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Biological Motivation Gene Finding Anne R. Haake Rhys Price Jones.

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Presentation on theme: "Biological Motivation Gene Finding Anne R. Haake Rhys Price Jones."— Presentation transcript:

1 Biological Motivation Gene Finding Anne R. Haake Rhys Price Jones

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 by Biological Methods: Extract mRNA reverse transcribe cDNA Label cDNA Detecting by using cDNA probe Gene found DNA library

4 Gene Finding by Computational Methods Dependent on good experimental data to build reliable predictive models Various aspects of gene structure/function provide information used in gene finding programs

5 Figure 12.3

6 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.

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

8 CTAGCAGGGACCCCAGCGCCCGAGAGACCATGCAGAGGTCGCCT CTGGAAAAGGCCAGCGTTGTCTCCAAACTTTTTTTCAGGTGAGA AGGTGGCCAACCGAGCTTCGGAAAGACACGTGCCCACGAAAGAG GAGGGCGTGTGTATGGGTTGGGTTGGGGTAAAGGAATAAGCAGT TTTTAAAAAGATGCGCTATCATTCATTGTTTTGAAAGAAAATGT GGGTATTGTAGAATAAAACAGAAAGCATTAAGAAGAGATGGAAG AATGAACTGAAGCTGATTGAATAGAGAGCCACATCTACTTGCAA CTGAAAAGTTAGAATCTCAAGACTCAAGTACGCTACTATGCACT TGTTTTATTTCATTTTTCTAAGAAACTAAAAATACTTGTTAATA AGTACCTANGTATGGTTTATTGGTTTTCCCCCTTCATGCCTTGG ACACTTGATTGTCTTCTTGGCACATACAGGTGCCATGCCTGCAT ATAGTAAGTGCTCAGAAAACATTTCTTGACTGAATTCAGCCAAC AAAAATTTTGGGGTAGGTAGAAAATATATGCTTAAAGTATTTAT TGTTATGAGACTGGATATAT...

9 G CTAGCAGGGACCCCAGCGCCCGAGAGACCATGCAGAGGTCGCCT CTGGAAAAGGCCAGCGTTGTCTCCAAACTTTTTTTCAGGTGAGA AGGTGGCCAACCGAGCTTCGGAAAGACACGTGCCCACGAAAGAG GAGGGCGTGTGTATGGGTTGGGTTGGGGTAAAGGAATAAGCAGT TTTTAAAAAGATGCGCTATCATTCATTGTTTTGAAAGAAAATGT GGGTATTGTAGAATAAAACAGAAAGCATTAAGAAGAGATGGAAG AATGAACTGAAGCTGATTGAATAGAGAGCCACATCTACTTGCAA CTGAAAAGTTAGAATCTCAAGACTCAAGTACGCTACTATGCACT TGTTTTATTTCATTTTTCTAAGAAACTAAAAATACTTGTTAATA AGTACCTANGTATGGTTTATTGGTTTTCCCCCTTCATGCCTTGG ACACTTGATTGTCTTCTTGGCACATACAGGTGCCATGCCTGCAT ATAGTAAGTGCTCAGAAAACATTTCTTGACTGAATTCAGCCAAC AAAAATTTTGGGGTAGGTAGAAAATATATGCTTAAAGTATTTAT TGTTATGAGACTGGATATAT...

10 Types of Genes Protein coding –most genes RNA genes –rRNA –tRNA –snRNA (small nuclear RNA) –snoRNA (small nucleolar RNA)

11 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

12 Looking for Protein Coding Genes Look for ORF (begins with start codon, ends with stop codon, no internal stops!) –long (usually > 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

13 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

14 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

15 Prokaryotic Gene Expression PromoterCistron1Cistron2CistronNTerminator TranscriptionRNA Polymerase mRNA 5’3’ Translation Ribosome, tRNAs, Protein Factors 12N Polypeptides N C N C N C 123 Slide modified from: SD in polycistronic message

16 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

17 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 GTA ACT GAC TAG GTG AAT CGT AAC TGA CTA GGT GAA

18 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%

19 Codon Bias Genetic code degenerate –Equivalent triplet codons code for the same amino acid –http://www.pangloss.com/seidel/Protocols/codon.htmlhttp://www.pangloss.com/seidel/Protocols/codon.html 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

20 Codon Bias Gene Differences GAL4ADH1 GlyGGG0.210 GlyGGA0.170 GlyGGT GlyGGC Slide modified from:

21 Codon Bias Organism 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:

22 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)

23 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

24 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

25 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.

26 Bacterial Promoter -35 T 82 T 84 G 78 A 65 C 54 A 45 … (16-18 bp)… T 80 A 95 T 45 A 60 A 50 T 96 …(A,G) Not so simple: remember, these are consensus sequences

27 Termination Sequences 3’-U tail Stem/loop –Inverted repeat immediately preceding the runs of uracil Termination sequence


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