1. Jul-01-0806/16/08Bioinformatics Workshop - Malaga Genome Bioinformatics Tyler Alioto Center for Genomic Regulation Barcelona, Spain

2. Jul-01-08Bioinformatics Workshop - Malaga Node 1 of the INB GN1 Bioinformática y Genómica Genome Bioinformatic Lab, CRG Roderic Guigó (PI)

3. Jul-01-08Bioinformatics Workshop - Malaga Themes Gene prediction ab initio => GeneID dual-genome => SGP2 u12 introns => GeneID v1.3 and U12DB combiner => GenePC Genome feature visualization gff2ps Alternative splicing ASTALAVISTA Gene expression regulatory elements meta and mmeta alignment

4. Jul-01-08Bioinformatics Workshop - Malaga Eukaryotic gene structure

5. Jul-01-08Bioinformatics Workshop - Malaga Eukaryotic gene structure EXONS INTRONS UPSTREAM REGULATOR DOWNSTREAM REGULATOR PROMOTOR acceptordonor

6. Jul-01-08Bioinformatics Workshop - Malaga The Splicing Code

7. Jul-01-08Bioinformatics Workshop - Malaga Gene Prediction Strategies Expressed Sequence (cDNA) or protein sequence available? Yes  Spliced alignment BLAT, Exonerate, est_genome, spidey, GMAP, Genewise No  Integrated gene prediction Informant genome(s) available? Yes  Dual or n-genome de novo predictors: SGP2, Twinscan, NSCAN, (Genomescan – same or cross genome protein blastx)‏ No  ab initio predictors geneid, genscan, augustus, fgenesh, genemark, etc. Many newer gene predictors can run in multiple modes depending on the evidence available.

8. Jul-01-08Bioinformatics Workshop - Malaga Gene Prediction Strategies

9. Jul-01-08Bioinformatics Workshop - Malaga Frameworks for gene prediction Hierarchical exon-buliding and chaining Hidden Markov Models (many flavors) HMM, GHMM, GPHMM, Phylo-HMM Conditional Random Fields (new!) Conrad, Contrast... and, no doubt, more to come All of them involve parsing the optimal path of exons using dynamic programming (e.g. GenAmic, Viterbi algorithms)

10. Jul-01-0806/16/08Bioinformatics Workshop - Malaga How does GeneID approach gene prediction?

11. Jul-01-08Bioinformatics Workshop - Malaga The gene prediction problem a1a1 a2a2 a3a3 a4a4 d1d1 d2d2 d3d3 d4d4 d5d5 e1e1 e2e2 e3e3 e4e4 e5e5 e6e6 e7e7 e8e8 sites exons genes e1e1 e4e4 e8e8

12. Jul-01-08Bioinformatics Workshop - Malaga GeneID Geneid follows a hierarchical structure: signal  exon  gene signalexongene Exon score: Score of exon-defining signals + protein-coding potential (log-likelihood ratios) Dynamic programming algorithm: maximize score of assembled exons  assembled gene

13. Jul-01-08Bioinformatics Workshop - Malaga 0.60.20.1 1.00.00.1 0.4T 0.10.50.1 0.01.00.70.1 G 0.20.1 0.20.0 0.10.2 C 0.10.20.70.60.0 0.10.60.3A 987654321 GAGGTAAAC TCCGTAAGT CAGGTTGGA ACAGTCAGT TAGGTCATT TAGGTACTG ATGGTAACT CAGGTATAC TGTGTGAGT AAGGTAAGT ATGGCAGGGACCGTGACGGAAGCCTGGGATGTGGCAGTATTTGCTGCCCGACGGCGCAAT GATGAAGACGACACCACAAGGGATAGCTTGTTCACTTATACCAACAGCAACAATACCCGG GGCCCCTTTGAAGGTCCAAACTATCACATTGCGCCACGCTGGGTCTACAATATCACTTCT GTCTGGATGATTTTTGTGGTCATCGCTTCAATCTTCACCAATGGTTTGGTATTGGTGGCC ACTGCCAAATTCAAGAAGCTACGGCATCCTCTGAACTGGATTCTGGTAAACTTGGCGATA GCTGATCTGGGTGAGACGGTTATTGCCAGTACCATCAGTGTCATCAACCAGATCTCTGGC Training GeneID

