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Computational analyses of yeast
and human chromatin
William Stafford Noble
Department of Genome Sciences
Department of Computer Science and Engineering
University of Washington
Outline
• Sequence-based models of nucleosome
positioning
• Footprinting protein binding sites
genomewide
Organization of cis-regulatory sequences
Chromatin Fiber
Gene ‘domains’
Nucleus
Trans-factor
complex
Genes
Genomic
DNA
Packaged into
Chromatin
DNaseI
Hypersensitive Site
4/43
9.3%
33/49
67.3%
108/146
73.9%
Overall approach
Microarray data from (Yuan et al. 2006).
Sequence spectrum
A/T 0.5966
C/G 0.4249
AA/TT 0.1931
AC/GT 0.1116
AG/CT 0.1288
AT/AT 0.0815
CA/TG 0.1674
CC/GG 0.0901
CG/CG 0.0172
GA/TC 0.1330
GC/GC 0.0429
TA/TA 0.0515
AAA/TTT 0.0730
AAC/GTT 0.0343
AAG/CTT 0.0472
AAT/ATT 0.0386
...
TTTAAA/TTTAAA 0.0043
• Compute frequencies
of substrings of length
k (k-mers) for k = 1 up
to 6.
• Treat reverse
complements as the
same k-mer.
• The resulting vector
contains 2772 entries.
Primary results
The SVM recapitulates array data
10bp periodicity
AA/TT/AT periodicity, Segal 2006
AA periodicity, Drew & Travers 1986
Periodicity in SVM score, Peckham 2007
Comparison of yeast models
Segal 2006:
• The model is positional.
• The model is generative.
Peckham 2007:
• The model is compositional.
• The model is discriminative.
• Compare predicted positions
with 199 sites from the
literature.
• 54% are within 35 bp
• Expect 39% by chance.
• The model explains >50% of
the signal.
• The model performs 15%
better than chance.
• Compare predicted positions
with sites derived from (Yuan
2006).
• 50% are within 40 bp
• Expect 33% by chance.
• The model explains ~50% of
the signal.
• The model performs 17%
better than chance.
Two data sets
• Dennis et al., Genome
Research, 2007
• 25 kb regions upstream
of 42 genes
• 50-mer probes every 20
bp
• 3 arrays, 3 copies of each
probe, forward and
reverse strand → 18
measurements per probe
• Ozsolak et al., Nature
Biotechnology, 2007.
• 1.5 kb regions upstream
of 3692 genes
• 50-mer probes every 10
bp
• 7 cell lines
Cross-validation results
Complementary aspects of
chromatin accessibility
MEC SVM accurately identifies high MNase accessibility.
Strong MNase digestion (MEC) allows the
recognition of nucleosome disfavoring sequences.
Weak MNase digestion (A375) allows the
recognition of nucleosome forming sequences.
Dennis and A375 SVMs accurately identify low MNase accessibility.
Yeast and human concordance
0.862
0.849
Each model was applied to the human ENCODE regions.
Low- and high-scoring regions
A375 SVM scores are averaged over 1000 top- and bottom-scoring regions.
Flanking lines indicate standard error of the mean.
Dinucleotide frequencies
• MNase cleavage bias is
unlikely to account for
such large differences.
• Nucleosome forming
sequences exhibit a 3bp
periodicity of CG and GC
dinucleotides.
• Nucleosome disfavoring
sequences tends to be
low complexity.
Transcription start sites
A375 – weak digestion
Recognizes nucleosome
forming sequences
MEC – strong digestion
Recognizes nucleosome
disfavoring sequences
SVM scores are averaged over all TSSs in the ENCODE regions.
Summary
• An SVM can discriminate between MNase
protected and MNase accessible sequences
with high accuracy.
• The model learns to recognize complementary
phenomena, depending upon the degree of
MNase digestion.
• The model recapitulates known features of
human chromatin.
• Most nucleosome positioning is boundary-event
driven.
Methodology
60% of DNaseI
cleavage occurs
in intergenic
regions
Individual footprints
Problem definition
• Given
– Cut-counts at each position
– Unique mappability (Boolean) of each position
– Size range of footprints
– Size of the background window
• Return
– A ranked list of non-overlapping footprints,
each associated with a statistical confidence
score
Scoring a candidate footprint
Foreground window
Background window
A depletion score
Depletion score: binomial distribution
• The probability that a window of size a within the
target region will contain x or fewer cuts
– a: effective foreground window size
– b: effective background window size
– B: # of cuts in the background window
• Score all overlapping windows of width kmin to
kmax.
Greedy selection
• Generate a non-overlapping set of highscoring windows
– Sort all of the depletion scores in ascending
order
– Traverse the sorted list, accepting a scored
window if it does not overlap a previously
accepted window
Empirical null model
• Shuffle the cut-counts at the level of
genomic positions, together with the
mappability information of each position
• Repeat the depletion scoring and greedy
selection procedure on the shuffled data
• Generate a ranked list of footprints
• Estimate false discovery rate using Storey
method.
Evaluation: gold standard
• MacIsaac set [MacIsaac et al. 2006]
– Conserved regulatory sites in yeast
– Identified from ChIP data
– 4387 sites with stringent thresholds
• Imperfect
– Conservatively defined
– Different experimental conditions
• Only used to compare different footprint
detectors
Evaluation: metric
• Recall = TP / (TP+FN)
• Precision = TP / (TP+FP)
Results
“What fraction of the
footprints contain a
MacIsaac motif?”
“What fraction of the MacIsaac
motifs are in footprints?”
Results
•
•
•
•
Binomial scoring performs better than the simple ratio.
The rank transformation yields better results.
Larger background widths are better.
Using the double scoring scheme does not always help.
Results
• 238,133 candidate
footprints
• 4514 are significant
at q<0.05.
• Estimated 10,716
footprints in total.
• Our algorithm
identifies 40.0% of
these at q<0.05.
• Scan footprints with
MacIsaac motifs, using
q<0.05.
• 36.6% of the footprints
contain a motif.
• Also scan intergenic
regions.
• Every motif occurs more
frequently in footprints
than in intergenic regions.
Footprints contain known motifs
Motif information content is inversely correlated with Phastcons score (p < 0.0022).
Motif discovery
15 sites, E=7e-12
8 sites, E=6e-11
7/8 sites occur in sigma
LTRs associated with
retrotransposons
41 sites, E=1e-29
28 sites, E=3e-6
MCM1
• The first motif matches the core of the
TRANSFAC MCM1 motif.
Motif discovery
41 sites, E=1e-29
• 108 occurrences in
footprints.
• Of these, 42 are within
250bp 5’ of the start of
a gene.
28 sites, E=3e-6
• 35 occurrences in
footprints.
• Of these, 22 are within
250bp 5’ of the start of a
gene.
Global view of chromatin organization
Summary
• Digital genomic footprinting provides a
nucleotide-level map of DNaseI accessibility
across the yeast genome.
• This map enables identification of individual
protein binding sites.
• Dramatically improves the signal-to-noise ratio
for motif searching.
• The method can be performed on any organism
whose genome is sequenced, exposing its entire
cis-regulatory framework in a single experiment.