Grupo 5

5’site5’site 3’site3’site

branchpoint sitebranchpoint site

exon 1 intron 1 exon 2 intron 2exon 1 intron 1 exon 2 intron 2

AG/GTAG/GT CAG/NTCAG/NT

Pol II RNA promoter elements Cap and CCAAT region GC and TATA region

Kozak sequence (Ribosome binding site-RBS)

Splice donor, acceptor and lariat signals Termination signal Polyadenylation signal

RNA Polymerase I – rRNA RNA Polymerase II – mRNA RNA Polymerase III – tRNAs, srRNAs

RNA Pol II

GC box~200 bp

CCAAT box~100 bp

TATA box~30 bp

Gene

Transcriptionstart site (TSS)

Exon Intron Exon

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. 409:832-833)

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

1st exonU1

GU

SR

TSS

GTFsRNAP II

5’ 3’

core promoter region (~100bp)

activator

repressor

70K

regulatory promoter region

1st exon1st exonU1

GUU1

GU

SRSR

TSS

GTFsRNAP II

5’ 3’

core promoter region (~100bp)core promoter region (~100bp)

activator

repressor

70K70K

regulatory promoter region

regulatory promoter region

Cap Region/Signal n C A G T n G

TATA box (~ 25 bp upstream) T A T A A A n G C C C

CCAAT box (~100 bp upstream) T A G C C A A T G

GC box (~200 bp upstream) A T A G G C G nGA

TATA box is found in ~70% of promoters

Content-based Methods GC content, hexamer repeats, composition

statistics, codon frequencies Site-based Methods

donor sites, acceptor sites, promoter sites, start/stop codons, polyA signals, lengths

Comparative Methods 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.

TRANSFAC is a database on eukaryotic cis-acting regulatory DNA elements and trans-acting factors. It covers the whole range from yeast to human.

Biological Databases/Biologische Datenbanken GmbH

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.

MatInspector Search for potential transcription factor binding sites in your own sequences with the matrix search program MatInspector using the TRANSFAC 4.0 matrices.

FastM A program for the generation of models for regulatory regions in DNA sequences. FastM using the TRANSFAC 3.4 matrices.

PatSearch Search for potential transcription factor binding sites in your own sequences with the pattern search program using TRANSFAC 3.5 TRRD 3.5 sites.

FunSiteP Run interactively FunSiteP. Recognition and classification of eukaryotic promoters by searching transcription factor binding sites using a collection of Transcription factor consensi.

http://www.gene-regulation.com/

http://www.epd.isb-sib.ch/

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.

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

AliBaba

http://darwin.nmsu.edu/~molb470/fall2003/Projects/solorz/

http://www.epd.isb-sib.ch/TRADAT.htmlhttp://www.epd.isb-sib.ch/TRADAT.html

Prediction of transcription factor binding sites by Prediction of transcription factor binding sites by constructing matrices on the fly from constructing matrices on the fly from TRANSFAC 4.0 sites. 4.0 sites.

Neural Networks (PROMOTER 2.0)

Density of TF from EPD (PromoterScan)

Searches of weight matrices against a test sequence (TFSearch/TESS)

http://www.cbs.dtu.dk/services/promoter/

http://bimas.dcrt.nih.gov/molbio/proscan/

http://www.cbil.upenn.edu/cgi-bin/tess/tess

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

22

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

Large introns (average 3365 bp ) Small exons (average 145 bp) Long genes (average 27 kb)

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 false-positive TSSs.

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.)