Genome analysis2
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This document discusses gene identification and genome annotation. It describes how gene finding in eukaryotes is difficult due to smaller percentages of genes in genomes like humans, and larger intron sizes. It covers open reading frames, complications with introns, and the use of six-frame translation to find protein coding sequences. Software tools for structural and functional annotation are outlined, including identifying genes through homology searching and ab initio prediction using hidden Markov models. The accuracy challenges of ab initio prediction are also summarized.
Genome analysis2
- 3. Six ORFs of dsDNA
- 4. Six ORFs of dsDNA
- 6. Genome annotation •Annotation : Obtaining biological information from unprocessed sequence data •Structural annotation : Identification of genes and other other important sequence elements •Functional annotation : The determination of the functional roles of genes in the organism
- 7. Genome annotation •Raw genomic sequence can be annotated by, i. Comparison with databases of previously cloned genes and ESTs ii. Gene prediction based on consensus features such as Promoters Splice sites Polyadenylation sites and ORFs
- 8. Gene identification Gene finding in eukaryotes is difficult Genome Genes Bacterial genome 80-85% Yeast 70% Fruit fly 25% Human genome 3-5% In human genome, Typical exon = 150bp Intron = Several kbs Complete gene = Hundreds of kbs
- 9. ORF prediction •Three reading frames are possible from each strand of a DNA using “six-frame translation process” - Result is 6 potential protein sequences - Longest frame uninterrupted by a stop codon is the correct one •Finding the ends of ORF is easier than finding beginning Beginning can be find using, - Start codon - kozak sequence (CCGCCAUGG) flanking start codon - CpG islands
- 10. Software programs for gene identification •Advantage : Speed – annotation can be carried out concurrently with sequencing itself. •Disadvantage : Accuracy •Two strategies used are, - Homology searching - ab initio prediction
- 15. ab initio prediction Based on type of algorithm, GRAIL – Based on neural networks - Predicts exons, genes, promoters, polyAs, CpG islands EST similarities, repetitive elements, GeneFinder – Rule-based system GENSCAN, GENEI, HMMGene, GeneMarkHMM, FGENEH – Hidden Markov model
- 16. Genescan
- 17. ab initio prediction 1. Feature dependent methods, Features of eukaryotic genes recognized are, -Control signals such as TATA box, cap site, Kozak consensus and polyadenylation sites HEXON, MZEF are gene predicting programs that can predict only a single feature, exon. 2. Few programs depend on differences in base composition
- 18. ab initio prediction Accuracy problem – Algorithms are not 100% accurate Errors include - Incorrect calling of exon boundaries - Missed exons - Failure to detect entire genes Solution: Running different programs on single genome
- 19. Homology searching •Finding genes in long sequences by looking for matches with sequences that are known to be transcribed, e.g. cDNA, EST or a gene Programs used are BLAST (Basic Local Alignment Search Tool) based, BLASTN BLASTX BLASTP etc.
- 20. Homology searching or ab initio ? •Algorithms that take similarity data into account are better at gene prediction – Reese et al(2000), Fortna et al(2001) Latest gene prediction algorithms combine similarity data with ab initio methods examples : Grail/Exp, GenieEST, GenomeScan tRNAScanSE : For tRNA identification
- 22. GLIMMER •Gene Locator and Interpolated Markov ModelER •For finding genes in microbial DNA
- 23. GLIMMER
- 24. GLIMMER
- 25. GeneMark
- 26. GeneMark
- 27. GenScan