Understanding Long Noncoding RNA and Chromatin Interactions: What We Know So Far

Mishra, Kankadeb; Kanduri, Chandrasekhar

doi:10.3390/ncrna5040054

Cited by 72 publications

(61 citation statements)

References 165 publications

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“…Although a small subset of total lncRNAs are known to play essential roles, many of them are involved in chromatin regulation by recruiting chromatin modifying proteins [54,55] (Table 1). LncRNAs exhibit properties that make them potential candidates for acting as architectural elements for chromatin organization [6].…”

Section: Candidate Architectural Lncrnas Involved In Chromatin Organimentioning

confidence: 99%

“…-Recruit the dosage-compensation complex on the male X chromosome in a cell-type-specific fashion in Drosophila. [85,86] complexes, might act as bridges between two chromatin contact points [55]. RNA-RNA interactions are essential for maintaining ribonucleoprotein particles along with RNA-RBP interactions and can therefore play an important role in chromatin organization as well [74,75].…”

Section: Coldair -Represses a Floral Repressor Flowering Locus C (Flcmentioning

confidence: 99%

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Architectural RNA in chromatin organization

Thakur

Henikoff

2020

Biochemical Society Transactions

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RNA plays a well-established architectural role in the formation of membraneless interchromatin nuclear bodies. However, a less well-known role of RNA is in organizing chromatin, whereby specific RNAs have been found to recruit chromatin modifier proteins. Whether or not RNA can act as an architectural molecule for chromatin remains unclear, partly because dissecting the architectural role of RNA from its regulatory role remains challenging. Studies that have addressed RNA's architectural role in chromatin organization rely on in situ RNA depletion using Ribonuclease A (RNase A) and suggest that RNA plays a major direct architectural role in chromatin organization. In this review, we will discuss these findings, candidate chromatin architectural long non-coding RNAs and possible mechanisms by which RNA, along with RNA binding proteins might be mediating chromatin organization.

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Section: Candidate Architectural Lncrnas Involved In Chromatin Organimentioning

confidence: 99%

Section: Coldair -Represses a Floral Repressor Flowering Locus C (Flcmentioning

confidence: 99%

Architectural RNA in chromatin organization

Thakur

Henikoff

2020

Biochemical Society Transactions

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“…The lncRNA can form triplex structures with DNA both in cis and in trans, but if differences exist between cis and trans targeting of triplexes lncRNAs remains unknown [27]. In this work, we explore the cis and trans targeting of triplexes lncRNAs following two data sources.…”

Section: In Cis / In Trans Triplex Forming Lncrna: An Explorationmentioning

confidence: 99%

Deep learning based DNA:RNA triplex forming potential prediction

Zhang

Long

Kwoh

2020

Preprint

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Background Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: i) they identify a large number of triplex forming lncRNAs, but the limited number of experimental verified triplex forming lncRNA indicate that maybe not all of them can from triplex in practice, and ii) their prediction only consider the theoretical relationship while lacking the features from the experimentally verified data. Results In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex forming lncRNAs and DNA sites based on the experimentally verified data, where their high-level features are learned by the deep neural networks. In the 5-fold cross validation, its average values of Area Under the ROC curves and PRC curves for triplex forming lncRNA and DNA sites predictions are 0.9949 and 0.9999, 0.8775 and 0.9692, respectively. Besides, we also briefly summarized the cis and trans targeting of triplexes lncRNAs. Conclusions The TriplexFPP can predict the most likely triplex forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities, and predict the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

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“…Take the first fold validation in two models in TriplexFPP as examples, we plot the average feature values for each class in the format of heatmap before training (original features), trained after one CNN layer, and trained after two CNN layers in Fig. 4 and Additional file 1: The lncRNA can form triplex structures with DNA both in cis and in trans, but if differences exist between cis and trans targeting of triplexes lncRNAs remains unknown [27]. In this work, we explore the cis and trans targeting of triplexes lncRNAs following two data sources.…”

Section: Triplexfpp Model Interpretationmentioning

confidence: 99%

Deep learning based DNA:RNA triplex forming potential prediction

Zhang

Long

Kwoh³

2020

Preprint

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Background: Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: i) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and ii) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data.Results: In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the 5-fold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the cis and trans targeting of triplexes lncRNAs. Conclusions: The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.

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Understanding Long Noncoding RNA and Chromatin Interactions: What We Know So Far

Cited by 72 publications

References 165 publications

Architectural RNA in chromatin organization

Architectural RNA in chromatin organization

Deep learning based DNA:RNA triplex forming potential prediction

Deep learning based DNA:RNA triplex forming potential prediction

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