scholarly journals Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis

2018 ◽  
Author(s):  
Denise Thiel ◽  
Nataša Djurdjevac Conrad ◽  
Ria X Peschutter ◽  
Heike Siebert ◽  
Annalisa Marsico
Keyword(s):  
Transcriptional Regulation ◽  
Rna Polymerase Ii ◽  
Expression Analysis ◽  
Unknown Function ◽  
Interaction Network ◽  
Chromatin Interaction ◽  
Centrality Measures ◽  
Regulatory Modules ◽  
Chromatin Interactions ◽  
State Models

AbstractBackgroundAlthough several studies have provided insights into the role of long non-coding RNAs (lncRNAs), the majority of them has unknown function. Recent evidence has shown the importance of both lncR-NAs and chromatin interactions in transcriptional regulation. Although network-based methods, mainly exploiting gene-lncRNA co-expression, have been applied to characterize lncRNA of unknown function by means of ‘guilt-by-association’ strategies, no method exists which combines co-expression analysis with 3D chromatin interaction data.ResultsTo better understand the function of chromatin interactions in the context of lncRNA-mediated gene regulation, we have developed a multi-step graph analysis approach to examine the RNA polymerase II ChIA-PET chromatin interaction network in the K562 human cell line. We have annotated the network with gene and lncRNA coordinates, and chromatin states from the ENCODE project. We used centrality measures, as well as an adaptation of our previously developed Markov State Models (MSM) clustering method, to gain a better understanding of lncRNAs in transcriptional regulation. The novelty of our approach resides into the detection of fuzzy regulatory modules based on network properties and their optimization based on co-expression analysis between genes and gene-lncRNA pairs. This results in our method returning morebona fideregulatory modules than other state-of-the art approaches for clustering on graphs.ConclusionsInterestingly, we find that lncRNA network hubs tend to be significantly enriched in disease association, positional conservation and enhancer-like functions. We validated regulatory functions for well known lncRNAs, such as MALAT1 and the enhancer-like lncRNA FALEC. In addition, by investigating the modular structure of bigger components we show that we can propose regulatory functional mechanisms for uncharacterized lncRNAs, such FLJ37453, RP11442N24 B.1 and LINC00910.

scholarly journals Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Wufeng Fan ◽  
Yuhan Zhou ◽  
Hao Li
Keyword(s):  
Listeria Monocytogenes ◽  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Interaction Network ◽  
Principal Component ◽  
Centrality Measures ◽  
Network Centrality ◽  
Human Monocytes ◽  
Pathway Interaction ◽  
Pathway Interaction Network

In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.

scholarly journals Discovery of directional chromatin-associated regulatory motifs affecting human gene transcription

2018 ◽  
Author(s):  
Naoki Osato
Keyword(s):  
Transcriptional Regulation ◽  
Dna Binding ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Expression Level ◽  
Chromatin Interaction ◽  
Binding Motifs ◽  
Chromatin Interactions ◽  
Dna Binding Motifs ◽  
Associated Functions

AbstractBackgroundChromatin interactions are essential in enhancer-promoter interactions (EPIs) and transcriptional regulation. CTCF and cohesin proteins located at chromatin interaction anchors and other DNA-binding proteins such as YY1, ZNF143, and SMARCA4 are involved in chromatin interactions. However, there is still no good overall understanding of proteins associated with chromatin interactions and insulator functions.ResultsHere, I describe a systematic and comprehensive approach for discovering DNA-binding motifs of transcription factors (TFs) that affect EPIs and gene expression. This analysis identified 96 biased orientations [64 forward-reverse (FR) and 52 reverse-forward (RF)] of motifs that significantly affected the expression level of putative transcriptional target genes in monocytes, T cells, HMEC, and NPC and included CTCF, cohesin (RAD21 and SMC3), YY1, and ZNF143; some TFs have more than one motif in databases; thus, the total number is smaller than the sum of FRs and RFs. KLF4, ERG, RFX, RFX2, HIF1, SP1, STAT3, and AP1 were associated with chromatin interactions. Many other TFs were also known to have chromatin-associated functions. The predicted biased orientations of motifs were compared with chromatin interaction data. Correlations in expression level of nearby genes separated by the motif sites were then examined among 53 tissues.ConclusionOne hundred FR and RF orientations associated with chromatin interactions and functions were discovered. Most TFs showed weak directional biases at chromatin interaction anchors and were difficult to identify using enrichment analysis of motifs. These findings contribute to the understanding of chromatin-associated motifs involved in transcriptional regulation, chromatin interactions/regulation, and histone modifications.

scholarly journals Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fan Cao ◽  
Yu Zhang ◽  
Yichao Cai ◽  
Sambhavi Animesh ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
Lymphocytic Leukemia ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Interaction Prediction ◽  
Chromatin Interactions ◽  
Genome Wide

AbstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

scholarly journals Predicting chromatin interactions between open chromatin regions from DNA sequences

2019 ◽  
Author(s):  
Fan Cao ◽  
Ying Zhang ◽  
Yan Ping Loh ◽  
Yichao Cai ◽  
Melissa J. Fullwood
Keyword(s):  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
State Of The Art ◽  
Lymphocytic Leukemia ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Rna Seq ◽  
Chromatin Interactions ◽  
Genome Wide

AbstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is very limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the ‘state-of-the-art’ methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (CHINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. CHINN is able to predict CTCF- and RNA polymerase II-associated chromatin interactions between open chromatin regions. CHINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. We applied CHINN to 84 chronic lymphocytic leukemia (CLL) samples and detected systematic differences in the chromatin interactome between IGVH-mutated and IGVH-unmutated CLL samples.

scholarly journals Chromatin Interaction Analysis with Updated ChIA-PET Tool (V3)

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 554 ◽  
Author(s):  
Li ◽  
Sun ◽  
Chang ◽  
Cai ◽  
Hong ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Target Genes ◽  
Sequence Data ◽  
Interaction Analysis ◽  
Regulatory Elements ◽  
Chromatin Interaction ◽  
Data Set ◽  
Chromatin Interactions ◽  
A Genome ◽  
Public Data

Understanding chromatin interactions is important because they create chromosome conformation and link the cis- and trans- regulatory elements to their target genes for transcriptional regulation. Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing is a genome-wide high-throughput technology that detects chromatin interactions associated with a specific protein of interest. We developed ChIA-PET Tool for ChIA-PET data analysis in 2010. Here, we present the updated version of ChIA-PET Tool (V3) as a computational package to process the next-generation sequence data generated from ChIA-PET experiments. It processes short-read and long-read ChIA-PET data with multithreading and generates statistics of results in an HTML file. In this paper, we provide a detailed demonstration of the design of ChIA-PET Tool V3 and how to install it and analyze RNA polymerase II (RNAPII) ChIA-PET data from human K562 cells with it. We compared our tool with existing tools, including ChiaSig, MICC, Mango and ChIA-PET2, by using the same public data set in the same computer. Most peaks detected by the ChIA-PET Tool V3 overlap with those of other tools. There is higher enrichment for significant chromatin interactions from ChIA-PET Tool V3 in aggregate peak analysis (APA) plots. The ChIA-PET Tool V3 is publicly available at GitHub.

scholarly journals Chromatin Interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences

2020 ◽  
Author(s):  
Fan Cao ◽  
Yu Zhang ◽  
Yichao Cai ◽  
Sambhavi Animesh ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Rna Polymerase Ii ◽  
Dna Sequences ◽  
Lymphocytic Leukemia ◽  
Computational Method ◽  
Chromatin Interaction ◽  
Open Chromatin ◽  
Clinical Patient ◽  
Chromatin Interactions ◽  
Genome Wide

AbstractChromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the ‘state-of-the-art’ methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (ChINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. ChINN is able to predict CTCF-, RNA polymerase II- and HiC-associated chromatin interactions between open chromatin regions. ChINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. To apply our results to clinical patient data, we applied CHINN to predict chromatin interactions in 6 chronic lymphocytic leukemia (CLL) patient samples and a cohort of open chromatin data from 84 CLL samples that was previously published. Our results demonstrated extensive heterogeneity in chromatin interactions in patient samples, and one of the sources of this heterogeneity were the different subtypes of CLL.

scholarly journals SAFE Hi-C: A Simplified, Amplification-free and Economically Efficient High-throughput Chromosome Conformation Capturing Method

2019 ◽  
Author(s):  
Longjian Niu ◽  
Yingzhang Huang ◽  
Chunhui Hou
Keyword(s):  
Three Dimensional ◽  
Pcr Amplification ◽  
Interaction Network ◽  
Comprehensive Analysis ◽  
Chromatin Interaction ◽  
Library Preparation ◽  
Chromosome Conformation ◽  
Amplification Bias ◽  
Chromatin Interactions ◽  
Genomic Architecture

Abstract PCR amplification of Hi-C libraries introduces unusable duplicates and results in a biased representation of chromatin interactions. We present a simplified, fast, and economically efficient Hi-C library preparation procedure that generates sufficient non-amplified ligation products for deep sequencing. Comprehensive analysis of the resulting data indicates that amplification-free Hi-C preserves higher complexity of chromatin interaction and lowers sequencing depth dramatically for the same number of unique paired reads. With amplification bias avoided, our method may produce a chromatin interaction network more faithfully reflecting the real three-dimensional genomic architecture.

