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CLoops2: a full-stack comprehensive analytical tool for chromatin interactions.

ABSTRACT: Investigating chromatin interactions between regulatory regions such as enhancer and promoter elements is vital for understanding the regulation of gene expression. Compared to Hi-C and its variants, the emerging 3D mapping technologies focusing on enriched signals, such as TrAC-looping, reduce the sequencing cost and provide higher interaction resolution for cis-regulatory elements. A robust pipeline is needed for the comprehensive interpretation of these data, especially for loop-centric analysis. Therefore, we have developed a new versatile tool named cLoops2 for the full-stack analysis of these 3D chromatin interaction data. cLoops2 consists of core modules for peak-calling, loop-calling, differentially enriched loops calling and loops annotation. It also contains multiple modules for interaction resolution estimation, data similarity estimation, features quantification, feature aggregation analysis, and visualization. cLoops2 with documentation and example data are open source and freely available at GitHub: https://github.com/KejiZhaoLab/cLoops2.

SUBMITTER: Cao Y

PROVIDER: S-EPMC8754654 | biostudies-literature |

REPOSITORIES: biostudies-literature

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cLoops2: a full-stack comprehensive analytical tool for chromatin interactions

Project description:Investigating chromatin interactions between regulatory regions such as enhancer and promoter elements is pivotal for a deeper understanding of gene expression regulation. The emerging 3D mapping technologies focusing on enriched signals such as Hi-TrAC/Trac-looping and HiChIP, compared to Hi-C, reduce the sequencing cost, and provide higher interaction resolution for cis-regulatory elements and more comprehensive information such as chromatin accessibility. A robust pipeline is needed for the comprehensive interpretation of these data, especially for loop-centric analysis. Therefore, we have developed a new versatile tool named cLoops2 for the full-stack analysis of the 3D chromatin interaction data. cLoops2 consists of core modules of peak-calling, loop-calling, differentially enriched loops calling and loops annotation. Additionally, it also contains multiple modules to carry out interaction resolution estimation, data similarity estimation, features quantification and aggregation analysis, and visualization. cLoops2 with documentation and example data are open source and freely available at GitHub: https://github.com/YaqiangCao/cLoops2

2021-11-29 | GSE179010 | GEO

cLoops2: a full-stack comprehensive analytical tool for chromatin interactions

Project description:cLoops2: a full-stack comprehensive analytical tool for chromatin interactions

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4DGenome: a comprehensive database of chromatin interactions.

Project description:MotivationThe 3D structure of the genome plays a critical role in regulating gene expression. Recent progress in mapping technologies for chromatin interactions has led to a rapid increase in this kind of interaction data. This trend will continue as research in this burgeoning field intensifies.ResultsWe describe the 4DGenome database that stores chromatin interaction data compiled through comprehensive literature curation. The database currently covers both low- and high-throughput assays, including 3C, 4C-Seq, 5C, Hi-C, ChIA-PET and Capture-C. To complement the set of interactions detected by experimental assays, we also include interactions predicted by a recently developed computational method with demonstrated high accuracy. The database currently contains ∼8 million records, covering 102 cell/tissue types in five organisms. Records in the database are described using a standardized file format, facilitating data exchange. The vast major of the interactions were assigned a confidence score. Using the web interface, users can query and download database records via a number of annotation dimensions. Query results can be visualized along with other genomics datasets via links to the UCSC genome browser. We anticipate that 4DGenome will be a valuable resource for investigating the spatial structure-and-function relationship of genomes.

| S-EPMC4514924 | biostudies-literature

GRID-seq for comprehensive analysis of global RNA-chromatin interactions.

Project description:Chromatin in higher eukaryotic nuclei is extensively bound by various RNA species. We recently developed a method for in situ capture of global RNA interactions with DNA by deep sequencing (GRID-seq) of fixed permeabilized nuclei that allows identification of the entire repertoire of chromatin-associated RNAs in an unbiased manner. The experimental design of GRID-seq is related to those of two recently published strategies (MARGI (mapping RNA-genome interactions) and ChAR-seq (chromatin-associated RNA sequencing)), which also use a bivalent linker to ligate RNA and DNA in proximity. Importantly, however, GRID-seq also implements a combined experimental and computational approach to control nonspecific RNA-DNA interactions that are likely to occur during library construction, which is critical for accurate interpretation of detected RNA-DNA interactions. GRID-seq typically finds both coding and non-coding RNAs (ncRNAs) that interact with tissue-specific promoters and enhancers, especially super-enhancers, from which a global promoter-enhancer connectivity map can be deduced. Here, we provide a detailed protocol for GRID-seq that includes nuclei preparation, chromatin fragmentation, RNA and DNA in situ ligation with a bivalent linker, PCR amplification and high-throughput sequencing. To further enhance the utility of GRID-seq, we include a pipeline for data analysis, called GridTools, into which key steps such as background correction and inference of genomic element proximity are integrated. For researchers experienced in molecular biology with minimal bioinformatics skills, the protocol typically takes 4-5 d from cell fixation to library construction and 2-3 d for data processing.