14. Jul-01-08Bioinformatics Workshop - Malaga Running GeneID command line or on geneid server NAME geneid - a program to annotate genomic sequences SYNOPSIS geneid[-bdaefitnxszr] [-DA] [-Z] [-p gene_prefix] [-G] [-3] [-X] [-M] [-m] [-WCF] [-o] [-j lower_bound_coord] [-k upper_bound_coord] [-O ] [-R ] [-S ] [-P ] [-E exonweight] [-V evidence_exonweight] [-Bv] [-h] RELEASE geneid v 1.3 OPTIONS -b: Output Start codons -d: Output Donor splice sites -a: Output Acceptor splice sites -e: Output Stop codons -f: Output Initial exons -i: Output Internal exons -t: Output Terminal exons -n: Output introns -s: Output Single genes -x: Output all predicted exons -z: Output Open Reading Frames -D: Output genomic sequence of exons in predicted genes -A: Output amino acid sequence derived from predicted CDS -p: Prefix this value to the names of predicted genes, peptides and CDS -G: Use GFF format to print predictions -3: Use GFF3 format to print predictions -X: Use extended-format to print gene predictions -M: Use XML format to print gene predictions -m: Show DTD for XML-format output -j Begin prediction at this coordinate -k End prediction at this coordinate -W: Only Forward sense prediction (Watson)‏ -C: Only Reverse sense prediction (Crick)‏ -U: Allow U12 introns (Requires appropriate U12 parameters to be set in the parameter file)‏ -r: Use recursive splicing -F: Force the prediction of one gene structure -o: Only running exon prediction (disable gene prediction)‏ -O : Only running gene prediction (not exon prediction)‏ -Z: Activate Open Reading Frames searching -R : Provide annotations to improve predictions -S : Using information from protein sequence alignments to improve predictions -E: Add this value to the exon weight parameter (see parameter file)‏ -V: Add this value to the score of evidence exons -P : Use other than default parameter file (human)‏ -B: Display memory required to execute geneid given a sequence -v: Verbose. Display info messages -h: Show this help AUTHORS geneid_v1.3 has been developed by Enrique Blanco, Tyler Alioto and Roderic Guigo. Parameter files have been created by Genis Parra and Tyler Alioto. Any bug or suggestion can be reported to geneid@imim.es

15. Jul-01-08Bioinformatics Workshop - Malaga GeneID output ## gff-version 2 ## date Mon Nov 26 14:37:15 2007 ## source-version: geneid v 1.2 -- geneid@imim.es # Sequence HS307871 - Length = 4514 bps # Optimal Gene Structure. 1 genes. Score = 16.20 # Gene 1 (Forward). 9 exons. 391 aa. Score = 16.20 HS307871geneid_v1.2 Internal 1710 1860-0.11+0 HS307871_1 HS307871geneid_v1.2 Internal 1976 2055 0.24+2 HS307871_1 HS307871geneid_v1.2 Internal 2132 2194 0.44+0 HS307871_1 HS307871geneid_v1.2 Internal 2434 2682 4.66+0 HS307871_1 HS307871geneid_v1.2 Internal 2749 2910 3.19+0 HS307871_1 HS307871geneid_v1.2 Internal 3279 3416 0.97+0 HS307871_1 HS307871geneid_v1.2 Internal 3576 3676 3.23+0 HS307871_1 HS307871geneid_v1.2 Internal 3780 3846-0.96+1 HS307871_1 HS307871geneid_v1.2 Terminal 4179 4340 4.55+0 HS307871_1

