17,357 research outputs found
TREEOME: A framework for epigenetic and transcriptomic data integration to explore regulatory interactions controlling transcription
Motivation: Predictive modelling of gene expression is a powerful framework
for the in silico exploration of transcriptional regulatory interactions
through the integration of high-throughput -omics data. A major limitation of
previous approaches is their inability to handle conditional and synergistic
interactions that emerge when collectively analysing genes subject to different
regulatory mechanisms. This limitation reduces overall predictive power and
thus the reliability of downstream biological inference.
Results: We introduce an analytical modelling framework (TREEOME: tree of
models of expression) that integrates epigenetic and transcriptomic data by
separating genes into putative regulatory classes. Current predictive modelling
approaches have found both DNA methylation and histone modification epigenetic
data to provide little or no improvement in accuracy of prediction of
transcript abundance despite, for example, distinct anti-correlation between
mRNA levels and promoter-localised DNA methylation. To improve on this, in
TREEOME we evaluate four possible methods of formulating gene-level DNA
methylation metrics, which provide a foundation for identifying gene-level
methylation events and subsequent differential analysis, whereas most previous
techniques operate at the level of individual CpG dinucleotides. We demonstrate
TREEOME by integrating gene-level DNA methylation (bisulfite-seq) and histone
modification (ChIP-seq) data to accurately predict genome-wide mRNA transcript
abundance (RNA-seq) for H1-hESC and GM12878 cell lines.
Availability: TREEOME is implemented using open-source software and made
available as a pre-configured bootable reference environment. All scripts and
data presented in this study are available online at
http://sourceforge.net/projects/budden2015treeome/.Comment: 14 pages, 6 figure
Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features.
Empirical evidence suggests that the malaria parasite Plasmodium falciparum employs a broad range of mechanisms to regulate gene transcription throughout the organism's complex life cycle. To better understand this regulatory machinery, we assembled a rich collection of genomic and epigenomic data sets, including information about transcription factor (TF) binding motifs, patterns of covalent histone modifications, nucleosome occupancy, GC content, and global 3D genome architecture. We used these data to train machine learning models to discriminate between high-expression and low-expression genes, focusing on three distinct stages of the red blood cell phase of the Plasmodium life cycle. Our results highlight the importance of histone modifications and 3D chromatin architecture in Plasmodium transcriptional regulation and suggest that AP2 transcription factors may play a limited regulatory role, perhaps operating in conjunction with epigenetic factors
Remotely acting SMCHD1 gene regulatory elements: in silico prediction and identification of potential regulatory variants in patients with FSHD
Background: Facioscapulohumeral dystrophy (FSHD) is commonly associated with contraction of the D4Z4 macro-satellite repeat on chromosome 4q35 (FSHD1) or mutations in the SMCHD1 gene (FSHD2). Recent studies have shown that the clinical manifestation of FSHD1 can be modified by mutations in the SMCHD1 gene within a given family. The absence of either D4Z4 contraction or SMCHD1 mutations in a small cohort of patients suggests that the disease could also be due to disruption of gene regulation. In this study, we postulated that mutations responsible for exerting a modifier effect on FSHD might reside within remotely acting regulatory elements that have the potential to interact at a distance with their cognate gene promoter via chromatin looping. To explore this postulate, genome-wide Hi-C data were used to identify genomic fragments displaying the strongest interaction with the SMCHD1 gene. These fragments were then narrowed down to shorter regions using ENCODE and FANTOM data on transcription factor binding sites and epigenetic marks characteristic of promoters, enhancers and silencers
Predicting variation of DNA shape preferences in protein-DNA interaction in cancer cells with a new biophysical model
DNA shape readout is an important mechanism of target site recognition by
transcription factors, in addition to the sequence readout. Several models of
transcription factor-DNA binding which consider DNA shape have been developed
in recent years. We present a new biophysical model of protein-DNA interaction
by considering the DNA shape features, which is based on a neighbour
dinucleotide dependency model BayesPI2. The parameters of the new model are