scholarly journals Amplification-free Library Preparation Improves Quality of Hi-C Analysis

2019 ◽  
Author(s):  
Longjian Niu ◽  
Wei Shen ◽  
Yingzhang Huang ◽  
Na He ◽  
Yuedong Zhang ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Three Dimensional ◽  
Pcr Amplification ◽  
Interaction Network ◽  
Human Cells ◽  
Chromatin Interaction ◽  
Library Preparation ◽  
Amplification Bias ◽  
Chromatin Interactions

AbstractPCR amplification of Hi-C libraries introduces unusable duplicates and results in a biased representation of chromatin interactions. We present a simplified, fast, and economically efficient Hi-C library preparation procedure that generates sufficient non-amplified ligation products for deep sequencing from 30 million Drosophila cells. Comprehensive analysis of the resulting data indicates that amplification-free Hi-C preserves higher complexity of chromatin interaction and lowers sequencing depth dramatically for the same number of unique paired reads. For human cells which has a large genome, this method recovers an amount of ligated fragments enough for direct high-throughput sequencing without amplification on as low as 250 thousand of cells. Comparison with published in situ Hi-C on millions of human cells reveals that amplification introduces distance-dependent amplification bias, which results in increasing background noise level against genomic distance. With amplification bias avoided, our method may produce a chromatin interaction network more faithfully reflecting the real three-dimensional genomic architecture.

scholarly journals Large-scale multi-omics analysis suggests specific roles for intragenic cohesin in transcriptional regulation

2021 ◽  
Author(s):  
Jiankang Wang ◽  
Masashige Bando ◽  
Katsuhiko Shirahige ◽  
Ryuichiro Nakato
Keyword(s):  
Transcriptional Regulation ◽  
Rna Polymerase Ii ◽  
Binding Sites ◽  
Large Scale ◽  
Gene Activation ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Context Specific ◽  
Comprehensive Characterization

Cohesin, an essential protein complex for chromosome segregation, regulates transcription through a variety of mechanisms. It is not a trivial task to genome-widely assign the diverse cohesin functions. Moreover, the context-specific roles of cohesin-mediated interactions, especially on intragenic regions, have not been thoroughly investigated. Here we performed a comprehensive characterization of cohesin binding sites in several human cell types. We integrated epigenomic, transcriptomic and chromatin interaction data with and without transcriptional stimulation, to explore context-specific functions of intragenic cohesin related to gene activation. We identified a new subset of cohesin binding sites, decreased intragenic cohesin sites (DICs), which have a different function from previously known ones. The intron-enriched DICs were negatively correlated with transcriptional regulation: a subgroup of DICs were related to enhancer markers and paused RNA polymerase II, whereas others contributed to chromatin architecture. We implemented machine learning and successfully isolated DICs with distinct genomic features. We observed DICs in various cell types, including cells from cohesinopathy patients. These results suggest a previously unidentified function of cohesin at intragenic regions for transcription regulation.

scholarly journals Genome-wide Identification and Expression Analysis of Autophagy-related Genes (ATGs) in Medicago Truncatula

2020 ◽  
Author(s):  
Mingkang Yang ◽  
Liping Wang ◽  
Xu Guo ◽  
Chuanglie Lin ◽  
Wei Huang ◽  
...  
Keyword(s):  
Medicago Truncatula ◽  
Expression Analysis ◽  
Interaction Network ◽  
Degradation Process ◽  
Abiotic Stress Response ◽  
Biotic And Abiotic Stress ◽  
Comprehensive Overview ◽  
Genome Wide ◽  
Evolutionarily Conserved ◽  
Gene Structures

Abstract Background: Autophagy is a highly conserved degradation process of cytoplasmic constituents in eukaryotes. Autophagy is known to be involved in the regulation of plant growth and development, as well as biotic and abiotic stress response. Although autophagy-related genes (ATGs) have been identified and characterized in many plant species, little is known about the autophagy process in Medicago truncatula. Results: In this study, 39 ATGs were identified in M. truncatula (MtATGs), and the gene structures and conserved domains of MtATGs were systematically characterized. In addition, many cis-elements which are related to hormone and stress responsiveness were identified in the promoters of MtATGs. Furthermore, phylogenetic analysis and interaction network analysis suggested that the function of MtATGs is evolutionarily conserved in Arabidopsis and M. truncatula. Gene expression analysis showed that most MtATGs were largely induced during seed development, but repressed by nodulation. Moreover, MtATGs were up-regulated in response to salt and drought stresses.Conclusion: These results provide a comprehensive overview of the MtATGs, which provided important clues for further functional analysis of autophagy in M. truncatula.

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