16. Jul-01-08Bioinformatics Workshop - Malaga GFF: a standard annotation format Stands for: Gene Finding Format -or- General Feature Format Designed as a single line record for describing features on DNA sequence -- originally used for gene prediction output 9 tab-delimited fields common to all versions seq source feature begin end score strand frame group The group field differs between versions, but in every case no tabs are allowed GFF2: group is a unique description, usually the gene name. NCOA1 GFF2.5 / GTF (Gene Transfer Format): tag-value pairs introduced, start_codon and stop_codon are required features for CDS transcript_id “NM_056789” ; gene_id “NCOA1” GFF3: Capitalized tags follow Sequence Ontology (SO) relationships, FASTA seqs can be embedded ID=NM_056789_exon1; Parent=NM_056789; note=“5’ UTR exon”

17. Jul-01-08Bioinformatics Workshop - Malaga GeneID output ## gff-version 2 ## date Mon Nov 26 14:37:15 2007 ## source-version: geneid v 1.2 -- geneid@imim.es # Sequence HS307871 - Length = 4514 bps # Optimal Gene Structure. 1 genes. Score = 16.20 # Gene 1 (Forward). 9 exons. 391 aa. Score = 16.20 HS307871geneid_v1.2 Internal 1710 1860-0.11+0 HS307871_1 HS307871geneid_v1.2 Internal 1976 2055 0.24+2 HS307871_1 HS307871geneid_v1.2 Internal 2132 2194 0.44+0 HS307871_1 HS307871geneid_v1.2 Internal 2434 2682 4.66+0 HS307871_1 HS307871geneid_v1.2 Internal 2749 2910 3.19+0 HS307871_1 HS307871geneid_v1.2 Internal 3279 3416 0.97+0 HS307871_1 HS307871geneid_v1.2 Internal 3576 3676 3.23+0 HS307871_1 HS307871geneid_v1.2 Internal 3780 3846-0.96+1 HS307871_1 HS307871geneid_v1.2 Terminal 4179 4340 4.55+0 HS307871_1

18. Jul-01-08Bioinformatics Workshop - Malaga Visualizing features with gff2ps generated by Josep Abril

19. Jul-01-08Bioinformatics Workshop - Malaga Visualizing features on UCSC genome browser (custom tracks) If “your” genome is served by UCSC, this is a good option because: browsing is dynamic access to other annotations can view DNA sequence can do complex intersections and filtering gff2ps is good when: your genome is not on UCSC you want more flexible layout options you want to run it ‘offline’

20. Jul-01-08Bioinformatics Workshop - Malaga Extensions to GeneID Syntenic Gene Prediction (dual-genome) Evidence-based (constrained) gene prediction U12 intron detection Combining gene predictions Selenoprotein gene prediction

21. Jul-01-08Bioinformatics Workshop - Malaga Syntenic Gene Prediction: SGP2

22. Jul-01-08Bioinformatics Workshop - Malaga Minor splicing and U12 introns U12 introns make up a minor proportion of all introns (~0.33% in human, less in insects) But they can be found in 2-3% of genes Normally ignored, but this causes annotation problems Easy to predict due to highly conserved donor and branch sites

23. Jul-01-08Bioinformatics Workshop - Malaga Splice Signal Profiles: major and minor

24. Jul-01-08Bioinformatics Workshop - Malaga Gathering U12 Introns U12 DB genome Human merge published all annotated introns score predict 563 568 385 658 ENSEMBL? ortholog search (17 species)‏ + spliced alignment genome Fruit Fly all annotated introns score predict merge aln to EST/ mRNA aln to EST/ mRNA 2084 597

25. Jul-01-08Bioinformatics Workshop - Malaga

26. Jul-01-08Bioinformatics Workshop - Malaga Coming Soon: GenePC a Gene Prediction Combiner

27. Jul-01-08Bioinformatics Workshop - Malaga Tutorial Homepage http://genome.imim.es/courses/Malaga08/ GBL Homepage http://genome.imim.es/