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Comparative analysis of eukaryotic genes
Mar Albà
http://genomics.imim.es/evolgenomeBarcelona Biomedical Research Park
Genome Projects
GOLD: Genomes Online Database (www.genomesonline.org)
Genome Projects
GOLD: Genomes Online Database (www.genomesonline.org)
Genome Projects
GOLD: Genomes Online Database (www.genomesonline.org)
Genome Browsers
-NCBI Map Viewerhttp://www.ncbi.nlm.nih.gov/mapview/
-Ensemblhttp://www.ensembl.org
-UCSC Genome Browserhttp://genome.cse.ucsc.edu
The three databases use the same genome assembly, which is generated by NCBI.
Ensembl
Ensembl
-genomic regions-alignments with synthenic sequences
-genes- Homologs, SNPs
- transcripts- EMBL mRNAS, ESTs, Expression
-proteins-Gene Ontology (function), protein domains, diseaseassociations
Ensembl - Biomart
- retrieval of information on gene datasets
Gene comparative sequence analysis
Genome and transcriptome projects have generated a vast amount of information on protein-coding and non-coding gene sequences.
Identification of conserved sequences in different genes can help us understand gene evolution and identify functional regions.
species 1
species 2x N genes(orthologs)
...
promoter
coding
species m
Non-coding sequences in vertebrate genomes
-only 1.2% of the human genome codes for proteins
-but 5% exhibits high sequence conservation levels, compatible with negative selection (MGSC, 2002)
-non-coding- Transcription regulatory regions- Introns- Non-protein coding exons/genes (miRNAs, etc.)- Repetitive elements (Alus, etc.)- Ultra-conserved elements
Gene transcription regulatory sequences
Maston et al., 2006Annu. Rev. Genomics Hum. Genet. 7: 29-59
Frequently-found metazoan motifs in the core promoter
Maston et al., 2006
Wray et al. (2003), Mol. Biol. Evol. 20(9):1377-1419.
Eukaryotic promoter diversity
High evolvability of regulatory sequences
-most of the changes in regulatory networks are likely to occur in cis; changes in trans (transcription factors) may often have too strong effects.
-one single mutation may lead to the acquisition of a newDNA-factor interaction (rapid turnover)
-the expression in one tissue may evolve independently of expression in another tissue (promoter modular organization)
Wray et al. (2003) The Evolution of Transcriptional Regulation in Eukaryotes. Mol. Biol. Evol. 20(9):1377-1419.
Transcription factor binding sites (TFBS) are short and imprecise
-short sequence motifs (6-12 bp)
- some positions of the motif are variable
- sometimes different transcription factors can recognize the same sequence motif
TATAAA TATAGA TATAAATATAAA GATAAATATAAATATAAATATAAT ***
TATA box
Transcription factor binding sites (TFBS)
Weight matrices
TATAAA TATAGA TATAAATATAAA GATAAATATAAATATAAATATAAT ***
1 2 3 4 5 6 - - - - - - - - - - - - A 0 8 0 8 7 7 C 0 0 0 0 0 0 G 1 0 0 0 1 0 T 7 0 8 0 0 1
-> can be used to search for putative motifs in sequences
TRANSFAChttp://transfac.gbf.de/TRANSFAC/
http://www.biobase.de
TRRDhttp://www.bionet.nsc.ru/trrd/
Placehttp://www.dna.affrc.go.jp/htdocs/PLACE/
ooTFD / rTFDhttp://www.ifti.org/cgi-bin/ifti/ootfd.pl
SCPDhttp://cgsigma.cshl.org/jian/
RegulonDBhttp://regulondb.ccg.unam.mx/
Transcription factor binding site databases
TFBS prediction using weight matrices
PROMO
Farré, D., et al. (2003). Nucleic Acids Research 31: 1739-1748.
http://promo.lsi.upc.edu
High false positive rate in TFBS prediction
Test Sequences: 200 vertebrate promoter sequences 607 experimentally-verified sites
Blanco, E., et al.. (2006). Nucleic Acids Research 34: D63-D67.
Predictions: Transfac v.6.4
SENSITIVITY: 46%
SPECIFICITY: 2% Very low!
Comparative approaches are necessary
- orthologous sequences : phylogenetic footprinting
- co-expressed genes : shared regulatory motifs
Select those motifs or regions that are shared by:
Boffelli D, Nobrega MA, Rubin EM. (2004) Nat Rev Genet. 5:456-65
Phylogenetic footprinting
Highly conserved enhancer in gene DACH1
Phylogenetic footprinting
Proximal promoter
pre-initiationcomplex
Motif positional bias
Signal Search Analysis Server (SIB)
Why some motifs should show positional bias?
