAGGTATTTTTATATTGCACGAATAATCAGATAACAGACAGAAAAATATTTATT TTATTTTTATTTAATATGAATAACAGAAAGTAAGAGTGGGGTATAATGCAAAGTAACGTAAAACAGGAAAATGTATGCTAGTGATTCGTGCATCGGAAACTTTCCTAGTTTCCCCTACAG CGATAGTATCAATAAAACCCTCCACATCATCACGATGTGGTTCTTCTGTCTTGCCCTCAGAAGGGACTAGACAAATCTGACAAAAATCATAAAGTGACATCTCCTTATGCTCATCATATA GATGAAACTCCACCGTAGGTGGTGAGTTCCTAGAATGAAAATGAAAGTTTTGCACAAAGGTATTTGTGAGCAAGAGATACTGATCGCATTGGTCATGGAGGAAAGCGGTGAGGCCTGCAT TATGAGCCAATTCATGAAAATCTTCATAAATCCCGGCTGCTCTCAAGAATTCATCGGAAGGCCATTCACACGCCCGAATCTCCGTGGTGCGCGGCAAATTATACATAGGCTATGGTGCCT TTTCCTTCGAGCTTTGGCTCGATGAGCCCCTCAATAGTCTCCTCGTCATTTTCTGAAAAATTCTGAAAATTTTAGTAACTTCAAAATAAAAGTAAACCAAACTCAATAATATTGATAGCA ACTACTCCTACAAGTGCCTAGGGCCTATATCATGCATCAAAACTACTTTTGACCATATAAATTTGACATGCAAGCTCAAGAACAGGGTCACCTAAGCAGCAAAAATTTGCAATGAATAAA GCACTAGAACAAAAACTAATTGGACCAATGGAGGAGTCACATACCAAGGAACAATCTCCCCAAGCAGTTTTGTGAGAGGTGCTTTGAGCAAGGAGATCGAAAATGGCAGCAAGATGAGCT AGAACTCGGGTTTGATCTGGTTATTCGTGTTTGTGGGAGGAAGATGAAGTGTGTGGGTGCAGGAATAAGTGGAGGAGGGCCACCGTGGGCCCATGAGGCAGCGGGCGCGCCCTACACCCT CGTGGGCAAGTGGTTGACCCCCCTGGTGTGTTTTCAGTTCCATAAATCCTCAAATATTCTAGAAAAAATATAATAATGCAAAGTAACGTAAAACAGGAAAATGTATGCTAGTGATTCGTG CATCGGAAACTTTCCTAGTTTCCCCTACAGCGATAGTATCAATAAAACCCTCCACATCATCACGATGTGGTTCTTCTGTCTTGCCCTCAGAAGGGACTAGACAAATCTGACAAAAATCAT AAAGTGACATCTCCTTATGCTCATCATATAGATGAAACTCCACCGTAGGTGGTGAGTTCCTAGAATGAAAATGAAAGTTTTGCACAAAGGTATTTGTGAGCAAGAGATACTGATCGCATT GGTCATGGAGGAAAGCGGTGAGGCCTGCATTATGAGCCAATTCATGAAAATCTTCATAAATCCCGGCTGCTCTCAAGAATTCATCGGAAGGCCATTCACACGCCCGAATCTCCGTGGTGC GCGGCAAATTATACATAGGCTATGGTGCCTTTTCCTTCGAGCTTTGGCTCGATGAGCCCCTCAATAGTCTCCTCGTCATTTTCTGAAAAATTCTGAAAATTTTAGTAACTTCAAAATAAA AGTAAACCAAACTCAATAATATTGATAGCAACTACTCCTACAAGTGCCTAGGGCCTATATCATGCATCAAAACTACTTTTGACCATATAAATTTGACATGCAAGCTCAAGAACAGGGTCA Sequence annotation Annotation performed on completed sequence Computer programs used to find the following: Genes Exons and introns Regulatory sequences Repetitive elements Gene discovery More difficult in Eukaryotes because of the presence of introns Extrinsic Methods Complete cDNAs and ESTs Comparative genomics Intrinsic methods Gene prediction programs Grail GENSCAN FGENES GeneSeqer http://bioinformatics.iastate.edu/cgi-bin/gs.cgi Method to identify potential exon/intron structure by splice site prediction and spliced alignment. Step 1: Select splice site model Step 2: Input genomic DNA sequence Step 3: Select or input cDNA/EST sequences Step 4: Submit job Be careful: retroelements are also present in EST collections! Dotplot Triticeae vs. rice BACs Barley BAC 635P2 (horizontal) vs. orthologous Rice BAC 36I05 (vertical) (Dubcovsky et al. 2001, Plant Physiology): G1 G2 G3 G4 Th G5 Ti G1 Ta Tb Tc G2 Td Te Tf Te G3 G 4a G 4b Tg Comparative genomics is a powerful approach to identify genes: Coding regions are more conserved than non-coding regions Programs used in human-mouse comparisons: TWINSCAN and SLAM Intrinsic Gene prediction Search by signal TATA box Translational start (ATG) 5 or donor splice site (GT, rarely GC) 3 or acceptor splicing site (AG) Translational stop (TAA, TAG, TGA) PolyA signal (AATAAA) TATAAAAATCGTTGTGGGGGCGGGATTG GGCGGCCATGGACACCTCTACTAGGGCG GCCAAGATCCCCTCCCTCCCCCAACAGA CGGAGATCAACTGGGACAAGTAAGCGCC CGCCCGCCCGCCCGCCTCCTCTTTCCTC AGCCGTTCCCCGGCTTATTTTTCTATTT TTTCCTCTAGGTTAATATCAACAAGCCA AAAGCCAGTGAAGTAGTTAGAAGATTGA TCGCATGGATGGAAAGGTTTCTAAGTAT GCCCATCCTTTCCGAGTCTTATTTCCTT CCGCTTCTTCCCAGAGCGTGTATGTGCT AAAGTTGAAGAGGCCCTGAGTCTATAAT ATTCTCATCAATAAA Search by content and compositional bias Absolute frequency of the pair A(k)A with k (0 to 50) in the first 200-bp from 1761 human genes and 1753 introns. In coding regions every 3rd base tends to be the same more often than by chance alone. Hidden Markov models Many uses in genomics Gene prediction Multiple sequence alignment Finding periodic patterns HMMs are trained on sequences that are members of known gene class HMM gives probability that a particular sequence belongs to the gene class A DNA sequence can be considered a fifth-order Markov model because the probability of a given base depends on the five preceding bases GENSCAN and GENIE are examples of HMM-based gene finding programs are ANALOGY: You can think of the HMMs as representing a trip across the country in which there are alternative routes (a,b,c) and places to eat (R1-4) and stay (H1-3). If we keep track of the trips of locals and tourists in a