Reconstruction of Histone Modification Network from Next-generation Sequencing Data

Le, Ngoc Tu; Ho, Tu Bao

doi:10.1109/bibe.2011.35

Cited by 5 publications

(3 citation statements)

References 37 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First introduced to model gene regulatory networks (GRNs) from expression data [20], it has been widely used in reconstructing various biological networks, such as protein-protein interactions, protein signaling networks [21–23]. Likewise, there have been attempts to employ BNs to analyze histone modification data, in which compelled edges of the resulting models were considered causal relationships between PTMs [14, 24, 25]. Though useful, these works have a significant drawback: they require causal sufficiency assumption, i.e., all confounders of PTMs should be observed [26, 27].…”

Section: Introductionmentioning

confidence: 99%

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation

et al. 2014

View full text Add to dashboard Cite

BackgroundThe rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes.ResultsHere we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/.ConclusionsNeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.

show abstract

Section: Introductionmentioning

confidence: 99%

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Computational methods that have been used to study epigenetic mechanisms include, for instance, Bayesian networks, sparse partial correlation networks and maximum entropy framework [13,12,18,26]. Yu et al [25] have also combined gene expression data analysis with histone modification networks and theoretical investigations of histone modification networks have also been proposed in [9].…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation

Nousiainen

Intosalmi

Lähdesmäki

2019

Algorithms for Computational Biology

View full text Add to dashboard Cite

Cell differentiation and development are for a great part steered by cell type specific enhancers. Transcription factor (TF) binding to an enhancer together with DNA looping result in transcription initiation. In addition to binding motifs for TFs, enhancer regions typically contain specific histone modifications. This information has been used to detect enhancer regions and classify them into different subgroups. However, it is poorly understood how TF binding and histone modifications are causally connected and what kind of molecular dynamics steer the activation process. Contrary to previous studies, we do not treat the activation events as static epigenetic marks but consider the enhancer activation as a dynamic process. We develop a mathematical model to describe the dynamic mechanisms between TF binding and histone modifications known to characterize an active enhancer. We estimate model parameters from time-course data and infer the causal relationships between TF binding and different histone modifications. We benchmark the performance of this framework using simulated data and survey the ability of our method to identify the correct causal mechanisms for a variety of system dynamics, noise levels and the number of measurement time points.

show abstract

“…First introduced to model gene regulatory networks (GRNs) from expression data [ 20 ], it has been widely used in reconstructing various biological networks, such as protein-protein interactions, protein signaling networks [ 21 – 23 ]. Likewise, there have been attempts to employ BNs to analyze histone modification data, in which compelled edges of the resulting models were considered causal relationships between PTMs [ 14 , 24 , 25 ]. Though useful, these works have a significant drawback: they require causal sufficiency assumption, i.e., all confounders of PTMs should be observed [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

A nucleosomal approach to inferring causal relationships of histone modifications

et al. 2014

BMC Genomics

View full text Add to dashboard Cite

MotivationHistone proteins are subject to various posttranslational modifications (PTMs). Elucidating their functional relationships is crucial toward understanding many biological processes. Bayesian network (BN)-based approaches have shown the advantage of revealing causal relationships, rather than simple cooccurrences, of PTMs. Previous works employing BNs to infer causal relationships of PTMs require that all confounders should be included. This assumption, however, is unavoidably violated given the fact that several modifications are often regulated by a common but unobserved factor. An existing non-parametric method can be applied to tackle the problem but the complexity and inflexibility make it impractical.ResultsWe propose a novel BN-based method to infer causal relationships of histone modifications. First, from the evidence that nucleosome organization in vivo significantly affects the activities of PTM regulators working on chromatin substrate, hidden confounders of PTMs are selectively introduced by an information-theoretic criterion. Causal relationships are then inferred from a network model of both PTMs and the derived confounders. Application on human epigenomic data shows the advantage of the proposed method, in terms of computational performance and support from literature. Requiring less strict data assumptions also makes it more practical. Interestingly, analysis of the most significant relationships suggests that the proposed method can recover biologically relevant causal effects between histone modifications, which should be important for future investigation of histone crosstalk.

show abstract

Reconstruction of Histone Modification Network from Next-generation Sequencing Data

Cited by 5 publications

References 37 publications

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation

A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation

A nucleosomal approach to inferring causal relationships of histone modifications

Contact Info

Product

Resources

About