Dataset Information

CircDeep: deep learning approach for circular RNA classification from other long non-coding RNA.

ABSTRACT: MOTIVATION:Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is vital to understanding their biogenesis and purpose. Prediction of circular RNA can be achieved in three steps: distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs and predicting circular RNAs from other long non-coding RNAs (lncRNAs). However, the available tools are less than 80 percent accurate for distinguishing circular RNAs from other lncRNAs due to difficulty of classification. Therefore, the availability of a more accurate and fast machine learning method for the identification of circular RNAs, which considers the specific features of circular RNA, is essential to the development of systematic annotation. RESULTS:Here we present an End-to-End deep learning framework, circDeep, to classify circular RNA from other lncRNA. circDeep fuses an RCM descriptor, ACNN-BLSTM sequence descriptor and a conservation descriptor into high level abstraction descriptors, where the shared representations across different modalities are integrated. The experiments show that circDeep is not only faster than existing tools but also performs at an unprecedented level of accuracy by achieving a 12 percent increase in accuracy over the other tools. AVAILABILITY AND IMPLEMENTATION:https://github.com/UofLBioinformatics/circDeep. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Chaabane M

PROVIDER: S-EPMC6956777 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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circDeep: deep learning approach for circular RNA classification from other long non-coding RNA.

Chaabane Mohamed M Williams Robert M RM Stephens Austin T AT Park Juw Won JW

Bioinformatics (Oxford, England) 20200101 1

<h4>Motivation</h4>Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is vital to understanding their biogenesis and purpose. Prediction of circular RNA can be achieved in three steps: distinguishing non-coding RNAs from protein coding ge ...[more]

PMID: 31268128

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Project description:The human malaria parasite Plasmodium falciparum has a complex and multi-stage life cycle that requires extensive and precise gene regulation to allow invasion and hijacking of host cells, transmission, and immune escape. To date, the regulatory elements orchestrating these critical parasite processes remain largely unknown. Yet it is becoming increasingly clear that long non-coding RNAs (lncRNAs) could represent a missing regulatory layer across a broad range of organisms.To investigate the regulatory capacity of lncRNA in P. falciparum, we harvested fifteen samples from two time-courses. Our sample set profiled 56 h of P. falciparum blood stage development. We then developed and validated strand-specific, non-polyA-selected RNA sequencing methods, and pursued the first assembly of P. falciparum strand-specific transcript structures from RNA sequencing data. This approach enabled the annotation of over one thousand lncRNA transcript models and their comprehensive global analysis: coding prediction, periodicity, stage-specificity, correlation, GC content, length, location relative to annotated transcripts, and splicing. We validated the complete splicing structure of three lncRNAs with compelling properties. Non-polyA-selected deep sequencing also enabled the prediction of hundreds of intriguing P. falciparum circular RNAs, six of which we validated experimentally.We found that a subset of lncRNAs, including all subtelomeric lncRNAs, strongly peaked in expression during invasion. By contrast, antisense transcript levels significantly dropped during invasion. As compared to neighboring mRNAs, the expression of antisense-sense pairs was significantly anti-correlated during blood stage development, indicating transcriptional interference. We also validated that P. falciparum produces circRNAs, which is notable given the lack of RNA interference in the organism, and discovered that a highly expressed, five-exon antisense RNA is poised to regulate P. falciparum gametocyte development 1 (PfGDV1), a gene required for early sexual commitment events.

Dataset Information

CircDeep: deep learning approach for circular RNA classification from other long non-coding RNA.

Publications

circDeep: deep learning approach for circular RNA classification from other long non-coding RNA.

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