survey, we come up with the most common routes restaurants and hotels that locals and tourists use. We can calculate probabilities for each choice and probabilities for each transition. The product of all these probabilities is the probability of a certain trip. After enough data is collected we can predict with certain probability if a traveler is a local or a tourist based on a trip description. Similarly if we keep track of a of lot genes for a particular organism, going from one sequence position to the next and choosing a particular base at each location, skipping certain positions or adding more, we can come up with the best path that are followed by most gene sequences, representing the conserved sequence positions, e.g. preferred codons and splice sites in that organism. Our gene sequence survey will allow us to calculate the probabilities that any new sequence is indeed a gene H1 H2 H3 R1 R2 R3 Site2 a, b, c R4 Site3 a, b, c BEG Site1 a, b, c END Neural Networks in gene prediction Computational model comprising a collection of neuron-like elements. Input neurons receive sequence, analyze one parameter and pass information to a hidden layer. The strength of the signals are trained to produce correct answer. The output layer integrates the signals and produce the answer The Grail II system for finding exons in eukaryotic genes uses a neural network to identify patterns characteristic of coding sequences. Similarities include the use of three layers, an input layer for the data with the data coming from a candidate exon sequence, and a hidden layer for discerning relationships among the input data. An output layer comprising one neuron indicates whether or not the region is likely to be an exon. Each neuron receives information from a set in the layer above (some with a positive value and others with a negative value), sums these values, and then converts them to an output of approximately 0 or 1. The system is trained using a set of known coding sequences, and, as each sequence is utilized, the strengths and types of connections (positive or negative) between the neurons are adjusted, decreasing or increasing the signal to the next neuron in a manner that produces the correct output. Neural networks for exon prediction use sequence pattern information as input. In Grail II, a candidate sequence is evaluated by calculating pattern frequencies in the sequence and applying these values to the neural network. If the output is close to a value of 1, then the region is predicted to be an exon . Discriminant analysis in gene prediction Discriminant function analysis is a frequently used technique aimed at determining which variables, can discriminate between two or more naturally occurring groups. In the area of gene prediction, multiple variables are used to try to discriminate whether a particular stretch of DNA is from an intron or an exon (presence of putative acceptor sites, donor sites, or start and stop codons, etc.). For two variables (for instance, splice site scores and exon length) the relationships can be represented by an XY graph The two different symbols on the graph represent different groups: x=: exon, whereas o =intron. A linear (Lx) or a quadratic function (Qx) can be used to try to separate the two groups. For three variable the graph will have a volume and for more than three variables you use ndimensional spaces. FGENESH lineal discriminant analysis http://sun1.softberry.com/berry.phtml?topic=fgenesh&gro up=programs&subgroup=gfind MZEF quadratic discriminant analysis http://rulai.cshl.org/tools/genefinder/ Gene Finding Programs Most gene finding programs use dynamic programming: Grail II, FGENESD, GENEID The use of HMM marked a significant advance in the area of gene prediction methodologies. Popular HMM-based gene finding programs are GENSCAN and GENIE Some programs combine ab initio predictions with BLASTX comparisons of translated predicted exons: GENOMESCAN, GENEID. GRAILEXP uses splice site models to align ESTs Different programs produce different results. Use more than one and be critical! Gene prediction of UROD gene( U30787) by FGENESH, GRAIL, GENEID and GENSCAN. The correct EMBL gene is indicated in red Gene Finding Programs: GeneID Uses a hierarchical approach: 1st predicts putative start, stop, and splicing sited 2nd exons are built from these defining sites 3rd