Promoter prediction in eukaryotes

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Promoter prediction in eukaryotes

• Grupo 5

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Eukaryotic Gene Structure
branchpoint site
5site
3site
exon 1 intron 1 exon 2
intron 2
CAG/NT
AG/GT
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(No Transcript)
4
(No Transcript)
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Key Eukaryotic Gene Signals

• 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

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(No Transcript)
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RNA Polymerase creates RNA from DNA

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

? RNA Pol II
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Pol II Promoter Elements
Exon Intron Exon
GC box 200 bp
CCAAT box 100 bp
TATA box 30 bp
Gene
Transcription start site (TSS)
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Transcriptional regulatory region

• 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.
  409832-833)
• 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

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Pol II Promoter Elements

• 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

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Pol II Promoter Elements
TATA box is found in 70 of promoters
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Prediccion de promotores
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Gen prediction
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Finding Eukaryotic Genes Computationally

• 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

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Site-based Methods
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Promoter prediction
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Site-based Methods
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• 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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Promoter Databases

• 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/
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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?WELC
OME
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/
Prediction of transcription factor binding sites
by constructing matrices on the fly from
TRANSFAC 4.0 sites.
AliBaba
http//darwin.nmsu.edu/molb470/fall2003/Projects/
solorz/
http//www.epd.isb-sib.ch/TRADAT.html
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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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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.htm
  l
• Start-/stop-codon identifiers
• DNALC Translator/ORF-Finder
• BCM Searchlauncher

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Problems arising in gene prediction.

• 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)

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Outstanding Issues

• Most Gene finders dont handle UTRs (untranslated
  regions)
• 40 of human genes have non-coding 1st exons
  (UTRs)
• Most gene finders dont handle alternative
  splicing
• Most gene finders dont handle overlapping or
  nested genes
• Most cant find non-protein genes (tRNAs)

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• 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.

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Bottom Line...

• 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.)