ChIP-seq and its applications in GRN construction

ChIP-seq and its applications in GRN construction

Jin Chen2012 Fall

CSE891-001

Layout• Genome-scale evidence from microarray measurements may

be used to identify regulatory interactions between TFs and targets

• Hu et al used a genetic approach to identify targets of transcription factors in Yeast and reconstruct a functional regulatory network

• Reimand et al re-analyzed Hu’s data using improved statistical techniques

Hu et al’s work• Grew each of 263 transcription factor knockout strains and

compared mRNA expression of each of these strains with a wildtype strain using microarrays

• Defined unrefined transcription factor target network as the cumulative set of significantly differentially expressed genes in each deletion strain.

• There was overlap between transcription factor targets identified in the unrefined network and targets identified by ChIP-chip

2-level Refinement• First level of network refinement

– If TF A activated TF B and gene M, B activated gene M, and if the confidence of A regulating gene M was lower than for B regulating gene M, then the regulation of gene M by A was presumed to be indirect and was therefore erased

• Additional refinement step– Similar to previous step, except that the indirect edge that was

removed bridged a three-step direct interaction series at the preceding level, resulting in a level 3 refined network

• Note that the logical consistency for regulatory edges was maintained at all times

Hu et al’s work• When the transcription factor bound to a promoter was

deleted, the expression of the downstream gene was much more likely to be affected than the background

• Expression from promoters that were detectably occupied by a single TF were even more likely to be affected by deletion of that potentially major or sole TF

• Thus, there was significant overlap between binding targets defined by ChIP-chip and functional targets defined by TF deletion

Hu et al’s work – problems• However, Hu et al ‘s study used relatively dated and

insensitive approaches for microarray data processing• As a result the published P-values and target-gene ranking are

likely to be unreliable– P-values were not corrected for multiple-testing– Lack of background and print-tip correction during normalization

• Reimand et al re-analyzed the same dataset with the state-of-art software and obtained a much larger network

Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

False Discovery Rate• False discovery rate is a statistical method used in multiple

hypothesis testing to correct for multiple comparisons. q-value is defined to be the FDR analogue of p-value

• FDR is the expected proportion of false positives among all significant hypotheses

• For example, if 1000 observations were experimentally predicted to be different, and FDR for these observations was 0.1, then 100 observations would be expected to be false

• FDR is determined from the observed p-value distribution, and hence is adaptive to the amount of records

Redo the Preprocessing • Microarrays were normalized using the VSN package,

including print-tip and background correction

• Differential expression was calculated using a moderated eBayes t-test as implemented in the Limma Bioconductor package

• FDR cut-off of 0.05 was used to detect significant differential gene expression


Re-analyze TF binding data• DNA–protein interactions derived from ChIP-chip experiments

were obtained and with a P_value<0.001 were considered

• A set of ‘trusted’ position weight matrices (PWMs) for 72 regulatory factors were derived by running the PROCSE and PhyloGibbs algorithms on a set of experimentally derived TF binding sites from SCPD

• These PWMs were then used to scan multiple alignments of each intergenic region in Yeast with the orthologous regions of another four Yeast species


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Re-analyze knockout expression and ChIP binding data

• Overlap between TF-binding and TF knockout data – Collect binding sites for 142 TFs, comprising 5,188 ChIP-

chip interactions and 17,091 motif predictions– Calculate the intersection between the list of differentially

expressed genes from the TF knockout and targets identified by ChIP-chip or binding-site predictions

– 2,230 regulation relations


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• Checked the expression levels of the TFs– Intuitively one expects the TF under consideration to have

lower expression in the mutant strain compared with the wild type strain

– confirms this for 155 TFs– 78 TFs display a negative fold change at statistically non-

significant levels– 36 TFs are lethal


15Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

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• Examine functional annotations of differentially expressed genes– As most TFs are considered to regulate distinct cellular

processes, their target genes should be associated with a coherent set of molecular and biological functions

– Used g:Profiler to identify GO, KEGG and Reactome pathway annotations

– Across all TF knockouts, this analysis has a higher score than the original analysis


17Reimand et al, Nucleic Acids Research, 2010, Vol. 38, No. 14 pp 4768–4777

SUMMARY - exploring biological networks

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Topology Approaches

• What’s the next after constructing biological networks?

• First of all, simple approaches– Degree, betweenness, clustering coefficient,

topological coefficient, shortest path– Shared neighbors, neighborhood connectivity,

closeness centrality

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Clustering Coefficient• Clustering coefficient is a measure of degree to which nodes

in a graph tend to cluster together • Clustering coefficient (local version): does my neighbors

connect with each other?• Evidence suggests that in most real-world networks, nodes

tend to create tightly knit groups characterized by a relatively high density of ties

where ki is the number of neighbors of node i and ei is the number of connected pairs between all neighbors of node i

𝐶𝑖=2𝑒𝑖

𝑘𝑖×(𝑘¿¿ 𝑖−1)¿

Luciano da F. Costa, Francisco A. Rodrigues, Alexandre S. Cristino. Complex networks: the key to systems biology. Genet. Mol. Biol. vol.31 no.3. 2008; http://med.bioinf.mpi-inf.mpg.de/netanalyzer

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Average Clustering Coefficient Distribution

Nodes with only a few links have a high C(k) and belong to highly interconnected small modules

By contrast, the highly connected hubs have a low C(k), with their role being to link different, and otherwise not communicating, modules

Define function C(k) as the average clustering coefficient of all nodes with k links

For many real networks C(k) ~ k–1

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closeness centrality• Closeness centrality is a measure of how many steps is required to access

every other node from a given node• Closeness centrality: How long it will take information to spread from a

given node to other reachable nodes in the network?

𝐶𝐶 (𝑖)=∑𝑡∈𝑉 ¿

𝑑𝐺(𝑖 ,𝑡)

¿𝑉∨−1

where dG(i, t) is the length of the shortest path from i to t, and V is the set of nodes in G

Freeman, 1978; Opsahl et al., 2010; Wasserman and Faust, 1994

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Distribution of closeness centrality

Closeness centrality are successful in distinguishing the important members of the community

Its distribution resembles a normal curve, while the other centrality measures have a long tail distribution similar to a power law

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Limitations of simple approaches

• Study each node/edge individually; cannot apply enrichment study

• Topology study only; difficult to integrate other knowledge

• Nodes with high scores <> key genes/proteins

Study a group of genes simultaneously

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Advanced approaches

• Dense subgraph detection• Network motif detection• Graph clustering• Graph classification• etc.

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Dense subgraph detection

Software available at http://zhoulab.usc.edu/CODENSE/

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Dense subgraph detection• A subgraph is considered coherent and dense if and only if

every edge is well supported, and its corresponding second-order graph is dense

CODE

NSE

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Network Motif Detection

Network Motif Detection

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Perform graph join operation to find repeated size-k graphs

Join each tree with it’s cousins to produce frequent motif candidates Ck.

t4_1

t4_2

&

& &

h1 h2

h3 h4 h5

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Graph Clustering• Graph clustering is an organization process with the goal to

put similar nodes together; the result is a partition of the network into a set of communities

• MCL algorithm is a fast and scalable unsupervised cluster algorithm for graphs based on simulation of stochastic flow in graphs, available at http://www.micans.org/mcl

Van Dongen, S. (2000) Graph Clustering by Flow Simulation. PhD Thesis, University of Utrecht, The Netherlands

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Graph Clustering

Graph

Graph Clusters

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ChIP-seq and its applications in GRN construction