calculates coding potential scores 4th the most likely structure is predicted The 2002 version combines ab initio predictions with BLASTX comparisons of translated predicted exons http://genome.imim.es/software/geneid/index.html Exon coordinates in the Protein Protein Gene Finding Programs: GENSCAN General purpose eukaryotic gene prediction program New method: maximal dependence decomposition, using several weight matrices (uses HMM) GenomeScan is an extension that incorporates sequence similarity to known proteins using BLASTX http://genes.mit.edu/GENSCAN.html http://genes.mit.edu/genomescan.html P values > 0.99 are 98% accurate, P>0.9= 88% accurate Gene Finding Programs FGENESH Uses linear discriminant analysis to identify exons and promoter elements (MZEF is another program that uses quadratic discriminant analysis) Filtered exons are assembled using dynamic programming FGENESH is a HMM version of FGENES FGENESH+ & FGENESH-C incorporates protein and cDNA sequence homology. Predicts TATA boxes and PolyA signals http://linux1.softberry.com/berry.phtml?topic=fgenes_plus&group=programs&subgroup=gfs Summary Genes Translate in all 6 frames and look for protein sequence databases (BLASTX) BLASTN to EST database Use gene prediction programs to locate gene Retest the predicted protein against protein database Transposable elements DNA regions flanked by repeats that can jump around the genome Transposable-element (TE) insertion can be fast Allows TEs to rapidly accumulate in genome 44% of human genome sequence is TEs Autonomous: all genes required for transposition Nonautonomous TEs can have different effects on the host Insertion into gene can destroy function TE can evolve into beneficial gene TE can affect gene expression But most TEs will have a neutral effect Inactive Class I transposable elements Use RNA to transcribe themselves Types Long terminal repeat (LTR) retrotransposons Non-LTR retrotransposons Class I transposable elements in the human genome account for ~42% of the total genome sequence Retrotransposon annotation TACAT TGTA Short inverted repeats flank the LTR Host dup. Inv. Flanking LTR LTR TREP annotation provides names and LTRs Wheat 5AL (FL 0.75) 215-kb LTR TGTA TACAT Short host duplications flank the retroelements Inv. Flanking LTR Host dup. They are useful to define borders of nested elements Class II transposable elements Use DNA-based method of transposition Approximately 200,000 copies of this type of TE in the human genome Class II also includes P-element transposons in Drosophila and activation-dissociation (Ac/Ds) elements in maize TSD transposase inverted terminal repeats Example: Tc1-mariner (1.4 kb) inverted terminal repeats TSD Miniature inverted transposable elements (MITEs) Found in plants, insects, nematodes, and humans Nonautonomous class II transposons Small (< 500 bp), less disruptive, size possibly responsible for high copy number Retrotransposon discovery Compare a sequence with itself! Pattern of an LTR retroelement Pattern of a small MITE Inverted repeat MITE2.txt vs MITE2.txt LTR WIS.txt vs WIS.txt 2 nested LTRs hwk4repeat.txt vs hwk4repeat.txt Repetitive elements databases REPBASE: http://www.girinst.org/repbase/index.html (animals, little from plants) REPEATMASKER: http://www.repeatmasker.org/ (animals, little from plants) TIGR: rice and grasses http://tigrblast.tigr.org/euk-blast/index.cgi?project=osa1 (rice) Thomas Wicker, Curator Last update 31 August 2005 TREP, the Triticeae Repeat Sequence Database http://wheat.pw.usda.gov/ITMI/Repeats/index.shtml Choose program and database to search Program Database Browse TREP -- summary tables Nonredundant Complete TREP annotations Nonredundant Complete FASTA files Nonredundant Complete Complete, by species Triticum, Aegilops Hordeum Secale Other Enter sequence below in FASTA format BLAST against TREP Search This database was created by the initiative of Dave Matthews, Thomas Wicker and Jorge Dubcovsky Summary Genes Translate in all 6 frames and look for protein sequence databases (BLASTX) BLASTN to EST database Use gene prediction programs to locate gene Retest the predicted protein against protein database Transposons Class I RNA intermediate Frequent replication Class II DNA intermediate Rare replication MITEs DNA intermediate High copy number in plants

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