restricted to a subspace spanned by the 2-mer DNA shape features, which
allowing a biophysical interpretation of the new parameters as
position-dependent preferences towards certain values of the features. Using
the new model, we explore the variation of DNA shape preferences in several
transcription factors across cancer cell lines and cellular conditions. We find
evidence of DNA shape variations at FOXA1 binding sites in MCF7 cells after
treatment with steroids. The new model is useful for elucidating finer details
of transcription factor-DNA interaction. It may be used to improve the
prediction of cancer mutation effects in the future
Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors
We propose a method to predict yeast transcription factor targets by integrating histone modification profiles with transcription factor binding motif information. It shows improved predictive power compared to a binding motif-only method. We find that transcription factors cluster into histone-sensitive and -insensitive classes. The target genes of histone-sensitive transcription factors have stronger histone modification signals than those of histone-insensitive ones. The two classes also differ in tendency to interact with histone modifiers, degree of connectivity in protein-protein interaction networks, position in the transcriptional regulation hierarchy, and in a number of additional features, indicating possible differences in their transcriptional regulation mechanisms
AIP1 is a novel Agenet/Tudor domain protein from Arabidopsis that interacts with regulators of DNA replication, transcription and chromatin remodeling
Background: DNA replication and transcription are dynamic processes regulating plant development that are dependent on the chromatin accessibility. Proteins belonging to the Agenet/Tudor domain family are known as histone modification "readers" and classified as chromatin remodeling proteins. Histone modifications and chromatin remodeling have profound effects on gene expression as well as on DNA replication, but how these processes are integrated has not been completely elucidated. It is clear that members of the Agenet/Tudor family are important regulators of development playing roles not well known in plants.
Methods: Bioinformatics and phylogenetic analyses of the Agenet/Tudor Family domain in the plant kingdom were carried out with sequences from available complete genomes databases. 3D structure predictions of Agenet/Tudor domains were calculated by I-TASSER server. Protein interactions were tested in two-hybrid, GST pulldown, semi-in vivo pulldown and Tandem Affinity Purification assays. Gene function was studied in a T-DNA insertion GABI-line.
Results: In the present work we analyzed the family of Agenet/Tudor domain proteins in the plant kingdom and we mapped the organization of this family throughout plant evolution. Furthermore, we characterized a member from Arabidopsis thaliana named AIP1 that harbors Agenet/Tudor and DUF724 domains. AIP1 interacts with ABAP1, a plant regulator of DNA replication licensing and gene transcription, with a plant histone modification "reader" (LHP1) and with non modified histones. AIP1 is expressed in reproductive tissues and its down-regulation delays flower development timing. Also, expression of ABAP1 and LHP1 target genes were repressed in flower buds of plants with reduced levels of AIP1.
Conclusions: AIP1 is a novel Agenet/Tudor domain protein in plants that could act as a link between DNA replication, transcription and chromatin remodeling during flower development
Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data
Genome-wide association studies (GWASs) identify single nucleotide polymorphisms (SNPs) that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease
ModHMM: A Modular Supra-Bayesian Genome Segmentation Method
Genome segmentation methods are powerful tools to obtain cell type or tissue-specific genome-wide annotations and are frequently used to discover regulatory elements. However, traditional segmentation methods show low predictive accuracy and their data-driven annotations have some undesirable properties. As an alternative, we developed ModHMM, a highly modular genome segmentation method. Inspired by the supra-Bayesian approach, it incorporates predictions from a set of classifiers. This allows to compute genome segmentations by utilizing state-of-the-art methodology. We demonstrate the method on ENCODE data and show that it outperforms traditional segmentation methods not only in terms of predictive performance, but also in qualitative aspects. Therefore, ModHMM is a valuable alternative to study the epigenetic and regulatory landscape across and within cell types or tissues
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