Biological Motivation Gene Finding

1. Biological MotivationGene Finding Rhys Price Jones Anne R. Haake

2. Gene Finding Why do it? • Find and annotate all the genes within the large volume of DNA sequence data • how many genes in an organism? homologies? • Gain understanding of problems in basic science • e.g. gene regulation-what are the mechanisms involved in transcription, splicing, etc? • Different emphasis in these goals has some effect on the design of computational approaches for gene finding.

3. Gene Finding • Dependent on good experimental data to build reliable predictive models • Various aspects of gene structure/function provide information used in gene finding programs

4. The Informatics View of Genes • Genes are character strings embedded in much larger strings called the genome • Genes are composed of ordered elements associated with the fundamental genetic processes including transcription, splicing, and translation.

5. Gene Finding • Cells recognize genes from DNA sequence • find genes via their bioprocesses • Not so easy for us..

6. CTAGCAGGGACCCCAGCGCCCGAGAGACCATGCAGAGGTCGCCTCTGGAAAAGGCCAGCGTTGTCTCCAAACTTTTTTTCAGGTGAGAAGGTGGCCAACCGAGCTTCGGAAAGACACGTGCCCACGAAAGAGGAGGGCGTGTGTATGGGTTGGGTTGGGGTAAAGGAATAAGCAGTTTTTAAAAAGATGCGCTATCATTCATTGTTTTGAAAGAAAATGTGGGTATTGTAGAATAAAACAGAAAGCATTAAGAAGAGATGGAAGAATGAACTGAAGCTGATTGAATAGAGAGCCACATCTACTTGCAACTGAAAAGTTAGAATCTCAAGACTCAAGTACGCTACTATGCACTTGTTTTATTTCATTTTTCTAAGAAACTAAAAATACTTGTTAATAAGTACCTANGTATGGTTTATTGGTTTTCCCCCTTCATGCCTTGGACACTTGATTGTCTTCTTGGCACATACAGGTGCCATGCCTGCATATAGTAAGTGCTCAGAAAACATTTCTTGACTGAATTCAGCCAACAAAAATTTTGGGGTAGGTAGAAAATATATGCTTAAAGTATTTATTGTTATGAGACTGGATATAT...CTAGCAGGGACCCCAGCGCCCGAGAGACCATGCAGAGGTCGCCTCTGGAAAAGGCCAGCGTTGTCTCCAAACTTTTTTTCAGGTGAGAAGGTGGCCAACCGAGCTTCGGAAAGACACGTGCCCACGAAAGAGGAGGGCGTGTGTATGGGTTGGGTTGGGGTAAAGGAATAAGCAGTTTTTAAAAAGATGCGCTATCATTCATTGTTTTGAAAGAAAATGTGGGTATTGTAGAATAAAACAGAAAGCATTAAGAAGAGATGGAAGAATGAACTGAAGCTGATTGAATAGAGAGCCACATCTACTTGCAACTGAAAAGTTAGAATCTCAAGACTCAAGTACGCTACTATGCACTTGTTTTATTTCATTTTTCTAAGAAACTAAAAATACTTGTTAATAAGTACCTANGTATGGTTTATTGGTTTTCCCCCTTCATGCCTTGGACACTTGATTGTCTTCTTGGCACATACAGGTGCCATGCCTGCATATAGTAAGTGCTCAGAAAACATTTCTTGACTGAATTCAGCCAACAAAAATTTTGGGGTAGGTAGAAAATATATGCTTAAAGTATTTATTGTTATGAGACTGGATATAT...

7. G CTAGCAGGGACCCCAGCGCCCGAGAGACCATGCAGAGGTCGCCTCTGGAAAAGGCCAGCGTTGTCTCCAAACTTTTTTTCAGGTGAGAAGGTGGCCAACCGAGCTTCGGAAAGACACGTGCCCACGAAAGAGGAGGGCGTGTGTATGGGTTGGGTTGGGGTAAAGGAATAAGCAGTTTTTAAAAAGATGCGCTATCATTCATTGTTTTGAAAGAAAATGTGGGTATTGTAGAATAAAACAGAAAGCATTAAGAAGAGATGGAAGAATGAACTGAAGCTGATTGAATAGAGAGCCACATCTACTTGCAACTGAAAAGTTAGAATCTCAAGACTCAAGTACGCTACTATGCACTTGTTTTATTTCATTTTTCTAAGAAACTAAAAATACTTGTTAATAAGTACCTANGTATGGTTTATTGGTTTTCCCCCTTCATGCCTTGGACACTTGATTGTCTTCTTGGCACATACAGGTGCCATGCCTGCATATAGTAAGTGCTCAGAAAACATTTCTTGACTGAATTCAGCCAACAAAAATTTTGGGGTAGGTAGAAAATATATGCTTAAAGTATTTATTGTTATGAGACTGGATATAT...

