Genomewide Analysis of Gene regulation

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• Genome-wide Analysis of Gene regulation

Berlin, 4th of May, 2005
Presentation by David Rozado
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Comparative analysis of methods forrepresenting
and searching for transcriptionfactor binding
sites

• Robert Osada, Elena Zaslavsky and Mona Singh
• Department of Computer Science Lewis-Sigler
  Institute for Integrative Genomics,
• Princeton University, Princeton, NJ 08544, USA

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Introduction

• Identification of DNA binding sites for
  transcription factors
• Important step in unraveling the transcriptional
  regulatory network
• Several approaches for transcription factors
  binding sites search
• consensus sequences
• position-specific scoring matrices
• Berg and von Hippel
• Centroid
• Such basic approaches can all be extended by
  incorporating
• pair wise nucleotide dependencies
• per-position information content
• The paper evaluates the effectiveness of the
  basic approaches and their extensions in finding
  binding sites for a transcription factor of
  interest

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Datasets

• 68 regulatory proteins and their aligned DNA
  binding domains
• Number of Filters applied
• Only proteins with at least four binding sites
  were considered
• Duplicate binding sites were removed in order to
  preserve the integrity of the leave-one-out
  cross-validation
• Each binding site unambiguously located within
  the E.coli K-12 genome and extracted along with
  flanking regions on each side
• This process left 35 transcription factors and
  410 binding sites
• Average of 11.7 8.5 sites per transcription
  factor

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Notation

• S set of N DNA binding sites for a transcription
  factor
• nj (b) number of times base b appears in the j
  -th position in S
• fj (b) corresponding frequency
• n(b) number of times base b appears overall in
  the N binding sites
• f (b) overall frequency for base b
• nij (b, d) number of times the ordered pair (b,
  d) occurs in positions i and j
• fij (b, d) corresponding frequency
• tj j -th base of the sequence t to be scored

j
i
AGTTAACAAT
t
AGGTAACAAA
S
ATATAACAAT
TTTTAACAAT
AGTAATCAAT
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Approaches for representing and searchingfor
binding sites
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Extension I - Pairwise correlations

• A method for incorporating pairwise correlations
  should only take into account those pairs that
  act together in determining DNAprotein binding
  specificity.
• Such precise information is not always readily
  available
• As approximation, focus on considering pairwise
  correlations between bases that are nearby in
  sequence
• Introduce the notion of scope to delimit which
  pairs are considered correlated.
• A scope of one restricts correlated positions to
  adjacent pairs
• a scope of two considers both adjacent pairs and
  pairs separated by an intermediate base

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Extensions II - Information content

• Information content (IC) is a concept based on
  the information-theoretic notion of entropy.
• In the current application, the entropy of a
  position expresses the number of bits necessary
  to describe the position in a binding site
• The information content of a position is
  calculated by subtracting its entropy from the
  value of the maximum possible entropy
• The higher the information content, the more
  conserved (and presumably more important) the
  position

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Cross-validation testing and analysis

• Common usage of any of the methods described
  above would be to scan non-coding regions in a
  genome in order to find binding sites for a
  particular transcription factor
• Such a framework is not easily applicable when we
  wish to evaluate and compare different methods
• The E.coli genome contains many yet
  uncharacterized binding sites
• Predicted windows may correspond to true binding
  sites even if they are not annotated as such in
  the original dataset
• Testing framework with sets of positive and
  negative examples

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Cross-validation testing and analysis II

• Conduct leave-one-out cross-validation studies to
  evaluate a particular method
• Suppose s belongs to a set S of known binding
  sites, each of length l, for a particular
  transcription factor TF
• The method under consideration uses all the sites
  except s, to build the binding site
  representation for TF, and scores s as well as a
  set of negative examples
• The negative examples consist of all binding
  sites in our dataset except those known to be
  bound by TF
• It is still is possible that transcription factor
  TF can bind some of the negative examples
• Nevertheless, s should be among the top scoring
  sites in the overall pool

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Comparing Methods

• For each site s of a transcription factor under
  consideration a rank in cross-validation testing
  is computed by counting how many negative
  examples score as well or better than s
• lower rank indicating better performance
• To compare how well two methods perform, a
  Wilcoxon matched-pairs signed-ranks test is used
• The number of times one method outperforms the
  other is compared with how many times such an
  event would happen merely by chance under the
  assumption that both methods perform equally well
• A ROC curve for each individual leave-one-out
  test is created and then, the average over all
  sites for that transcription factor is computed

