Identify regulatory modules from gene expression data

1
Identify regulatory modules from gene expression
data

• Xu Ling
• 02/09/2005

2
Introduction

• Much of a cells activity is organized as a
  network of interacting modules sets of genes
  coregulated to respond to different conditions.
  Identifying this organization is crucial for
  understanding cellular responses to internal and
  external signals.
• Genome-wide expression profiles (e.g., DNA
  microarray) provide important information about
  regulatory mechanisms.
• With the availability of complete genome
  sequences, identifying cis-regulatory elements
  via a bioinformatics approach on a genome-wide
  manner comes out as a promising solution.

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Tasks

• Whats the underlying mechanisms by which genes
  are regulated?
• Modules of coregulated genes?
• Regulators (transcription factors)?
• Regulation conditions (TFBSs/motifs, positional
  and combinatorial constraints)?

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General scheme (1)

• clustering-based approaches for finding motifs
  from gene expression and sequence data

classify
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General scheme (2)

• sequence(/knowledge)-based approaches for finding
  motifs from gene expression and sequence data

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General scheme (3)

• Comparative genomics has also been applied to
  identify eukaryotic regulatory elements (e.g.,
  Human-Mouse) because functional noncoding
  sequences may be conserved across species from
  evolutionary constraints.
• Finding a good pair of species to compare and
  choosing a good sequence conservation threshold
  are critical and such information is not
  available for most species.

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Related work

• Predicting gene expression from sequence
• Michael A. Beer and Saeed Tavazoie
• Cell, 2004, 117 185-198
• A successful application of existing
  computational approaches in studying the yeast
  transcriptional regulation network

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Approach

• Clustering (k-means) modules of coregulated
  genes
• Motif Finding (AlignACE) putative regulatory
  elements (TFBSs)
• Bayesian network learning regulation conditions
  (motifs, positional and combinatorial constraints)

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Bayesian Network

• Sequence features (x1,,xn) ? expression patterns
  (ei)
• Sequence feature (xi) presence of motifs,
  positional constraints, and combinatorial
  constraints
• Expression pattern (ei) a binary one layer
  network
• Maximizing P(eix1,,xn), the probability that
  genes with these sequence features will
  participate in expression pattern i

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Properties

• Easy to integrate all kinds of sequence features
• Explicit Sequence features
• To avoid complex networks overfit the training
  data, a parameter for penalizing dense networks
  is used.
• Optimal network is greedily learned.

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Motif finding approaches

• Explicit statistical modeling based
• Expectation maximization MEME,
• Gibbs Sampling AlignACE, Gibbs Motif sampler,
• Others CONSENSUS,
• word enumeration based MDscan,

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MEME

• Sequence is broken up into all overlapping
  subsequences of length W which it contains.
• Two-component finite mixture model Motif (a
  set of similar subsequences of fixed width)
  Background (all other positions in the
  sequences)
• Motif model each example of the motif is assumed
  to be generated by a sequence of independent,
  multinomial random variables.
• Background model each position (which is not
  part of a motif) is generated independently by a
  multinomial random variable.
• Maximize the likelihood of the model M given the
  data D L(MD)p(DM) by EM algorithm

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Gibbs motif sampler

• Dealing with a specific model alignment rather
  than a weighted average as EM does.
• Iteratively sample motif models (or possibly
  background model) for each subsequence and
  thereby partition motif-encoding regions into
  different motifs.
• Iterative heuristic method, which combines
  gradient search steps with random jumps in the
  search space, hence not guaranteed to reach
  optimal, but wont stuck at local maximums as EM
  does.
• Identify the most probable motif models by
  locating the optimum alignments, which maximize
  the ratios of the corresponding target
  probabilities to the background probabilities
  (MAP (maximum a posteriori) score).

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Future work

• Ab initio motif finding approach from gene
  expression and sequence data by attempting new
  heuristic or statistic model.
• Integrating prior knowledge (e.g., GO) to
  facilitate identification of regulatory elements
  and transcriptional network.