Computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns

Liu, Ze; Han, Jiuqiang; Lv, Hongqiang; Li, Jun; Liu, Ruiling

doi:10.1016/j.compbiolchem.2016.02.003

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…PredcircRNA uses a multi-core learning algorithm [ 21 ], and extract sequence features such as graph features and conservation scores to classify circRNAs and lncRNAs. PredicircRNATool [ 46 ] distinguishes circRNAs based on the SVM model by extracting flanking introns and thermodynamic dinucleotide properties as features. WebCircRNA is based on random forests and uses sequence-derived features to predict circRNA in stem cells [ 22 ].…”

Section: Resultsmentioning

confidence: 99%

StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy

Wang

Liu

et al. 2022

BMC Bioinformatics

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Background CircRNAs are essential for the regulation of post-transcriptional gene expression, including as miRNA sponges, and play an important role in disease development. Some computational tools have been proposed recently to predict circRNA, since only one classifier is used, there is still much that can be done to improve the performance. Results StackCirRNAPred was proposed, the computational classification of long circRNA from other lncRNA based on stacking strategy. In order to cope with the potential problem that a single feature might not be able to distinguish circRNA well from other lncRNA, we first extracted features from different sources, including nucleic acid composition, sequence spatial features and physicochemical properties, Alu and tandem repeats. We innovatively apply the stacking strategy to integrate the more advantageous classifiers of RF, LightGBM, XGBoost. This allows the model to incorporate these features more flexibly. StackCirRNAPred was found to be significantly better than other tools, with precision, accuracy, F1, recall and MCC of 0.843, 0.833, 0.831, 0.819 and 0.666 respectively. We tested it directly on the mouse dataset. StackCirRNAPred was still significantly better than other methods, with precision, accuracy, F1, recall and MCC of 0.837, 0.839, 0.839, 0.841, 0.677. Conclusions We proposed StackCirRNAPred based on stacking strategy to distinguish long circRNAs from other lncRNAs. With the test results demonstrating the validity and robustness of StackCirRNAPred, we hope StackCirRNAPred will complement existing circRNA prediction methods and is helpful in down-stream research.

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Section: Resultsmentioning

confidence: 99%

StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy

Wang

Liu

et al. 2022

BMC Bioinformatics

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“…For example, PredcircRNA [ 76 ] and StackCirRNAPred [ 107 ] predict whether an unknown RNA sequence possibly comes from circRNA by some common reliable features, such as ALU repeats, structural motifs and sequence motifs [ 15 , 76 ]. Other machine learning circRNA prediction tools based on the characteristics of nucleotide sequences are PredicircRNATool [ 108 ], DeepCirCode [ 77 ], CirRNAPL [ 109 ], PCirc [ 110 ], circDeep [ 111 ], etc. CirRNAPL is a user-friendly web server that extracts the structural features and pseudo-ribonucleic acid composition of circRNA to optimize the extreme learning machine based on the particle swarm optimization algorithm, which achieves identification accuracy in three public datasets [ 109 ].…”

Section: Characterization Of Circrnasmentioning

confidence: 99%

New insight into circRNAs: characterization, strategies, and biomedical applications

Feng,

Zhu,

et al. 2023

Exp Hematol Oncol

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Circular RNAs (circRNAs) are a class of covalently closed, endogenous ncRNAs. Most circRNAs are derived from exonic or intronic sequences by precursor RNA back-splicing. Advanced high-throughput RNA sequencing and experimental technologies have enabled the extensive identification and characterization of circRNAs, such as novel types of biogenesis, tissue-specific and cell-specific expression patterns, epigenetic regulation, translation potential, localization and metabolism. Increasing evidence has revealed that circRNAs participate in diverse cellular processes, and their dysregulation is involved in the pathogenesis of various diseases, particularly cancer. In this review, we systematically discuss the characterization of circRNAs, databases, challenges for circRNA discovery, new insight into strategies used in circRNA studies and biomedical applications. Although recent studies have advanced the understanding of circRNAs, advanced knowledge and approaches for circRNA annotation, functional characterization and biomedical applications are continuously needed to provide new insights into circRNAs. The emergence of circRNA-based protein translation strategy will be a promising direction in the field of biomedicine.

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“…Recently, machine learning approaches have also been applied to predict circRNAs, using several models classified on their known features (i.e., the conservation of transposable element, tandem repeats, open reading frame length, and single nucleotide polymorphism density). These tools mainly include DeepCirCode [174], PredcircRNA [175], WebCircRNA [176], and PredicircRNATool [177]. It is noteworthy that integration of different circRNA identification tools can reduce the false‐positive rate [178–180].…”

Section: Databases For the Prediction And Validation Of Circrnas And Lncrnasmentioning

confidence: 99%

The pleiotropic roles of circular and long noncoding RNAs in cutaneous melanoma

et al. 2021

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Computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns

Cited by 13 publications

References 21 publications

StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy

StackCirRNAPred: computational classification of long circRNA from other lncRNA based on stacking strategy

New insight into circRNAs: characterization, strategies, and biomedical applications

The pleiotropic roles of circular and long noncoding RNAs in cutaneous melanoma

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