Current Genomics - Volume 21, Issue 1, 2020

RNA 5-methylcytosine (m5C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m5C plays a vital role in RNA metabolism. Accurate localization of RNA m5C sites in tissue cells is the premise and basis for the in-depth understanding of the functions of m5C. However, the main experimental methods of detecting m5C sites are limited to varying degrees. Establishing a computational model to predict modification sites is an excellent complement to wet experiments for identifying m5C sites. In this review, we summarized some available m5C predictors and discussed the characteristics of these methods.

MicroRNAs, a group of short non-coding RNA molecules, could regulate gene expression. Many diseases are associated with abnormal expression of miRNAs. Therefore, accurate identification of miRNA precursors is necessary. In the past 10 years, experimental methods, comparative genomics methods, and artificial intelligence methods have been used to identify pre-miRNAs. However, experimental methods and comparative genomics methods have their disadvantages, such as timeconsuming. In contrast, machine learning-based method is a better choice. Therefore, the review summarizes the current advances in pre-miRNA recognition based on computational methods, including the construction of benchmark datasets, feature extraction methods, prediction algorithms, and the results of the models. And we also provide valid information about the predictors currently available. Finally, we give the future perspectives on the identification of pre-miRNAs. The review provides scholars with a whole background of pre-miRNA identification by using machine learning methods, which can help researchers have a clear understanding of progress of the research in this field.

Introduction: N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved. Methodology: In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set. Results: Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors. Conclusion: In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.

Nearly three decades ago, the association between Bladder cancer (BC) and DNA methylation has initially been reported. Indeed, in the recent years, the mechanism connecting these two has gained deeper insights. Still, the mediocre performance of DNA methylation markers in the clinics raises the major concern. Strikingly, whether it is the inter-individual methylation variations or the paucity of knowledge about methylation fingerprints lying within histologically distinct subtypes of BC requires critical discussion. In the future, besides identifying the initial causative factors, it will be important to illustrate the cascade of events that determines the fraction of the genome to convey altered methylation patterns specific towards each cancer type.

The pluripotent state of stem cells depends on the complicated network orchestrated by thousands of factors and genes. Long noncoding RNAs (lncRNAs) are a class of RNA longer than 200 nt without a protein-coding function. Single-cell sequencing studies have identified hundreds of lncRNAs with dynamic changes in somatic cell reprogramming. Accumulating evidence suggests that they participate in the initiation of reprogramming, maintenance of pluripotency, and developmental processes by cis and/or trans mechanisms. In particular, they may interact with proteins, RNAs, and chromatin modifier complexes to form an intricate pluripotency-associated network. In this review, we focus on recent progress in approaches to profiling functional lncRNAs in somatic cell reprogramming and cell differentiation.

Aim: The aim of this study was to explore the expression of exosomal non-coding RNAs (ncRNAs) in the sera of patients with HCC versus control. Methods: Firstly, Bioinformatics analysis was conducted to retrieve ncRNAs specific to HCC (hsamiRNA- 1298 and lncRNA-RP11-583F2.2). Afterwards, extraction and characterization of exosomes were performed. We measured the expression of the chosen exosomal RNAs by reverse transcriptase quantitative real-time PCR in sera of 60 patients with HCC, 42 patients with chronic hepatitis C (CHC) infection and 18 healthy normal volunteers. Results: The exosomal ncRNAs [hsa-miRNA-1298, lncRNA-RP11-583F2.2] had better sensitivity and specificity than alpha-fetoprotein (AFP) in HCC diagnosis. Conclusion: The exosomal hsa-miRNA-1298, lncRNA-RP11-583F2.2 can be potential biomarkers for HCC diagnosis.

Introduction: 5' splice site GT>GC or +2T>C variants have been frequently reported to cause human genetic disease and are routinely scored as pathogenic splicing mutations. However, we have recently demonstrated that such variants in human disease genes may not invariably be pathogenic. Moreover, we found that no splicing prediction tools appear to be capable of reliably distinguishing those +2T>C variants that generate wild-type transcripts from those that do not. Methodology: Herein, we evaluated the performance of a novel deep learning-based tool, SpliceAI, in the context of three datasets of +2T>C variants, all of which had been characterized functionally in terms of their impact on pre-mRNA splicing. The first two datasets refer to our recently described “in vivo” dataset of 45 known disease-causing +2T>C variants and the “in vitro” dataset of 103 +2T>C substitutions subjected to full-length gene splicing assay. The third dataset comprised 12 BRCA1 +2T>C variants that were recently analyzed by saturation genome editing. Results: Comparison of the SpliceAI-predicted and experimentally obtained functional impact assessments of these variants (and smaller datasets of +2T>A and +2T>G variants) revealed that although SpliceAI performed rather better than other prediction tools, it was still far from perfect. A key issue was that the impact of those +2T>C (and +2T>A) variants that generated wild-type transcripts represents a quantitative change that can vary from barely detectable to an almost full expression of wild-type transcripts, with wild-type transcripts often co-existing with aberrantly spliced transcripts. Conclusion: Our findings highlight the challenges that we still face in attempting to accurately identify splice-altering variants.

Introduction: N⁶-methyladenosine (m⁶A) is one of the most widely studied epigenetic modifications. It plays important roles in various biological processes, such as splicing, RNA localization and degradation, many of which are related to the functions of introns. Although a number of computational approaches have been proposed to predict the m⁶A sites in different species, none of them were optimized for intronic m⁶A sites. As existing experimental data overwhelmingly relied on polyA selection in sample preparation and the intronic RNAs are usually underrepresented in the captured RNA library, the accuracy of general m⁶A sites prediction approaches is limited for intronic m⁶A sites prediction task. Methodology: A computational framework, WITMSG, dedicated to the large-scale prediction of intronic m⁶A RNA methylation sites in humans has been proposed here for the first time. Based on the random forest algorithm and using only known intronic m⁶A sites as the training data, WITMSG takes advantage of both conventional sequence features and a variety of genomic characteristics for improved prediction performance of intron-specific m⁶A sites. Results and Conclusion: It has been observed that WITMSG outperformed competing approaches (trained with all the m⁶A sites or intronic m⁶A sites only) in 10-fold cross-validation (AUC: 0.940) and when tested on independent datasets (AUC: 0.946). WITMSG was also applied intronome-wide in humans to predict all possible intronic m⁶A sites, and the prediction results are freely accessible at http://rnamd.com/intron/.