1. Genes, Genomes, and Genomics
Bioinformatics in the Classroom
June, 2003

2. Craig Venter, Celera Inc.
Two. Again …
Francis Collins, HGP
Craig Venter, Celera Inc.

3. What’s in a chromosome?

4. Hierarchical vs. Whole Genome

5. The value of genome sequences lies in their annotation
Annotation – Characterizing genomic features using computational and experimental methods
Genes: Four levels of annotation
Gene Prediction – Where are genes?
What do they look like?
Domains – What do the proteins do?
Role – What pathway(s) involved in?

6. How many genes? Consortium: 35,000 genes? Celera: 30,000 genes?
Affymetrix: 60,000 human genes on GeneChips?
Incyte and HGS: over 120,000 genes?
GenBank: 49,000 unique gene coding sequences?
UniGene: > 89,000 clusters of unique ESTs?

7. Current consensus (in flux …)
15,000 known genes (similarity to previously isolated genes and expressed sequences from a large variety of different organisms)
17,000 predicted (GenScan, GeneFinder, GRAIL)
Based on and limited to previous knowledge

8. What are genes? - 1
Complete DNA segments responsible to make functional products
Products
Proteins
Functional RNA molecules
RNAi (interfering RNA)
rRNA (ribosomal RNA)
snRNA (small nuclear)
snoRNA (small nucleolar)
tRNA (transfer RNA)

9. What are genes? - 2 Definition vs. dynamic concept Consider
Prokaryotic vs. eukaryotic gene models
Introns/exons
Posttranscriptional modifications
Alternative splicing
Differential expression
Genes-in-genes
Genes-ad-genes
Posttranslational modifications
Multi-subunit proteins

10. Where do genes live? In genomes Example: human genome
Ca. 3,200,000,000 base pairs
25 chromosomes : 1-22, X, Y, mt
28,000-45,000 genes (current estimate)
128 nucleotides (RNA gene) – 2,800 kb (DMD)
Ca. 25% of genome are genes (introns, exons)
Ca. 1% of genome codes for amino acids (CDS)
30 kb gene length (average)
1.4 kb ORF length (average)
3 transcripts per gene (average)

11. List of 68 eukaryotes, 141 bacteria, and 17 archaea at
Sample genomes
Species
Size
Genes
Genes/Mb
H.sapiens
3,200Mb
35,000 11
D.melanogaster
137Mb
13.338 97
C.elegans
85.5Mb
18,266
214
A.thaliana
115Mb
25,800
224
S.cerevisiae
15Mb
6,144
410
E.coli
4.6Mb
4,300
934
 List of 68 eukaryotes, 141 bacteria, and 17 archaea at

12. How do we get to the genes?

13. Prokaryotic gene model: ORF-genes
“Small” genomes, high gene density
Haemophilus influenza genome 85% genic
Operons
One transcript, many genes
No introns.
One gene, one protein
Open reading frames
One ORF per gene
ORFs begin with start,
end with stop codon (def.)
TIGR:
NCBI:

14. And this?

15. Eukaryotic gene model: spliced genes
Posttranscriptional modification
5’-CAP, polyA tail, splicing
Open reading frames
Mature mRNA contains ORF
All internal exons contain open “read-through”
Pre-start and post-stop sequences are UTRs
Multiple translates
One gene – many proteins via alternative splicing

16. Expansions and Clarifications
ORFs
Start – triplets – stop
Prokaryotes: gene = ORF
Eukaryotes: spliced genes or ORF genes
Exons
Remain after introns have been removed
Flanking parts contain non-coding sequence (5’- and 3’-UTRs)

17. So much DNA – so “few” genes …

18. Genomic sequence features
Repeats (“Junk DNA”)
Transposable elements, simple repeats
RepeatMasker
Genes
Vary in density, length, structure
Identification depends on evidence and methods and may require concerted application of bioinformatics methods and lab research
Pseudo genes
Look-a-likes of genes, obstruct gene finding efforts.
Non-coding RNAs (ncRNA)
tRNA, rRNA, snRNA, snoRNA, miRNA
tRNASCAN-SE, COVE

19. Gene identification Homology-based gene prediction
Similarity Searches (e.g. BLAST, BLAT)
Genome Browsers
RNA evidence (ESTs)
Ab initio gene prediction
Gene prediction programs
Prokaryotes
ORF identification
Eukaryotes
Promoter prediction
PolyA-signal prediction
Splice site, start/stop-codon predictions

20. Gene prediction through comparative genomics
Highly similar (Conserved) regions between two genomes are useful or else they would have diverged
If genomes are too closely related all regions are similar, not just genes
If genomes are too far apart, analogous regions may be too dissimilar to be found

21. Genome Browsers Generic Genome Browser (CSHL)
NCBI Map Viewer
Ensembl Genome Browser
UCSC Genome Browser
genome.ucsc.edu/cgi-bin/hgGateway?org=human
Apollo Genome Browser

22. Gene discovery using ESTs
Expressed Sequence Tags (ESTs) represent sequences from expressed genes.
If region matches EST with high stringency then region is probably a gene or pseudo gene.
EST overlapping exon boundary gives an accurate prediction of exon boundary.

