1. Gene Structure and Identification
Genes and Genomes
ORFs and more
Consensus Sequences
Gene Finding

2. Cells recognize genes from DNA sequence.
GATC to Gene
Cells recognize genes from DNA sequence.
Can we??

3. Genes
Protein Coding
RNA genes
rRNA
tRNA
snRNA, snoRNA…

4. Protein Coding Genes ORF Regulatory signals long (usually >100 aa)
“known” proteins likely
Regulatory signals
Depend on organism
Prokaryotes vs Eukaryotes
Verterbrate vs fungi, eg.
Yeast, ~1% of genes have ORFs<100 aa

5. ??? Infer Gene Structure mRNA Promoter Splicing Strength Stability
ORF=protein
Promoter
Strength
Regulation

6. Genomes Gene Content E. coli 4000 genes X 1 kbp/gene=4 Mbp
Genome=4 Mbp!

7. 2200 Mbp=??? Genomes Gene Content Introns=600 Mbp? Human
100,000 genes X 2 kbp=200 Mbp
Introns=600 Mbp?
2200 Mbp=???

8. Prokaryotic Gene Expression
Promoter
Cistron1
Cistron2
CistronN
Terminator
Transcription
RNA Polymerase
mRNA 5’ 3’ 1 2 N
Translation
Ribosome, tRNAs,
Protein Factors N N C N C C 1 2 3
Polypeptides

9. ORF Characteristics No STOPS! Codon bias
Biased nucleotide distribution
periodicity of 3
dicodon frequency

10. ORFs
P(ORF)=(61/64)n
P(20)=(61/64)20=.38
P(100)=0.008
P(200)=10-4

11. ORF finding tools Translate/Map Frames: graphical 6 frames Testcode
UNIX graphics problem (see WWW)
Testcode
CodonPreference
WWW tools
ORF Finder (NCBI)
BCM Search Launcher...

12. Codon Bias Genetic code degenerate Codon usage varies
organism to organism
gene to gene
high bias correlates with high level expression
bias correlates with tRNA isoacceptors
Change bias or tRNAs, change expression

13. Codon Bias

14. Codon Bias Gene Differences

15. Codon Bias Organism Differences
Micrococcus luteus
Pneumocystis carinii

16. Codon Bias Organism DifferencesPc Ml

17. Nucleotide Bias Useful: DNA sequence Errors?
Coding DNA vs non-Coding DNA
often G+C content higher than bulk
Empirical statistics (Fickett’s TESTCODE)
Useful:
ORF matches “typical”
organism, bias
ORF obscured by STOP codons
DNA sequence
Errors?

18. Complex Genome DNA ~10% highly repetitive (300 Mbp)
NOT GENES
~25% moderate repetitive (750 Mbp)
Some genes
~25% exons and introns (800 Mbp)
40%=?
Regulatory regions
Intergenic regions

19. Alternate sigma factors
Bacterial Promoter
-35
T82T84G78A65C54A45…
(16-18 bp)…
T80A95T45A60A50T96…(A,G)
Alternate sigma factors
CCCTTGAA….CCCGATNT

20. Terminators Stem/loop 3’-U tail Rho-independent C-rich G-poor
structural only
3’-U tail
Rho-independent
C-rich
G-poor
“loose” consensus
Rho-dependent

21. Translation Ribosome Binding Site, Shine-Dalgarno Site
nnGGAGGnnnnnATG…
typical E. coli
nnaaAGGnnnnnATG

22. Operon Structure
Promoter?

23. GCG Tools Frames Testcode Findpatterns (bacterial promoters) Setplot
Options
Frames –all myseq.seq output.png
FTP output.png
View output.png
Testcode
Findpatterns (bacterial promoters)

24. .ps

25. Eukaryotic Gene Expression
Enhancer
Promoter
Transcribed Region
Terminator
Transcription
RNA Polymerase II
Primary transcript 5’
Intron1 3’
Exon1
Exon2
Cap
Splice
Cleave/Polyadenylate
Translation
7mG An N C
Transport
Polypeptide
7mG An

26. Eukaryotic Gene Complexity
Yeast
introns rare
promoters adjacent
genome dense

27. Eukaryotes, cont’d Fungi “large” Eukaryotes
introns common, short relative to exons
promoter/enhancer
genome dense
“large” Eukaryotes
introns common, LONGER than exons
Promoter/enhancer
genome sparse

28. Intron Prevalence

29. Intron Size

30. Exon Size

31. Yeast
ORFS=genes!
Small ORFS
(RNA genes)
Regulatory Sequnces

32. Fungi Sew together exons ORF regions consensus sequences
domain/polypeptide matches

33. Exon/Intron Structure
CCACATTgtn(30-10,000)an(5-20)agCAGAA
...CCACATTCAGAA...
...ProHisSerGlu...

34. Alternative Splice CCACATTgtn(30-10,000)an(5-20)agcagAA
...CCACATTAA...
...ProHisSTOP

35. Position Weight Matrices
Consensus Sequences
Promoter sites
Intron/Exon
Transcription Termination/PolyA
Translation initation
Position Weight Matrices

