Transcript Promoter prediction in eukaryotes

Grupo 5
branchpoint site
5’site
exon 1
intron 1
AG/GT
3’site
exon 2
CAG/NT
intron 2
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Pol II RNA promoter elements
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Cap and CCAAT region
GC and TATA region
Kozak sequence (Ribosome binding site-RBS)
Splice donor, acceptor and lariat signals
Termination signal
Polyadenylation signal
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RNA Polymerase I – rRNA
RNA Polymerase II – mRNA
RNA Polymerase III – tRNAs, srRNAs
 RNA Pol II
Exon Intron Exon
GC box
~200 bp
CCAAT box
~100 bp
TATA box
~30 bp
Gene
Transcription
start site (TSS)
TSS
activ ator
RNAP II
70K
GTFs
1
5’
st
SR
exon
U1
GU
3’
repressor
regulatory promoter
region
core promoter
region (~100bp)
 TFs play a significant role in differentiation in a number of cell types
 The fact that ~ 5% of the genes are predicted to encode transcription factors underscores the
importance of transcriptional regulation in gene expression (Tupler et al. 2001 Nature. 409:832833)
 The combinatorial nature of transcriptional regulation and practically unlimited number of cellular
conditions significantly complicate the experimental identification of TF binding sites on a genome
scale
 Understanding the transcriptional regulation is a major challenge
 Computational approaches to identify potential regulatory elements and modules, and derive new,
biologically relevant and testable hypothesis
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Cap Region/Signal
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TATA box (~ 25 bp upstream)
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TATAAAnGCCC
CCAAT box (~100 bp upstream)
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nCAGTnG
TAGCCAATG
GC box (~200 bp upstream)
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A T A G G C G nGA
TATA box is found in ~70% of promoters
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Content-based Methods
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Site-based Methods
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donor sites, acceptor sites, promoter sites, start/stop
codons, polyA signals, lengths
Comparative Methods
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GC content, hexamer repeats, composition statistics,
codon frequencies
sequence homology, EST searches
Combined Methods
• Methods
– Neural nw trained on TATA and Inr Sites allowing a variable spacing
between sites. NN-GA approach to identify conserved patterns in RNA PolII
promoters and conserved spacing among them (PROMOTER2.0).
– TATA box recognition using weight matrix and density analysis of TF sites.
– Usage of linear (TSSD and TSSW) /quadratic (CorePromoter) discriminant
function. The function is based on:
• TATA box score
• Base-pair frequencies around TSS (triplet)
• Frequencies in consecutive 100-bp upstream regions
• TF binding site prediction
– Searches of weight matrices for different organism against a test sequence
(TFSearch/ TESS). MatInspector and ConInspector allows user-provided
limits on type of weight matrix, generation of new matrices etc.
– Testing for presence of clustered groups (or modules) of TF binding sites
which are characteristics of a given pattern of gene regulation.
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TRANSFAC is a database on eukaryotic cis-acting regulatory DNA elements and trans-acting
factors. It covers the whole range from yeast to human.
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Biological Databases/Biologische Datenbanken GmbH
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In release 4.0, it contains 8415 entries, 4504 of them referring to sites within 1078 eukaryotic genes,
the species of which ranging from yeast to human. Additionally, this table comprises 3494 artificial
sequences which resulted from mutagenesis studies, in vitro selection Procedures starting from
random oligonucleotide mixtures or from specific theoretical considerations. And finally, there are 417
entries with consensus binding sequences given in the IUPAC code.
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http://www.gene-regulation.com/
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MatInspector
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FastM
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PatSearch
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FunSiteP
Search for potential transcription factor binding sites in your own
sequences with the matrix search program MatInspector using the TRANSFAC 4.0 matrices.
A program for the generation of models for regulatory regions in DNA
sequences. FastM using the TRANSFAC 3.4 matrices.
Search for potential transcription factor binding sites in your own sequences with the
pattern search program using TRANSFAC 3.5 TRRD 3.5 sites.
Run interactively FunSiteP. Recognition and classification of eukaryotic promoters by
searching transcription factor binding sites using a collection of Transcription factor consensi.
TESS Transcription Element Search System
Computational Biology and Informatics Laboratory, School of Medicine, University of
Pennsylvania, 1997
http://www.cbil.upenn.edu/cgi-bin/tess/tess33?WELCOME
Eukaryotic Promoter Database Swiss Institute for
Experimental Cancer Research
•The Eukaryotic Promoter Database is an annotated non-redundant collection of eukaryotic
POL II promoters, for which the transcription start site has been determined experimentally.
•The annotation part of an entry includes description of the initiation site mapping data,
cross-references to other databases, and bibliographic references.
•EPD is structured in a way that facilitates dynamic extraction of biologically meaningful
promoter subsets for comparative sequence analysis.
•EPDEX is a complementary database which allows users to view available gene expression
data for human EPD promoters.
EPDEX is also accessible from the ISREC-TRADAT database entry server.
http://www.epd.isb-sib.ch/
AliBaba
Prediction of transcription factor binding sites by
constructing matrices on the fly from TRANSFAC 4.0 sites.
http://darwin.nmsu.edu/~molb470/fall2003/Projects/solorz/
http://www.epd.isb-sib.ch/TRADAT.html
Neural Networks (PROMOTER 2.0)
http://www.cbs.dtu.dk/services/promoter/
Density of TF from EPD (PromoterScan)
http://bimas.dcrt.nih.gov/molbio/proscan/
Searches of weight matrices against a test sequence
(TFSearch/TESS)
http://www.cbil.upenn.edu/cgi-bin/tess/tess
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ORF detectors
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Promoter predictors
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CSHL: argon.cshl.org/tabaska/polyadq_form.html
Splice site predictors
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CSHL: http://rulai.cshl.org/software/index1.htm
BDGP: fruitfly.org/seq_tools/promoter.html
ICG: TATA-Box predictor
PolyA signal predictors
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NCBI: http://www.ncbi.nih.gov/gorf/gorf.html
BDGP: http://www.fruitfly.org/seq_tools/splice.html
Start-/stop-codon identifiers
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DNALC: Translator/ORF-Finder
BCM: Searchlauncher
22
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Distinguishing pseudogenes (not working former
genes) from genes.
Exon/intron structure in eukaryotes, exon flanking
regions – not very well conserved.
Exon can be shuffled alternatively – alternative
splicing.
Genes can overlap each other and occur on different
strands of DNA.
Only 2% of human genome is coding regions
Intron-exon structure of genes
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Large introns (average 3365 bp )
Small exons (average 145 bp)
Long genes (average 27 kb)
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Most Gene finders don’t handle UTRs
(untranslated regions)
~40% of human genes have non-coding 1st exons
(UTRs)
Most gene finders don’t’ handle alternative
splicing
Most gene finders don’t handle overlapping or
nested genes
Most can’t find non-protein genes (tRNAs)
• Gene expression is also influenced by the region
upstream of the core promoter and other enhancer sites.
• Eukaryotic sequences show variation not only b/w
species but also among genes within a species. Hence, a
set of promoters in an organism that share a common
regulatory response is analyzed
• The programs can predict 13-54% of the TSSs correctly,
but also each program predicted a number of falsepositive TSSs.
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Gene finding in eukaryotes is not yet a “solved”
problem
Accuracy of the best methods approaches 80% at
the exon level (90% at the nucleotide level) in
coding-rich regions (much lower for whole
genomes)
Gene predictions should always be verified by
other means (cDNA sequencing, BLAST search,
Mass spec.)