1. From Genomes to Genes
Rui Alves

2. How to make sense of genome sequences?
How do I know where genes are?
…atgattattggcggaatcggcggtgcaaggacacaaacaggactcagattcgaagaacgtacagacttacgaaagttgtttgaagaaattcc…

3. Predicting ORFs is easy, predicting genes is hard
An ORF is a sequence of nucleotides that goes from a start codon (ATG, GTG,…) to a stop codon (GTA)
Finding them is as easy as reading the DNA sequence
How do we know if an ORF is a gene?

4. There are several ways to predict genes
By homology

5. Homology predictions Sequence of known gene Homologue gene
…Sequenced … Genome…
Homologue gene

6. How are sequences aligned?
Substitution probability table A C - … 1
0.001
…UUACAUUUCCCGUCCGCUCU…
…GGGGUUAAUUUGCCCGUCCA…
…UUACAUUUCCCGUCCGCUCU…
…GGGGUUAAUUUGCCCGUCCA…
S2>S1 S1

7. Problems of homology predictions: The genetic code
NO HOMOLOGY!!
…UUAAUUUCCCGUCCG…
…CUUAUAAGUAGACCA…
Yet, the code is for the same peptide
…LISRP…

8. Solution for redundancy of genetic code:
Use synonymous substitution when doing the DNA alignment
The problem of doing this:
…UUAAUUUCCCGUCCG…
…UUAAUUUCCCGUCCA…
…UUAAUUUCCAGACCG… …
…CUUAUAAGUAGACCA…
Combinatorial Explosion!!!
Solutions?
Not many, efficient algorithms, more computer power, pacience

9. Homology predictions most effective for closely related organisms
Thus, homology-based gene predictions works best when the genome of a close organism has been fully sequenced and annotated!!!

10. There are other ways to predict if Orfs are genes
By homology
Ab initio methods
Signal Sensors
ATG sites
Promoter elements id
Regulatory elements id
Shine-Dalgarno sequences id (i.e. rybosome binding sites) …

11. Using initiation and termination codons to identify ORFs
ATG is the start codon
GTG, CTG, TTG are minor start codons
If termination codon too close to ATG then ORFs unlikely to be gene
atgaatgaatgctgccgaagatctctggcaccaaattttggagcggttgcag…
atgaatgaatgctgccgaagatctctggcaccaaattttggagcggtgacag…

12. Using Promoter sequences to identify ORFs
Many promoters have a known structure
Identifying Promoters close to initiation codons increases likelihood of ORF being gene
Lac promoter

13. Using response elements to identify ORFs
Regulatory binding sites (RBS) have a known structure
Identifying RBS close to initiation codons increases likelihood of ORF being gene

14. Using Rybosomal binding sequences to identify ORFs
Rybosomal binding sites (SDS) have a known structure
Identifying SDS close to initiation codons increases likelihood of ORF being gene
AGGAGG
Consensus Shine-Dalgarno sequence

15. There are several ways to predict genes
By homology
Ab initio methods
Signal Sensors
Promoter elements id
Regulatory elements id
Shine-Dalgarno sequences id (i.e. rybosome binding sites)
ATG sites …
Content Sensors
Codon usage
GC content
Position assymetry
CpG islands

16. Using codon bias to predict expressed ORFs
Average Codon usage Ile RF1
ATT
ATC
ATA
0.34
0.26
0.40
Frequency of synonymous codons in an organism are not uniform
Frequency of synonymous codons in coding sequences is different from that in non-coding sequences
This can be used to predict coding open reading frames
Average Codon Usage Ile
ATT
ATC
ATA
0.34
0.46
0.20
atgaatgcatgctgccgaagatctctggcaccaaattttggagcggttgcag…
Average Codon usage Ile RF2
ATT
ATC
ATA
0.40
0.20
Average Codon usage Ile RF3
ATT
ATC
ATA
0.32
0.42
0.25
The third reading frame is the most likely to be a gene

