Lecture bioinformatics Part2.next generation

Genome

1. Genes, Genomes, and
Genomics
Bioinformatics in the Classroom
plagiarized from:
http://www.dnalc.org/bioinformatics/presentations
/hhmi_2003/2003_3.ppt
June, 2003

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

3. 3
What’s in a chromosome?

4. 4
Hierarchical vs.Whole Genome

5. 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. 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. 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. 8
How to we get from here …

9. 9
to here,

10. 10
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)
What are genes? -1

11. 11
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

12. 12
Prokaryotic gene model: ORF-genes
“Small” genomes, high gene density
Haemophilus influenzagenome 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: http://www.tigr.org/tigr-scripts/CMR2/CMRGenomes.spl
NCBI: http://www.ncbi.nlm.nih.gov/PMGifs/Genomes/micr.html

13. 13
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 viaalternative splicing

14. 14
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)

15. 15
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)

16. 16
Sample genomes
Species Size GenesGenes/Mb
H.sapiens 3,200Mb35,000 11
D.melanogaster137Mb13.338 97
C.elegans 85.5Mb18,266 214
A.thaliana 115Mb25,800 224
S.cerevisiae15Mb 6,144 410
E.coli 4.6Mb4,300 934
List of 68 eukaryotes, 141 bacteria, and 17 archaea at
http://www.ncbi.nlm.nih.gov/PMGifs/Genomes/links2a.html

17. 17
So much DNA –so “few” genes …s
T

Genic
Intergenic
T C

18. 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. 19
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
Gene identification

20. 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. 21
Genome Browsers
Generic Genome Browser (CSHL)
www.wormbase.org/db/seq/gbrowse
NCBI Map Viewer
www.ncbi.nlm.nih.gov/mapview/
Ensembl Genome Browser
www.ensembl.org/
Apollo Genome Browser
www.bdgp.org/annot/apollo/
UCSC Genome Browser
genome.ucsc.edu/cgi-bin/hgGateway?org=human

22. 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. 23
Ab initiogene 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. 24
Tools
ORF detectors
NCBI: http://www.ncbi.nih.gov/gorf/gorf.html
Promoter predictors
CSHL: http://rulai.cshl.org/software/index1.htm
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: http://www.fruitfly.org/seq_tools/splice.html
Start-/stop-codon identifiers
DNALC: Translator/ORF-Finder
BCM: Searchlauncher

25. 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~~~~~~~~~~~tgtgtttc AG|tgcacccact~~
~~ccgccgctga|GTgagccgtg~~~~~~~~~~~tctattct AG|gacgcgcggg~~
~~tgtgaattag|GTaagaggtt~~~~~~~~~~~atatctcc AG|atggagatca~~
~~ccatgaggag|GTgagtgcca~~~~~~~~~~~ttatttcc AG|gtatgagacg~~
Splice site Splice site
Motif Extraction Programs at http://www-btls.jst.go.jp/

26. 26
How it works II -Movies
Pribnow-Box Finder0/1
Pribnow-Box Finderall

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

28. 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. 29
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)

30. 30
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.

31. 31
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.

32. 32
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.

33. 33
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.