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 Figure 12.3 In prokaryotes, these processes are coupled
In Eukaryotes, these processes are physically separated and there are more steps!
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. CTAGCAGGGACCCCAGCGCCCGAGAGACCATGCAGAGGTCGCCTCTGGAAAAGGCCAGCGTTGTCTCCAAACTTTTTTTCAGGTGAGAAGGTGGCCAACCGAGCTTCGGAAAGACACGTGCCCACGAAAGAGGAGGGCGTGTGTATGGGTTGGGTTGGGGTAAAGGAATAAGCAGTTTTTAAAAAGATGCGCTATCATTCATTGTTTTGAAAGAAAATGTGGGTATTGTAGAATAAAACAGAAAGCATTAAGAAGAGATGGAAGAATGAACTGAAGCTGATTGAATAGAGAGCCACATCTACTTGCAACTGAAAAGTTAGAATCTCAAGACTCAAGTACGCTACTATGCACTTGTTTTATTTCATTTTTCTAAGAAACTAAAAATACTTGTTAATAAGTACCTANGTATGGTTTATTGGTTTTCCCCCTTCATGCCTTGGACACTTGATTGTCTTCTTGGCACATACAGGTGCCATGCCTGCATATAGTAAGTGCTCAGAAAACATTTCTTGACTGAATTCAGCCAACAAAAATTTTGGGGTAGGTAGAAAATATATGCTTAAAGTATTTATTGTTATGAGACTGGATATAT...
Even when entire genome is sequenced much work remains to make sense of the sequence
May appear simple but is not
For example, where do genes begin? Where do they end? how do we find individual changes in sequence that are meaningful?
Some aren’t: because not all sequences contain coding information for amino acids and proteins;
Even in coding sequences some differences are tolerated. Is like finding a needle in a haystack
Example here of gene, that when mutated, is responsible for cystic fibrosis

9. G
CTAGCAGGGACCCCAGCGCCCGAGAGACCATGCAGAGGTCGCCTCTGGAAAAGGCCAGCGTTGTCTCCAAACTTTTTTTCAGGTGAGAAGGTGGCCAACCGAGCTTCGGAAAGACACGTGCCCACGAAAGAGGAGGGCGTGTGTATGGGTTGGGTTGGGGTAAAGGAATAAGCAGTTTTTAAAAAGATGCGCTATCATTCATTGTTTTGAAAGAAAATGTGGGTATTGTAGAATAAAACAGAAAGCATTAAGAAGAGATGGAAGAATGAACTGAAGCTGATTGAATAGAGAGCCACATCTACTTGCAACTGAAAAGTTAGAATCTCAAGACTCAAGTACGCTACTATGCACTTGTTTTATTTCATTTTTCTAAGAAACTAAAAATACTTGTTAATAAGTACCTANGTATGGTTTATTGGTTTTCCCCCTTCATGCCTTGGACACTTGATTGTCTTCTTGGCACATACAGGTGCCATGCCTGCATATAGTAAGTGCTCAGAAAACATTTCTTGACTGAATTCAGCCAACAAAAATTTTGGGGTAGGTAGAAAATATATGCTTAAAGTATTTATTGTTATGAGACTGGATATAT...

10. Types of Genes Protein coding RNA genes most genes rRNA tRNA
snRNA (small nuclear RNA)
snoRNA (small nucleolar RNA)
snRNAs: small nuclear RNAs; usually associated with proteins and are then known as SNRNPs or snurps
Ex: those involved with splicing
snoRNAs: small nucleolar RNAs: involved in processing of rRNAs; perhaps in ribosome assembly
PolI: most rRNAs; snoRNAs
PolII: mRNAs, snRNAs
PolIII: tRNAs, 5srRNA

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
Similarity: -involves translation in all 6 possible reading frames. Need to discuss repetitive sequences & how to handle them. Can also use database information to aid in the prediction –not only known proteins but also ESTs, cDNAs (explain these).

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
Yeast, ~1% of genes have ORFs<100 aa

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
Promoter
Cistron1
Cistron2
CistronN
Terminator
Transcription
RNA Polymerase
mRNA 5’ 3’ 1 2 N
Translation
Ribosome, tRNAs,
Protein Factors
SD in polycistronic message N N C N C C 1 2 3
Polypeptides
Slide modified from:

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 A C G T A A C T G A C T A G G T G A A T
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

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 Codon usage varies Biological basis
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

20. Codon Bias Gene Differences
GAL4 ADH1
Gly GGG
Gly GGA
Gly GGT
Gly GGC
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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. Not so simple: remember, these are consensus sequences
Bacterial Promoter
-35
T82T84G78A65C54A45…
(16-18 bp)…
T80A95T45A60A50T96…(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