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Gene Prediction
- 1. Molecular Data Analysis BIT-757, 3(3-0) Dr. Farrukh Azeem MS Biotechnology 1st semester 2015-17 Gene and Regulatory Element Prediction
- 2. Levels of Gene Regulation
- 3. Strategies used in Gene Prediction Programs Homology based • Based on comparison with other sequence Ab-initio based • Based on given sequence • Use two major features a) Gene signals - Start and stop codons - Transcription factor binding sites - Ribosomal binding sites - Polyadenylation (Poly-A) sites b) Gene content - Nucleotide composition - Pattern of sequence in coding and non-coding region - GC content etc.
- 4. Gene Prediction in Prokaryotes Characteristics of Genome - Small size (0.5 to 10Mbp) - High gene density….90% genome is coding sequence - Few repetitive sequences - No Introns - ATG, ….also GTG, TTG - Shine-Delgarno sequence……..a Purin-rich sequence complementary to 16sRNA in ribosome…..AGGAGGT…present downstream of TSS and upstream of Translation ignition codon - Possible stop codons.. Three - Operon….. Followed by termination codon (p-independent terminator)…….and stretch of TTTT
- 5. Gene Prediction in Prokaryotes
- 6. Gene Prediction in Prokaryotes Conventional Method a) Identification of ORF and major signals related to prokaryotic gene - Conceptual translation in all six reading frames - Identification of a frame longer than 30 codons … a stop codon in every 20 codons by chance - Confirmation of signals like Shine-Delgarno sequence and other signals - Sequence similarity searching by BLAST… b) Codon Biasness.. Third codon nucleotide…G/C…coding regions have higher GC contents. c) TESTCODE….. Codon at third position tend to repeat itself… so by plotting the repetition pattern… coding and non-coding regions can be differentiated
- 7. Gene Prediction in Prokaryotes
- 8. Gene Prediction in Prokaryotes Non-conventional Method A Markov model describes the probability of the distribution of nucleotides in a DNA sequence, in which the conditional probability of a particular sequence position depends on k previous positions. Based on Markov model and Hidden Markov Models Zero Order….every position independent First Order….a position is dependent on previous position Second Order…a position depends on preceding two positions Based upon gene content and gene length…. a prokaryotic gene can be Typical (100 to 500 AA) or Atypical (shorter or longer ) So to explain both types….HMM are developed. Tools based upon MM, HMM…….GeneMark, Glimmer, FGENESB
- 9. Gene Prediction in Eukaryotes Characteristics of Genome - Large size genomes (10Mbp 670 Gbp) - Low gene density….almost 3% human genome is coding sequence - Very rich repetitive sequences between coding regions - Introns and Exons - ATG, ….also GTG, TTG - RNA processing…5’caping……Splicing……Polyadenylation GT-AG rule for intron exon prediction - Kozak sequence…..flanking ATG ….. - High CG dinucleotides near TSS………called CpG island - Possible stop codons.. Three - Poly-A signal - High frequency of hexamers in coding regions.
- 10. Ab Initio based -First objective is discrimination of Exons and Introns Use two major features a) Gene signals - Start and stop codons -Intron splice signals -Transcription factor binding sites -Polyadenylation (Poly-A) sites b) Gene content -Nucleotide composition -Pattern of sequence in coding and non-coding region -GC content etc. -Hexamer Frequencies Tools….GRAIL, MZEF, FGENES, HMMgene Strategies used in Gene Prediction Programs Homology based Based upon homology of Exon structure, Exon sequence and patterns of Exon-Intron Genomescan, EST2Genome Also Consensus Based GeneComber, DIGIT
Editor's Notes
- Promoters in prokaryotic organisms are two short DNA sequences located at the -10 (10bp 5' or upstream) and -35 positions from the transcription start site (TSS). Their equivalent to the eukaryotic TATA box, the Pribnow box (TATAAT) is located at the -10 position and is essential for transcription initiation. The -35 position, simply titled the -35 element, typically consists of the sequence TTGACA and this element controls the rate of transcription. Prokaryotic cells contain sigma factors which assist the RNA polymerase in binding to the promoter region. Each sigma factor recognizes different core promoter sequences.
- 1 gene per 100 Kb Nearly 250 As at the 3’ end