Genome-Wide Technologies to Study RNA–Chromatin Interactions

Kato, Masaki; Carninci, Piero

doi:10.3390/ncrna6020020

Cited by 23 publications

(18 citation statements)

References 41 publications

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“…Recent genome-wide proteomic studies have identified many new RBPs [ 2 , 210 ]. Those newly identified RBPs are conserved from yeast to humans.…”

Section: Chromatin Associated Non-coding Rnas and Acting Modes In Chromatin Remodelingmentioning

confidence: 99%

“…Those newly identified RBPs are conserved from yeast to humans. Despite lacking the conventional or canonical RBDs, those new RBPs can modulate chromatin structure and gene expression in response to various environmental and intrinsic stimuli [ 2 , 210 , 211 , 212 , 213 ]. Both canonical and non-canonical RBPs are reported to function together to regulate transcription and chromatin structure [ 214 , 215 ].…”

Section: Chromatin Associated Non-coding Rnas and Acting Modes In Chromatin Remodelingmentioning

confidence: 99%

“…In eukaryotes, genomic DNA and its associated proteins, primarily histones, are organized into chromatin structural domains that affect the binding of transcription factors (TFs), recruitment of RNA polymerases (RNAPs) and the initiation and elongation of transcription [ 1 ]. The advent of new genome-wide sequencing technologies has identified chromatin-associated RNAs (ChrRNAs) [ 2 ], including nascent RNAs, coding RNAs and various regulatory non-coding RNAs (ncRNAs). RNAs can act either in cis by targeting genomic DNA or in trans through RNAP complexes and interactions with DNA-binding proteins, such as TFs and DNA/histone modifiers, to regulate chromatin structure and gene expression.…”

Section: Introductionmentioning

confidence: 99%

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RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases

Willbanks

Wood

Cheng

2021

Genes

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Chromatin structure plays an essential role in eukaryotic gene expression and cell identity. Traditionally, DNA and histone modifications have been the focus of chromatin regulation; however, recent molecular and imaging studies have revealed an intimate connection between RNA epigenetics and chromatin structure. Accumulating evidence suggests that RNA serves as the interplay between chromatin and the transcription and splicing machineries within the cell. Additionally, epigenetic modifications of nascent RNAs fine-tune these interactions to regulate gene expression at the co- and post-transcriptional levels in normal cell development and human diseases. This review will provide an overview of recent advances in the emerging field of RNA epigenetics, specifically the role of RNA modifications and RNA modifying proteins in chromatin remodeling, transcription activation and RNA processing, as well as translational implications in human diseases.

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“…Recent genome-wide proteomic studies have identified many new RBPs [ 2 , 210 ]. Those newly identified RBPs are conserved from yeast to humans.…”

Section: Chromatin Associated Non-coding Rnas and Acting Modes In Chromatin Remodelingmentioning

confidence: 99%

Section: Chromatin Associated Non-coding Rnas and Acting Modes In Chromatin Remodelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases

Willbanks

Wood

Cheng

2021

Genes

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“…However, a deep learning model can help identify such generic features in an ab-initio manner. When trained on existing known lncRNAs examples it can pick up similar patterns on screening the transcripts obtained in RNA-seq experiments en masse [30, 31].…”

Section: Introductionmentioning

confidence: 99%

linc2function: A deep learning model to identify and assign function to long noncoding RNA (lncRNA)

Ramakrishnaiah

Kuhlmann

Tyagi

2021

Preprint

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MotivationLncRNAs are much more versatile and are involved in many regulatory roles inside the cell than previously believed. Existing databases lack consistencies in lncRNA annotations, and the functionality of over 95% of the known lncRNAs are yet to be established. LncRNA transcript identification involves discriminating them from their coding counterparts, which can be done with traditional experimental approaches, or via in silico methods. The later approach employs various computational algorithms, including machine learning classifiers to predict the lncRNA forming potential of a given transcript. Such approaches provide an economical and faster alternative to the experimental methods. Current in silico methods mainly use primary-sequence based features to build predictive models limiting their accuracy and robustness. Moreover, many of these tools make use of reference genome based features, in consequence making them unsuitable for non-model species. Hence, there is a need to comprehensively evaluate the efficacy of different predictive features to build computational models. Additionally, effective models will have to provide maximum prediction performance using the least number of features in a species-agnostic manner.It is popularly known in the protein world that “structure is function”. This also applies to lncRNAs as their functional mechanisms are similar to those of proteins. Generally, lncRNA function by structurally binding to its target proteins or nucleic acid forming complexes. The secondary structures of the lncRNAs are modular providing interaction sites for their interactome made of DNA, RNA, and proteins. Through these interactions, they epigenetically regulate cellular biology, thereby forming a layer of genomic programming on top of the coding genes. We demonstrate that in addition to using transcript sequence, we can provide comprehensive functional annotation by collating their interactome and secondary structure information.ResultsHere, we evaluated an exhaustive list of sequence-based, secondary-structure, interactome, and physicochemical features for their ability to predict the lncRNA potential of a transcript. Based on our analysis, we built different machine learning models using optimum feature-set. We found our model to be on par or exceeding the execution of the state-of-the-art methods with AUC values of over 0.9 for a diverse collection of species tested. Finally, we built a pipeline called linc2function that provides the information necessary to functionally annotate a lncRNA conveniently in a single window.AvailabilityThe source code is accessible use under MIT license in standalone mode, and as a webserver (https://bioinformaticslab.erc.monash.edu/linc2function).

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“…Recent data implies that at least 80% of the human genome is transcribed, producing about 20,000 messenger RNAs (mRNAs), over 2300 microRNAs (miRNAs), a set of structural RNAs (such as ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), and small nuclear RNAs (snRNAs)), and at least 50,000 long noncoding RNAs (lncRNAs) [ 1 , 2 , 3 ]. LncRNAs comprise a diverse group of non-protein coding transcripts that are longer than 200 nucleotides in length, lack an open reading frame, and regulate gene expression at many levels by affecting transcription, cellular localization, mRNA stability, translation, and other post-transcriptional events [ 4 , 5 , 6 , 7 ]. As a result, lncRNAs regulate a broad range of biological processes, including aging, cell growth and differentiation, hematopoiesis, and immune response [ 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Long Non-Coding RNAs in Diffuse Large B-Cell Lymphoma

Karstensen

Schein

Petri

et al. 2020

ncRNA

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Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid malignancy in adults. Although significant progress has been made in recent years to treat DLBCL patients, 30%–40% of the patients eventually relapse or are refractory to first line treatment, calling for better therapeutic strategies for DLBCL. Long non-coding RNAs (lncRNAs) have emerged as a highly diverse group of non-protein coding transcripts with intriguing molecular functions in human disease, including cancer. Here, we review the current understanding of lncRNAs in the pathogenesis and progression of DLBCL to provide an overview of the field. As the current knowledge of lncRNAs in DLBCL is still in its infancy, we provide molecular signatures of lncRNAs in DLBCL cell lines to assist further lncRNA research in DLBCL.

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Genome-Wide Technologies to Study RNA–Chromatin Interactions

Cited by 23 publications

References 41 publications

RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases

RNA Epigenetics: Fine-Tuning Chromatin Plasticity and Transcriptional Regulation, and the Implications in Human Diseases

linc2function: A deep learning model to identify and assign function to long noncoding RNA (lncRNA)

Long Non-Coding RNAs in Diffuse Large B-Cell Lymphoma

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