23. Ab initio gene prediction
Prokaryotes
ORF-Detectors
Eukaryotes
Position, extent & direction: through promoter and polyA-signal predictors
Structure: through splice site predictors
Exact location of coding sequences: through determination of relationships between potential start codons, splice sites, ORFs, and stop codons

24. Tools ORF detectors Promoter predictors PolyA signal predictors
NCBI:
Promoter predictors
CSHL:
BDGP: fruitfly.org/seq_tools/promoter.html
ICG: TATA-Box predictor
PolyA signal predictors
CSHL: argon.cshl.org/tabaska/polyadq_form.html
Splice site predictors
BDGP:
Start-/stop-codon identifiers
DNALC: Translator/ORF-Finder
BCM: Searchlauncher

25. How it works I – Motif identification
Exon-Intron Borders = Splice Sites
Exon Intron Exon 
~~gaggcatcag|GTttgtagac~~~~~~~~~~~tgtgtttcAG|tgcacccact~~
~~ccgccgctga|GTgagccgtg~~~~~~~~~~~tctattctAG|gacgcgcggg~~
~~tgtgaattag|GTaagaggtt~~~~~~~~~~~atatctccAG|atggagatca~~
~~ccatgaggag|GTgagtgcca~~~~~~~~~~~ttatttccAG|gtatgagacg~~
Splice site Splice site
Exon Intron Exon 
~~gaggcatcag|gtttgtagac~~~~~~~~~~~tgtgtttcag|tgcacccact~~
~~ccgccgctga|gtgagccgtg~~~~~~~~~~~tctattctag|gacgcgcggg~~
~~tgtgaattag|gtaagaggtt~~~~~~~~~~~atatctccag|atggagatca~~
~~ccatgaggag|gtgagtgcca~~~~~~~~~~~ttatttccag|gtatgagacg~~
Splice site Splice site
Motif Extraction Programs at

26. How it works II - Movies
Pribnow-Box Finder 0/1
Pribnow-Box Finder all

27. How it works III – The (ugly) truth

28. Gene prediction programs
Rule-based programs
Use explicit set of rules to make decisions.
Example: GeneFinder
Neural Network-based programs
Use data set to build rules.
Examples: Grail, GrailEXP
Hidden Markov Model-based programs
Use probabilities of states and transitions between these states to predict features.
Examples: Genscan, GenomeScan

29. Uberbacher and Mural PNAS (1991)

30. Burge, C.B. and S. Karlin, Finding the genes in
genomic DNA. Curr Opin Struct Biol, 1998.
8(3): p
Burge, C. and S. Karlin, Prediction of complete gene structures
in human genomic DNA. J Mol Biol, (1): p

31. Evaluating prediction programs
Sensitivity vs. Specificity
Sensitivity
How many genes were found out of all present?
Sn = TP/(TP+FN)
Specificity
How many predicted genes are indeed genes?
Sp = TP/(TP+FP)

32. Evaluation of Gene Prediction Algorithms
Sn = Sensitivity = TP/(TP+FN)
How many exons were found out of total present?
Sp = Specificity = TP/(TP+FP)
How many predicted exons were correct out of total exons predicted?

33. Gene prediction accuracies
Nucleotide level: 95%Sn, 90%Sp (Lows less than 50%)
Exon level: 75%Sn, 68%Sp (Lows less than 30%)
Gene Level: 40% Sn, 30%Sp (Lows less than 10%)
Programs that combine statistical evaluations with similarity searches most powerful.

34. Common difficulties
First and last exons difficult to annotate because they contain UTRs.
Smaller genes are not statistically significant so they are thrown out.
Algorithms are trained with sequences from known genes which biases them against genes about which nothing is known.
Masking repeats frequently removes potentially indicative chunks from the untranslated regions of genes that contain repetitive elements.

35. The annotation pipeline
Mask repeats using RepeatMasker.
Run sequence through several programs.
Take predicted genes and do similarity search against ESTs and genes from other organisms.
Do similarity search for non-coding sequences to find ncRNA.

36. Annotation nomenclature
Known Gene – Predicted gene matches the entire length of a known gene.
Putative Gene – Predicted gene contains region conserved with known gene. Also referred to as “like” or “similar to”.
Unknown Gene – Predicted gene matches a gene or EST of which the function is not known.
Hypothetical Gene – Predicted gene that does not contain significant similarity to any known gene or EST.