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Comparison of basic methods
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ROC curves comparing performance when pairs are
considered for Centroid
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ROC curves comparing performance when pairs are
considered for PSSM
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ROC curves comparing Centroid-P with scope 2
using regular sites and sites with columns
shuffled
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Performance of methods based on averaged ranks
per transcription factor
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Conclusions

• Using per-position information content to weigh
  positional scores improves the performance of all
  methods
• Sometimes dramatically
• Methods based on nucleotide matches, such as
  consensus sequences and Centroid, show
  statistically significant improvements when
  incorporating pairwise nucleotide dependences
• Probabilistic methods, such as log-odds PSSMs, do
  not show statistically significant improvements
  when incorporating pairwise dependencies
• Difference in performance between methods
  decreases substantially once information content
  and pairwise correlations have been incorporated

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Making connections between novel
transcriptionfactors and their DNA motifs

• Kai Tan,1 Lee Ann McCue,2 and Gary D. Stormo1,3
• 1Department of Genetics, Washington University
  School of Medicine, Saint Louis, Missouri 63110,
  USA 2The Wadsworth Center,
• New York State Department of Health, Albany, New
  York 12201-0509, USA

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Introduction

• A computational method to connect novel
  transcription factors and DNA motifs in E. coli
• The method takes advantage of three types of
  information to assign a DNA binding motif to a
  given TF
• A distance constraint between a TF and its
  closest binding site in the genome (Dmin
  information)
• The phylogenetic correlation between TFs and
  their regulated genes (PC information)
• A binding specificity constraint for TFs having
  structurally similar DNA-binding domains (FMC
  information)
• The different types of information are combined
  to calculate the probability of a given
  transcription-factorDNA-motif pair being a true
  pair

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Distance constraint

• Besides auto-regulation, it has been noticed in
  many cases that TFs and the genes they regulate
  are near each other in the genome
• Distance constraint between the TF and its
  closest binding site in the genome

• Dmin_self is the distance between a TF gene and
  its closest binding site in the genome
• Dmin_cross is the distance between a TF gene and
  the closest binding site for a different TF

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The phylogenetic correlation

• TFs and their regulated genes tend to evolve
  concurrently
• Connect TFs and DNA motifs through correlation
  between their occurrences in a comparative
  analysis of multiple species
• Two types of phylogenetic correlation (PC)
  distributions
• PC for true TFDNA-motif pairs.
• PC for false TFDNA-motif pairs

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Binding specificity constraint

• TFs that are more similar to one another are
  expected to bind to sites that are more similar
  to each other than to dissimilar pairs
• Distribution of average similarity scores for
  motifs from the same family and from different
  families

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Conclusions

• Hypothesize that information concerning the
  connection of a TF to its DNA motif is carried in
  the genome sequences
• TFs and their binding sites are often in similar
  genomic locations (Dmin information)
• TFs tend to evolve concurrently with their
  regulated genes (PC information)
• TFs from the same structural family tend to have
  similar DNA motifs

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Functional determinants of transcription factors
inEscherichia coli protein families and binding
sites

• M. Madan Babu and Sarah A. Teichmann
• MRC Laboratory of Molecular Biology, Hills Road,
  Cambridge CB2 2QH, UK

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Introduction

• DNA-binding transcription factors regulate
  expression of genes near to where they bind
• These factors can be activators or repressors of
  transcription, or both
• A fundamental question is what determines whether
  a transcription factor acts as an activator or a
  repressor?
• Proteinprotein contacts
• Position of the DNA-binding domain in the protein
  primary sequence
• Altered DNA structure,
• Position of its binding site on the DNA relative
  to the transcription start site
• This work suggest that, in general, in E. Coli, a
  transcription factors protein family is not
  indicative of its regulatory function, but the
  position of its binding site on the DNA is

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Domain Architectures for different TFs
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Conclusions

• Activators, repressors and dual regulators in E.
  coli belong to many of the same protein families
  and share some domain architectures
• A transcription factors regulatory role is not
  determined by protein structure or evolutionary
  relationships
• Transcription factors have evolved by duplication
  of an ancestral transcription factor, followed by
  a change in function through a shift in binding
  sites
• A transcription factors regulatory role is
  determined to a large extent simply by the
  position of the transcription factor binding site
• Activators have essentially only upstream binding
  sites
• More than two thirds of repressors have at least
  one downstream binding site

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