36. Finding Functional Sequences
Known Consensus Sequences
Consensus Sequence Generation
Functional Tests

37. Consensus Inference ProfileScan Position Weight Matrices
Sequence Logos
Hidden Markov Models
ProfileScan

38. Translation Initiation SitesC G T
C A T G G

39. Functional Assay Conservation Correlated Positions CCATGG 100 CCCTGG 0
CCTTGG 5
CCATAG 0
CTATGG 90
CCATGA 85
Conservation
Correlated
Positions

40. Splicing Consensus Alternate Splicing!?? A64G73GTA62A68G84T63…
Y80NY80Y87R75AY95…C65AGNN Vert
GTRNGT(N){ }
CTRAC(N){5-15}YAG Fungi
Alternate Splicing!??

41. Linguistic Approach Looks like a duck... Non-repetitive DNA!! Long ORF
similar to known protein
ORF extended by “reasonable” splices
ORF begins with “good” ATG
Promoter/terminator flanks
Looks like a duck...

42. Protein Database Matches
Great for the “known”
What about the unknown???

43. Codon Bias-useful? High bias = high confidence
Low bias = low confidence
Sensitive to indel

44. Tools-GCG
Most USEFUL
Frames
Testcode
FindPatterns
Map/Translate

45. Tools-WWW HMM Probabilities GRAIL II: integrated gene parsing GenLang
GENIE
HMMGene (lock ESTs, etc.)
GENESCAN
GENEMARK
HMM
Probabilities

46. Hidden Markov Models Probabilistic Models
Applicable to linear sequences
P(all states)=1, infer probabilities of all states from observed (hidden states unobserved)
Work best when local correlations unimportant
Genefinding, phylogeny, secondary structure, genetic mapping
Work best with “Training Set”
Quantitative probabilities

47. Accuracy Assessment AC = ((TP/(TP+FN)) + (TP/(TP+FP))
PP=predicted coding
PN=predicted non-coding
AP=“real” positive
AN=“’real” negatives
TP=number correct positive
TN=number correct negative
FP=number false positive
FN=number false negative
Sn=TP/AP
Sp=TP/PP
AC = ((TP/(TP+FN)) + (TP/(TP+FP))
+ (TN/(TN+FP)) + (TN/(TN+FN))) / 2 - 1

48. Accuracy Levels
DNA Sequence Error Rate!??

49. NEXT Regulatory Sequences Real examples Known Consensus Sequences
Consensus Sequence Generation
Functional (Lab) Data
Real examples

50. Gene Regulatory Sequences
Functional sites
Consensus
Experimental tests

51. Transcript initiation
Regulatory Sites
Transcript initiation
mRNA processing
Translation sites

52. Transcript Initiation
Basal Promoters
Enhancers/Silencers/Regulatory Sites
Boundary elements?
Transcription Initation
Prokaryotes vs Eukaryotes
Organism-to-Organism

53. mRNA processing Exon/Intron Polyadenylation/Cleavage Stability
Alternate splicing
Polyadenylation/Cleavage
Stability

54. Translation Initation site (Frameshifting)
Translational regulatory elements
upstream ORFs
translational enhancers

55. Infer from expression data?
Regulatory Factors
lacI, trpR, CAP, araC….
GAL4, NDT80…
Known from experiment
Infer from genome?
Infer from expression data?

56. EUKARYOTES More complex signals More genes More dispersed signals
Combinatoric regulation common

57. Basal Promoter Analysis
Myers and Maniatis, Genes VI, 831
ATATAA -30 TBP
GGCCAATC -75 CTF/NF1
GCCACACCC -90 SP1 +1
TATA
CAAT GC

58. Enhancer Elements False +, False - Octamer OCT1, OCT2 B NF B ATF ATF
AP1… AP1
……..
False +,
False -

59. Poly A sites
Metazoans
AATAAA
Yeast-different

60. Translation Sites Initiate at 5’-ATG (Frameshifting)
upstream ORF…regulatory
(Frameshifting)
Translation enhancers….

61. Consensus Sequence Databases
WWW-based
TFD (transcription factor database)
BCM Search launcher

62. Practical Gene Finding
Use ALL tools
Comparative
BLASTN, BLASTX
Predictive: Stitch together a consensus
HMM, GRAIL…
Frames, Testcode
Findpatterns (and WWW pattern searches)
cDNA OR protein OR genetic evidence
Most Genefinding starts with mRNA—that’s not where the cell actually starts!

63. DATABASE SEARCH www.ncbi.nlm.nih.gov BLASTN BLASTX/TBLASTX
DNA:DNA comparison (ALWAYS!)
Not sensitive (DNA conservation low)
BLASTX/TBLASTX
6 frame ORFS:polypeptide database
6 frames vs. 6 frames of a DNA database

64. FRAMES-aldolase gene

65. If aldolase is so tough, how do you really do it?
Combine DNA sequence
with other data!

66. Infer Promoter, Enhancer
Genome-cDNA P
Infer Promoter, Enhancer
Test in cis
DNA sequencing
Align (GAP)
cDNA