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Genome Bioinformatics

Tyler Alioto Center for Genomic Regulation Barcelona, Spain. Genome Bioinformatics. Node 1 of the INB. GN1 Bioinformática y Genómica Genome Bioinformatic Lab, CRG Roderic Guigó (PI). Themes. Gene prediction ab initio => GeneID dual-genome => SGP2 u12 introns => GeneID v1.3 and U12DB

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Genome Bioinformatics

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  1. Tyler Alioto Center for Genomic Regulation Barcelona, Spain Genome Bioinformatics Bioinformatics Workshop - Malaga

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

  3. Bioinformatics 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. Bioinformatics Workshop - Malaga Eukaryotic gene structure

  5. Bioinformatics Workshop - Malaga Eukaryotic gene structure INTRONS PROMOTOR donor acceptor EXONS DOWNSTREAM REGULATOR UPSTREAM REGULATOR

  6. Bioinformatics Workshop - Malaga The Splicing Code

  7. Bioinformatics 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. Bioinformatics Workshop - Malaga Gene Prediction Strategies

  9. Bioinformatics 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. How does GeneID approach gene prediction? Bioinformatics Workshop - Malaga

  11. Bioinformatics Workshop - Malaga The gene prediction problem e4 e8 sites a4 a2 a1 a3 exons d1 d2 e1 d3 e2 d4 e3 d5 e4 e5 e6 e7 genes e8 e1

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

  13. Bioinformatics Workshop - Malaga Training GeneID 1 2 3 4 5 6 7 8 9 GAGGTAAAC TCCGTAAGT CAGGTTGGA ACAGTCAGT TAGGTCATT TAGGTACTG ATGGTAACT CAGGTATAC TGTGTGAGT AAGGTAAGT A 0.3 0.6 0.1 0.0 0.0 0.6 0.7 0.2 0.1 C 0.2 0.2 0.1 0.0 0.0 0.2 0.1 0.1 0.2 G 0.1 0.1 0.7 1.0 0.0 0.1 0.1 0.5 0.1 T 0.4 0.1 0.1 0.0 1.0 0.1 0.1 0.2 0.6 ATGGCAGGGACCGTGACGGAAGCCTGGGATGTGGCAGTATTTGCTGCCCGACGGCGCAAT GATGAAGACGACACCACAAGGGATAGCTTGTTCACTTATACCAACAGCAACAATACCCGG GGCCCCTTTGAAGGTCCAAACTATCACATTGCGCCACGCTGGGTCTACAATATCACTTCT GTCTGGATGATTTTTGTGGTCATCGCTTCAATCTTCACCAATGGTTTGGTATTGGTGGCC ACTGCCAAATTCAAGAAGCTACGGCATCCTCTGAACTGGATTCTGGTAAACTTGGCGATA GCTGATCTGGGTGAGACGGTTATTGCCAGTACCATCAGTGTCATCAACCAGATCTCTGGC

  14. Bioinformatics 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 <gff_exons_file>] [-R <gff_annotation-file>] [-S <gff_homology_file>] [-P <parameter_file>] [-E exonweight] [-V evidence_exonweight] [-Bv] [-h] <locus_seq_in_fasta_format> 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 <exons_filename>: Only running gene prediction (not exon prediction)‏ -Z: Activate Open Reading Frames searching -R <exons_filename>: Provide annotations to improve predictions -S <HSP_filename>: 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 <parameter_file>: 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. Bioinformatics 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 HS307871 geneid_v1.2 Internal 1710 1860 -0.11 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 1976 2055 0.24 + 2 HS307871_1 HS307871 geneid_v1.2 Internal 2132 2194 0.44 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 2434 2682 4.66 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 2749 2910 3.19 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 3279 3416 0.97 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 3576 3676 3.23 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 3780 3846 -0.96 + 1 HS307871_1 HS307871 geneid_v1.2 Terminal 4179 4340 4.55 + 0 HS307871_1

  16. Bioinformatics 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. Bioinformatics 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 HS307871 geneid_v1.2 Internal 1710 1860 -0.11 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 1976 2055 0.24 + 2 HS307871_1 HS307871 geneid_v1.2 Internal 2132 2194 0.44 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 2434 2682 4.66 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 2749 2910 3.19 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 3279 3416 0.97 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 3576 3676 3.23 + 0 HS307871_1 HS307871 geneid_v1.2 Internal 3780 3846 -0.96 + 1 HS307871_1 HS307871 geneid_v1.2 Terminal 4179 4340 4.55 + 0 HS307871_1

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

  19. Bioinformatics 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. Bioinformatics 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. Bioinformatics Workshop - Malaga Syntenic Gene Prediction: SGP2

  22. Bioinformatics 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. Bioinformatics Workshop - Malaga Splice Signal Profiles: major and minor

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

  25. Bioinformatics Workshop - Malaga

  26. Bioinformatics Workshop - Malaga Coming Soon: GenePCa Gene Prediction Combiner

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

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