- promoter structure
- protein-protein interaction positional constraints
Predicted element
Reference element (known)
TFBS 1
proximal promoter
TSSPICACTTFBS 1 TFB 2
regulatory module
TF1 TF2
PEAKS: identification of motif positional bias
functionally-related sequences(ex. co-expressed)
random
Predicted elementReference element (known)
TSSTFBS
over-representation
1
1
2
3
seq1
seq2
seq3
seq4
PEAKS
11
2
Step 1. Construct motif frequency profile
profile
sliding window
Predicted elementReference element (known)
PEAKSStep 1. Construct motif frequency profile
308 housekeeping genesTransfac v.6.4 matrix library
TSS
PEAKSStep 2. Measure significance of peaks
Score (max peak) = Sa x Sb x Sc
Sa = max peak / num motifSb = max peak / num seqSc = max peak / average num motifs
maximum peakFor each matrix:
CAAT-box+675 -325
average signal
difference
PEAKSStep 2. Measure significance of peaks
- determine random expectation score cut-off for different levels of significance using 1000 random datasets
- define significant signal range:
cut-off 0.005
max peak
CAAT-box
aver signal
PEAKS
Step 3. Build “promoter type”
52 genes regulated by NFkB, p < 0.5%
TATASp1
NFkB
BACH1
PEAKS serverhttp://genomics.imim.es/peaks/
Bellora, Farré and Albà (2007). Bioinformatics 23, 243-4.
308 housekeeping genes52 NFkB regulated genes
TATA
CAATGC-box YYTATANFkB
GC-box
BACH1
PEAKS resultshuman promoter sequences
TRANSFAC vertebrate matrices
PEAKS resultspromoters from yeast genes, amino acid metabolism (86 genes)
- 54 yeast weight matrices tested
- significant regions detected by the method show significant enrichment in experimentally-validated sites
Measuring promoter sequence divergence
promoter
species 1
species 2
promoter
species 1
species 2
Divergence (Non-aligned promoter fraction or dSM)
0.8
0.4
Castillo-Davis et al., 2004
1. highly divergent -> less constraints
2. highly conserved -> more constraints
0-0.1 0.1-0.2
0.2-0.3
0.3-0.4
0.4-0.5
0.5-0.6
0.6-0.7
0.7-0.8
0.8-0.9
0.9-1 1
Variability in promoter sequence divergence
8385 human-mouse orthologues2 Kb from transcription start site
Average divergence = 70%
Regulatory genes contain more conserved promoters than structural/metabolic genes
Functional classes enriched in high score promoter alignments
Lee et al. (2006). BMC Genomics 6: 188
- consistent with results by Iwama and Gojobori (2004)
Structural/metabolic genes contain less highly conservedpromoters than regulatory genes
Functional classes enriched in low score promoter alignments
Lee et al. (2006). BMC Genomics 6: 188
Comparison neurogenesis versus ribosomal
neurogenesisribosomal
Lee et al. (2006). BMC Genomics 6: 188
Is expression breadth related to promoter sequence divergence?
0
200
400
600
800
1000
1200
01-05 06-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55
Expression breadth (number of tissues)
Number of genes
Expression data from Zhang et al. (2004)
tissue-specific
intermediate
housekeeping
orthologues human-mouse
promoter
species 1
species 2
Measure sequence divergence
-tissue-specific-intermediate-housekeeping
Divergence = non-aligned promoter fraction
2 Kb
l=16 N dSM Ka Ks Ka/KsZhangdataset
3983
01-10 1006 0.688 0.108 0.764 0.14811-50 1931 0.702 0.080 0.746 0.11451-55 1046 0.734 0.050 0.678 0.078
Relationship between promoter divergence and expression breadth
number of tissues
Coding sequence evolutionary rate
Promoter divergence
but..
housekeeping
tissue-specificintermediate
promoter divergence
coding sequence divergence
0
10
20
30
40
50
60
70
-2000-1900-1800-1700-1600-1500-1400-1300-1200-1100-1000-900-800-700-600-500-400-300-200-100
Relationship between promoter divergence and expression breadth
- divergence measured in 100 nt bins
housekeeping
non-housekeeping
TSS
% conservation
Promoter divergence and gene function
highly divergent promoter
RNA bindingligase activityhydrolase activitycatalytic activity
highly conserved promoter
receptor bindingsignal transducer activityreceptor activitystructural molecule activitytranscription regulator activitytranscription factor activityDNA binding
GO class > 50 genes, p-value < 0.01
0,5
0,6
0,7
0,8
organdevelopment
transcriptionfactor
receptor proteinmetabolism
housekeeping
non-housekeeping
Promoter divergence and gene function
divergence
Summary
- the prediction of transcription factor binding sites is very noisy, we need to use comparative genomics
- some motifs show positional bias, this property canhelp us understand the structure of promoters and improve motif predictions
-promoter sequence conservation is related to gene function and to gene expression breadth. the fact that housekeeping genes contain less conserved promoters may obey to a more simple gene expression regulation
NicolasBellora
DomènecFarré
LorisMularoni
MacarenaToll
The teamEvolutionary Genomics GroupUniversitat Pompeu Fabra, Barcelonahttp://genomics.imim.es/evolgenome
MedyaShikhagaie