8. Types of Genes • Protein coding • most genes • RNA genes • rRNA • tRNA • snRNA (small nuclear RNA) • snoRNA (small nucleolar RNA)

9. 3 Major Categories of Information used in Gene Finding Programs • Signals/features = a sequence pattern with functional significance e.g. splice donor & acceptor sites, start and stop codons, promoter features such as TATA boxes, TF binding sites, CpG islands • Content/composition -statistical properties of coding vs. non-coding regions. • e.g. codon-bias; length of ORFs in prokaryotes;GC content • Similarity-compare DNA sequence to known sequences in database • Not only known proteins but also ESTs, cDNAs

10. Looking for Protein Coding Genes • Look for ORF (begins with start codon, ends with stop codon, no internal stops!) • long (usually > 60-100 aa) • If homologous to “known” protein more likely • Look for basal signals • Transcription, splicing, translation • Look for regulatory signals • Depends on organism • Prokaryotes vs Eukaryotes • Vertebrate vs fungi

11. Easier problem:Gene Finding in Bacterial Genomes Why? • Dense Genomes • Short intergenic regions • Uninterrupted ORFs • Conserved signals • Abundant comparative information • Complete Genomes available for many

12. What do Prokaryotic Genes look like? 5’ 3’ Open Reading Frame Promoter region (maybe) Ribosome binding site (maybe) Termination sequence (maybe) Start codon / Stop Codon

13. Ribosome, tRNAs, Protein Factors Translation Prokaryotic Gene Expression Promoter Cistron1 Cistron2 CistronN Terminator Transcription RNA Polymerase mRNA 5’ 3’ 1 2 N N N C N C C 1 2 3 Polypeptides Slide modified from: http://biology.uky.edu/520/Lecture/lect8/lect8Notes.ppt

14. Open Reading Frame (ORF) • Any stretch of DNA that potentially encodes a protein • The identification of an ORF is the first indication that a segment of DNA may be part of a functional gene

15. Open Reading Frames Each grouping of the nucleotides into consecutive triplets constitutes a reading frame. There are three different reading frames in the 5’->3’ direction and a further three in the reverse direction on the opposite strand. A sequence of triplets that contains no stop codon is an Open Reading Frame (ORF) A C G T A A C T G A C T A G G T G A A T CGT AAC TGA CTA GGT GAA GTA ACT GAC TAG GTG AAT

16. ORFs as gene candidates • An open reading frame that begins with a start codon (usually ATG, GTG or TTG, but this is species-dependent) • Most prokaryotic genes code for proteins that are 60 or more amino acids in length • The probability that a random sequence of nucleotides of length n has no stop codons is (61/64)n • When n is 50, there is a probability of 92% that the random sequence contains a stop codon • When n is 100, this probability exceeds 99%

17. Codon Bias • Genetic code degenerate • Equivalent triplet codons code for the same amino acid • Codon usage varies • organism to organism • gene to gene • Biological basis • Avoidance of codons similar to stop • Preference for codons that correspond to abundant tRNAs within the organism

18. Codon Bias Gene Differences GAL4 ADH1 Gly GGG 0.21 0 Gly GGA 0.17 0 Gly GGT 0.38 0.93 Gly GGC 0.24 0.07 Slide modified from: http://biology.uky.edu/520/Lecture/lect8/lect8Notes.ppt

19. Codon BiasOrganism differences • Yeast Genome: arg specified by AGA 48% of time (other five equivalent codons ~10% each) • Fruitfly Genome: arg specified by CGC 33% of time (other five ~13% each) • Complete set of codon usage biases can be found at: • http://www.kazusa.or.jp/codon/

20. GC content • GC relative to AT is a distinguishing factor of bacterial genomes • Varies dramatically across species • Serves as a means to identify bacterial species • For various biological reasons • Mutational bias of particular DNA polymerases • DNA repair mechanisms • horizontal gene transfer (transformation, transduction, conjugation)

21. GC Content • GC content may be different in recently acquired genes than elsewhere • This can lead to variations in the frequency of codon usage within coding regions • There may be significant differences in codon bias within different genes of a single bacterium’s genome

22. Ribosome Binding Sites • RBS is also known as a Shine-Dalgarno sequence (species-dependent) that should bind well with the 3’ end of 16S rRNA (part of the ribosome) • Usually found within 4-18 nucleotides of the start codon of a true gene

23. Shine-Dalgarno Sequence • Is a nucleotide sequence (consensus = AGGAGG) that is present in the 5'-untranslated region of prokaryotic mRNAs. • This sequence serves as a binding site for ribosomes and is thought to influence the reading frame. • If a subsequence aligning well with the Shine-Dalgarno sequence is found within 4-18 nucleotides of an ORF’s start codon, that improves the ORF’s candidacy.

24. Bacterial Promoter -35 T82T84G78A65C54A45… (16-18 bp)… T80A95T45A60A50T96…(A,G) -10 +1 Not so simple: remember, these are consensus sequences

25. Termination Sequences • 3’-U tail • Stem/loop • Inverted repeat immediately preceding the runs of uracil