Inference of cell type specific regulatory networks on mammalian lineages

Chasman, Deborah; Roy, Sushmita

doi:10.1016/j.coisb.2017.04.001

Cited by 18 publications

(19 citation statements)

References 113 publications

(113 reference statements)

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“…PBSCs. This analysis revealed that the vast majority of DHS underlying interactions between the three data-sets and those of individual patients were shared with an average level of more than 80% overlap ( Fig S8A) confirming earlier observations that the global transcriptional network of related cells is also highly related 35,36 . Sub-type-specific DHSs participating in interactions clustered within their patient group, and related groups, but not with unrelated groups (Fig S8B), confirming that the two patients were representative for those groups.…”

Section: Differential Interactions Drive Aml Subtype-specific Expresssupporting

confidence: 87%

“…Constitutive and inducible transcription factors form regulatory circuitries and networks by interacting with their own/or other regulatory genes 35 . Cancer cells are capable of maintaining a stable regulatory network over extended periods of time, implying that the expression of each member of such a network is tightly controlled and remains in balance.…”

Section: Different Types Of Aml Are Maintained By Different Transcripmentioning

confidence: 99%

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Subtype-specific regulatory network rewiring in acute myeloid leukemia

Boniferi

Assi

Imperato

et al. 2018

Experimental Hematology

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Section: Differential Interactions Drive Aml Subtype-specific Expresssupporting

confidence: 87%

Section: Different Types Of Aml Are Maintained By Different Transcripmentioning

confidence: 99%

Subtype-specific regulatory network rewiring in acute myeloid leukemia

Boniferi

Assi

Imperato

et al. 2018

Experimental Hematology

View full text Add to dashboard Cite

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“…The majority of DHSs underlying interactions between the three data-sets and those of individual patients were shared with an average level of 80% overlap (Supplementary Fig. 8d), confirming that the global transcriptional network of related cells is highly related35,36. Sub-type-specific DHSs participating in interactions were enriched within related groups, but not within unrelated groups (Supplementary Fig.…”

Section: Resultsmentioning

confidence: 57%

“…Constitutive and inducible TFs form regulatory networks by interacting with their own and/or other regulatory genes35. Cancer cells maintain a stable regulatory network, implying that the expression of each network member is tightly controlled and remains in balance.…”

Section: Resultsmentioning

confidence: 99%

Subtype-specific regulatory network rewiring in acute myeloid leukemia

et al. 2018

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Acute myeloid leukemia (AML) is a heterogeneous disease caused by a variety of mutations in transcription factors, epigenetic regulators and signaling molecules. To determine how different mutant regulators establish AML subtype-specific transcriptional networks we performed a comprehensive global analysis of cis-regulatory element activity and interaction, transcription factor occupancy and gene expression patterns in purified leukemic blast cells. Here, we focussed on specific sub-groups of patients carrying mutations in genes encoding transcription factors ( RUNX1, CEBPA) and signaling molecules ( FTL3-ITD, RAS, NPM1). Integrated analyses of these data demonstrates that each mutant regulator establishes a specific transcriptional and signaling network unrelated to that seen in normal cells, sustaining the expression of unique sets of genes required for AML growth and maintenance.

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“…Many techniques can be employed to improve the fidelity of a given dataset, from more effective experimental and data processing pipelines to post-processing methods for finding particular features within a dataset [13–15]. Fewer analytical methods, however, focus on the dynamics of the chromatin architecture and its specificity [16]. Clustering techniques in particular may be useful in detecting functional modules of genes in Hi-C data.…”

Section: Introductionmentioning

confidence: 99%

Topological structure analysis of chromatin interaction networks

et al. 2019

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BackgroundCurrent Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks.ResultsIt has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, such evidence was based on manual analysis and was limited. Here we present a novel method for analysis of chromatin interaction networks aimed towards identifying characteristic topological features of interaction graphs and confirming their potential significance in chromatin architecture. Our method automatically identifies all connected components with an assigned significance score above a given threshold. These components can be subjected afterwards to different assessment methods for their biological role and/or significance. The method was applied to the largest PCHi-C data set available to date that contains interactions for 17 haematopoietic cell types. The results demonstrate strong evidence of well-pronounced component structure of chromatin interaction networks and provide some characterisation of this component structure. We also performed an indicative assessment of potential biological significance of identified network components with the results confirming that the network components can be related to specific biological functionality.ConclusionsThe obtained results show that the topological structure of chromatin interaction networks can be well described in terms of isolated connected components of the network and that formation of these components can be often explained by biological features of functionally related gene modules. The presented method allows automatic identification of all such components and evaluation of their significance in PCHi-C dataset for 17 haematopoietic cell types. The method can be adapted for exploration of other chromatin interaction data sets that include information about sufficiently large number of different cell types, and, in principle, also for analysis of other kinds of cell type-specific networks.

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Inference of cell type specific regulatory networks on mammalian lineages

Cited by 18 publications

References 113 publications

Subtype-specific regulatory network rewiring in acute myeloid leukemia

Subtype-specific regulatory network rewiring in acute myeloid leukemia

Subtype-specific regulatory network rewiring in acute myeloid leukemia

Topological structure analysis of chromatin interaction networks

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