17. Using GC content to predict expressed ORFs
gtgattagctctgccgaagatctctggcaccaaattttggagcggttgcag…
Frame 1
Frame 2
Frame 3 11 9 5
The G+C content of the third position of codons in coding sequences is biased
Genes have a very high (low) G+C content on the third position of the codons in the reading frame. Frame 1 (3) more likely to be expressed
Not very usefull for eukaryotes

18. Using position assymetry to predict expressed ORFs
Av Gene A T C G
Position 1
0.20
0.22
0.40
Position 2
0.38
Position 3
0.30
0.24
Coding sequences have a characteristic distribution of nucleotides in each of the three positions of codons
gtgaatgtatgctctgccgaagatctctggcaccaaattttggagcggttgcag…
RF3 A T C G
Position 1
0.45
0.15
0.25
Position 2
0.20
0.18
0.30
0.32
Position 3
0.11
0.36
RF2 A T C G
Position 1
0.38
0.24
0.19
Position 2
Position 3
0.25
RF1 A T C G
Position 1
0.19
0.24
0.38
Position 2
Position 3
0.29

19. Using position assymetry to predict expressed ORFs
Reading Frame 1 the most likely because it has the highest similarity to the position assymetry of known genes.

20. CpG Islands are signals for transcription initiation
Near the promoter of known genes, the content of CG dinucleotides is higher than that away from initiation of transcription sites
Thus, ATG preceded by CpG island are more likely to be genes

21. Other assimetry measures of gene likelihood
Dinucleotide bias
Hexanucleotide bias …

22. Summary Genes can be predicted by Homology Content sensors
Signal sensors
If you need to annotate a genome, e.g. go to TIGR

23. How are eukaryotic genes different?
DNA
mRNA
RNA Pol
Ryb
Protein

24. How are eukaryotic genes different?
DNA
mRNA
RNA Pol
Ryb
Spliceosome
Protein
mRNA
Correctly Identifying Splicing sites is not a trivial task

25. How do we predict splicing sites?
By Homology
Ab initio
SS motifs
Codon usage
Exonic Splicing Enhancers
Intronic Splicing Enhancers
Exonic Splicing Silencers
Intronic Splicing Silencers

26. Homology Splice Site Prediction
Known
spliced
gene
Predicted
spliced
gene

27. Splice Site Motifs

28. Exonic Splicing Enhancers

29. Exonic Splicing Silencers
Genes & Development 18:

30. Interaction between SE and SI

31. Rules for Splicing 3’ end likely target for repression
Distance between SE and 3’ end < 100bp
Splicing efficiency a p(interaction SEC-3’ end)

32. Methods for splicing detection
Set of
know spliced
genes
Test set of
know spliced
genes
Training set of
know spliced
genes
GA, NN, HMM
Bayes,ME
GA, NN, HMM
Bayesian
Algorithm
Test set
Predictions

33. A Genetic Algorithm Method
Motif
DM1
… AMi … EM AM
p(i) IM
Shuffle lines and columns k times and each time calculate the probability of a given combination of motifs getting spliced
Select m best combinations and continue to evolve the algorithm until it predicts training set

34. A Neural Net Method Sequences Predicted Splicing
Corrected Weight Table for splice elements
Weight Table for splice elements
Hidden Nodes
Predicted Splicing

35. Summary Eukaryotic genes have exons
Biological rules combined with mathematical and statistical approaches can be used to predict the boundaries for the exons and to predict the splice variants

36. How to find what genes a string of DNA contains
Rui Alves

37. Simple steps Go to a known gene prediction server (or google for one)
Input sequence and wait for prediction
Get prediction(s), either as cDNA or as a tranlated protein sequence and do homology searches to identify them in a known database (e.g. NCBI or SWISSPROT)

38. Simple steps a)
Go to a known gene prediction server (or google for one)
Input sequence and wait for prediction
Get prediction(s), either as cDNA or as a translated protein sequence and do homology searches to identify them

39. Paper Presentation
The human genome (Science) vs. The human genome (Nature)
Nature : Pages 875 to 901
Science: Pages
Compare the differences in methods and results for the annotation
DO NOT SPEND TIME TALKING ABOUT THE SEQUENCING OR ASSEMBLY ITSELF
Do not go into the comparative genome analysis