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STAR3D: a stack-based RNA 3D structural alignment tool.

Project description:The various roles of versatile non-coding RNAs typically require the attainment of complex high-order structures. Therefore, comparing the 3D structures of RNA molecules can yield in-depth understanding of their functional conservation and evolutionary history. Recently, many powerful tools have been developed to align RNA 3D structures. Although some methods rely on both backbone conformations and base pairing interactions, none of them consider the entire hierarchical formation of the RNA secondary structure. One of the major issues is that directly applying the algorithms of matching 2D structures to the 3D coordinates is particularly time-consuming. In this article, we propose a novel RNA 3D structural alignment tool, STAR3D, to take into full account the 2D relations between stacks without the complicated comparison of secondary structures. First, the 3D conserved stacks in the inputs are identified and then combined into a tree-like consensus. Afterward, the loop regions are compared one-to-one in accordance with their relative positions in the consensus tree. The experimental results show that the prediction of STAR3D is more accurate for both non-homologous and homologous RNAs than other state-of-the-art tools with shorter running time.

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LocalSTAR3D: a local stack-based RNA 3D structural alignment tool.

Project description:A fast-growing number of non-coding RNA structures have been resolved and deposited in Protein Data Bank (PDB). In contrast to the wide range of global alignment and motif search tools, there is still a lack of local alignment tools. Among all the global alignment tools for RNA 3D structures, STAR3D has become a valuable tool for its unprecedented speed and accuracy. STAR3D compares the 3D structures of RNA molecules using consecutive base-pairs (stacks) as anchors and generates an optimal global alignment. In this article, we developed a local RNA 3D structural alignment tool, named LocalSTAR3D, which was extended from STAR3D and designed to report multiple local alignments between two RNAs. The benchmarking results show that LocalSTAR3D has better accuracy and coverage than other local alignment tools. Furthermore, the utility of this tool has been demonstrated by rediscovering kink-turn motif instances, conserved domains in group II intron RNAs, and the tRNA mimicry of IRES RNAs.

| S-EPMC7367197 | biostudies-literature

ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing.

Project description:Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) is a new technology to study genome-wide long-range chromatin interactions bound by protein factors. Here we present ChIA-PET Tool, a software package for automatic processing of ChIA-PET sequence data, including linker filtering, mapping tags to reference genomes, identifying protein binding sites and chromatin interactions, and displaying the results on a graphical genome browser. ChIA-PET Tool is fast, accurate, comprehensive, user-friendly, and open source (available at http://chiapet.gis.a-star.edu.sg).

| S-EPMC2872882 | biostudies-literature

Comprehensive Map of the Regulated Cell Death Signaling Network: A Powerful Analytical Tool for Studying Diseases.

Project description:The processes leading to, or avoiding cell death are widely studied, because of their frequent perturbation in various diseases. Cell death occurs in three highly interconnected steps: Initiation, signaling and execution. We used a systems biology approach to gather information about all known modes of regulated cell death (RCD). Based on the experimental data retrieved from literature by manual curation, we graphically depicted the biological processes involved in RCD in the form of a seamless comprehensive signaling network map. The molecular mechanisms of each RCD mode are represented in detail. The RCD network map is divided into 26 functional modules that can be visualized contextually in the whole seamless network, as well as in individual diagrams. The resource is freely available and accessible via several web platforms for map navigation, data integration, and analysis. The RCD network map was employed for interpreting the functional differences in cell death regulation between Alzheimer's disease and non-small cell lung cancer based on gene expression data that allowed emphasizing the molecular mechanisms underlying the inverse comorbidity between the two pathologies. In addition, the map was used for the analysis of genomic and transcriptomic data from ovarian cancer patients that provided RCD map-based signatures of four distinct tumor subtypes and highlighted the difference in regulations of cell death molecular mechanisms.

| S-EPMC7226067 | biostudies-literature

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring.

Project description:Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review.MethodsWe developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning-based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients.ResultsWe achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning-assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.

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