Efficient Algorithms for Finding Edit-Distance Based Motifs

Xiao, Peng; Cai, Xingyu; Rajasekaran, Sanguthevar

doi:10.1007/978-3-030-18174-1_16

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Many computational methods such as MEME ( 58 ), LDDMS ( 59 ) and EMS3 ( 60 ) have been developed to efficiently explore the putative patterns hidden in biological sequences. In this study, the widely used and readily accessible MEME ( http://meme-suite.org/tools/meme ) with the differential enrichment mode ( 58 ) was applied to the identification of candidate RNA motifs enriched in the positive instances relative to the negative instances in the human U2OS dataset.…”

Section: Methodsmentioning

confidence: 99%

Identification and analysis of consensus RNA motifs binding to the genome regulator CTCF

Kuang

2020

NAR Genomics and Bioinformatics

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CCCTC-binding factor (CTCF) is a key regulator of 3D genome organization and gene expression. Recent studies suggest that RNA transcripts, mostly long non-coding RNAs (lncRNAs), can serve as locus-specific factors to bind and recruit CTCF to the chromatin. However, it remains unclear whether specific sequence patterns are shared by the CTCF-binding RNA sites, and no RNA motif has been reported so far for CTCF binding. In this study, we have developed DeepLncCTCF, a new deep learning model based on a convolutional neural network and a bidirectional long short-term memory network, to discover the RNA recognition patterns of CTCF and identify candidate lncRNAs binding to CTCF. When evaluated on two different datasets, human U2OS dataset and mouse ESC dataset, DeepLncCTCF was shown to be able to accurately predict CTCF-binding RNA sites from nucleotide sequence. By examining the sequence features learned by DeepLncCTCF, we discovered a novel RNA motif with the consensus sequence, AGAUNGGA, for potential CTCF binding in humans. Furthermore, the applicability of DeepLncCTCF was demonstrated by identifying nearly 5000 candidate lncRNAs that might bind to CTCF in the nucleus. Our results provide useful information for understanding the molecular mechanisms of CTCF function in 3D genome organization.

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Section: Methodsmentioning

confidence: 99%

Identification and analysis of consensus RNA motifs binding to the genome regulator CTCF

Kuang

2020

NAR Genomics and Bioinformatics

View full text Add to dashboard Cite

show abstract

Efficient Algorithms for Finding Edit-Distance Based Motifs

Cited by 1 publication

References 17 publications

Identification and analysis of consensus RNA motifs binding to the genome regulator CTCF

Identification and analysis of consensus RNA motifs binding to the genome regulator CTCF

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