Computational detection of genomic cisregulatory modules applied to body patterning in the early Dro

1
Computational detection of genomic cis-regulatory
modules applied to body patterning in the early
Drosophila embryo

• N. Rajewsky, M. Vergassola, U. Gaul and E. Siggia
• Presented by Bin Tan

2
Cis-regulatory modules (CRM)

• In higher eukaryotes, many genes show complex
  spatial-temporal expression patterns.
• Gene transcription regulation apparatus is
  largely organized in the form of separable
  cis-regulatory modules.
• A module integrates inputs from several
  transcription factors and regulates another
  genes expression, forming a regulatory network.

3
Structural features of modules

• Hundreds of nucleotides in length
• Contains binding sites for as many as 4-5
  different transcription factors
• Possibly multiple binding sites for the same
  transcription factor
• Certain combinations of binding sites
  correlations between different transcription
  factors

4
Why computational methods?

• Pure experimental methods such as promoter
  bashing is tedious.
• It is easier to screen a modest list of
  candidates suggested by a computational method.

5
About this paper

• Uses data on body patterning of the early
  Drosophila embryo
• Makes statistically significant predictions of
  regulatory modules using three different levels
  of prior information
• Binding sites (motifs)
• Several related modules
• Only genome

6
Three levels of prior information1. Binding
sites (motifs)2. Several related modules3. Only
genome
7
The Ahab algorithm

• Uses known binding sites (motifs) information
• Scans the genome in windows
• Scores each window according to how well the
  sequence can be stochastically generated from the
  motifs
• Outputs windows with high ranks

8
Ahab features

• (As compared to Mobydick)
• Uses positional weight matrices as the motif
  model
• Introduces a local background to remove influence
  from local variations in sequence composition
• Allows binding sites to overlap
• Allows weak binding sites to contribute to the
  score
• No parameter tuning (other than the window size)

9
Algorithm details

• Background model k-th order Markov chain (each
  nucleotide is only dependent on the preceding k
  nucleotides)

10
Algorithm details (cont.)

• Sequence Ss1s2..
• Weight matrices w1 w2 .. for motifs
• Background wb
• Probabilistic generation of S
• Choose a motif or background wk1,2,..b with
  probability pk
• Sample a sequence according to w and append it to
  S
• Repeat until S reaches a certain length

11
Algorithm details (cont.)

• Unknown arameters? p1 p2 .. pb
• Maximize
• Conjugate descent or EM algorithm

12
Experiment setup

• Input weight matrices for 8 transcription
  factors constructed from 11 modules
• Window size 500 bp
• 27 modules known to receive maternal/gap gene
  input

13
Results

• 146 highly significant modules found
• For 27 known modules
• 116 recovered
• 3 when filtering for at least 3 different factors
• 3 because they contain only other factors
• 4 ranked very low (700)
• For 15 novel predictions
• one of the adjacent genes is patterned in the
  blastoderm

14
Estimation of positive rate

• Scramble the columns in the weight matrices half
  as many predictions - 50 false positive rate
• (615)3/(146-11) - 50 positive rate

15
Experiment variations

• Remove the least specific matrix (tailless) from
  input
• 75 of the predictions without using tailless are
  also present in the list of 146
• Vary window size to 700bp
• 58 in the list of 146 are also among the top 200
  of the 700bp set
• Interesting new predictions

16
Three levels of prior information1. Binding
sites (motifs)2. Several related modules3. Only
genome
17
Motivation

• For most transcription factors, binding site
  information is rarely known
• Modules obtained by experimental methods (e.g.
  promoter bashing) are more common

18
The method

• Uses standard motif finders to recover weight
  matrices from input modules
• Feed the motifs to Ahab to find similarly
  regulated genes

19
The method (cont.)

• Gibbs sampler algorithm
• Lawrence et al. Detecting subtle sequence
  signals a Gibbs sampling strategy for multiple
  alignment. (Presented by Xin He)
• Customizations
• Search for only one binding site at a time.
• Mask only the central 1-2 bases of each motif
  before iterating.
• - Results are more reproducible between runs.
• - Motifs are allowed to overlap.

20
Experiment results

• Testing on modules with known binding site
  information
• Gibbs sampling predicts 30-50 of the sequence is
  covered by motifs
• Gibbs motifs has higher specificity
• Recovers half of the known motifs
• Predicts several new interesting motifs

21
Experiment results (cont.)

• Input 3 modules receiving inputs from 6
  transcription factors
• 6 highly significant weight matrices found
• Kr, Kni, (HbCad) 3 new
• Ahab finds 63 highly significant modules
• 4 overlaps with the input modules
• 13 contiguouss to genes patterned in the
  blastoderm
• Comparable positive rates

22
Three levels of prior information1. Binding
sites (motifs)2. Several related modules3. Only
genome
23
The Argos algorithm

• Only uses the genome data (Unsupervised)
• Motivation Is the redundancy of binding sites
  inside modules strong enough to predict modules
  alone?
• The first successful attempt to do this for a
  metazoan genome

24
The Argos algorithm

• To determine whether a motif is locally
  overrepresented Score its frequency in the
  sequence against its expected frequency
  (according to genome wide background).
• Enumerate all possible motifs of length 8.
• Compute their frequency in the genome (background
  counts), allowing 2 mutations

25
The Argos algorithm (cont.)

• Move a sliding window S over the genome
• Compute a motifs local count c in S
• Compute the motifs expected count from
  background
• Rank the motifs by their Poisson scores
• The motifs are often related to each other
• Greedily select the top motif and eliminate
  related ones (under shifts and up to 4 mutations)
• Repeat until 5 motifs have been produced
• Use the sum of the selected motifs scores as the
  score for S

26
Experiment results

• For a certain set of modules, Argos recovers half
  of them - 50 false negative rate
• For several genes with 15 known modules, Argos
  recovers 7 when looking over 10kbp upstream of
  translation start
• Genome wide, roughly one module per gene

27
Experiment results