CRIT:Identifying RNA-binding protein regulator in circRNA life cycle via non-negative matrix factorization

Circular RNAs (circRNAs) are endogenous non-coding RNAs that regulate gene expression and participate in carcinogenesis. However, the RNA-binding proteins (RBPs) involved in circRNAs biogenesis and modulation remain largely unclear. We developed the circRNA regulator identification tool (CRIT), a non-negative matrix-factorization-based pipeline to identify regulating RBPs in cancers. CRIT uncovered 73 novel regulators across thousands of samples by effectively leveraging genomics data and functional annotations. We demonstrated that known RBPs involved in circRNA control are significantly enriched in these predictions. Analysis of circRNA-RBP interactions using two large cross-linking immunoprecipitation (CLIP) databases, we validated the consistency between CRIT prediction and the CLIP experiments. Furthermore, newly discovered RBPs are functionally connected with authentic circRNA regulators by various biological associations, such as physical interaction, similar binding motifs, common transcription factor modulation, and co-expression. When analyzing RNA sequencing (RNA-seq) datasets after short hairpin RNA (shRNA)/small interfering RNA (siRNA) knockdown, we found several novel RBPs that can affect global circRNA expression, which strengthens their role in the circRNA life cycle. The above evidence provided independent confirmation that CRIT is a useful tool to capture RBPs in circRNA processing. Finally, we show that authentic regulators are more likely the core splicing proteins and peripheral factors and usually harbor more alterations in the vast majority of cancers

Alternative Splicing Regulatory Networks: Functions, Mechanisms, and Evolution

High-throughput sequencing-based methods and their applications in the study of transcriptomes have revolutionized our understanding of alternative splicing. Networks of functionally coordinated and biologically important alternative splicing events continue to be discovered in an ever-increasing diversity of cell types in the context of physiologically normal and disease states. These studies have been complemented by efforts directed at defining sequence codes governing splicing and their cognate trans-acting factors, which have illuminated important combinatorial principles of regulation. Additional studies have revealed critical roles of position-dependent, multivalent protein-RNA interactions that direct splicing outcomes. Investigations of evolutionary changes in RNA binding proteins, splice variants, and associated cis elements have further shed light on the emergence, mechanisms, and functions of splicing networks. Progress in these areas has emphasized the need for a coordinated, community-based effort to systematically address the functions of individual splice variants associated with normal and disease biology

End-to-end learning framework for circular RNA classification from other long non-coding RNAs using multi-modal deep learning.

Over the past two decades, a circular form of RNA (circular RNA) produced from splicing mechanism has become the focus of scientific studies due to its major role as a microRNA (miR) ac tivity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is a vital operation for continued comprehension of their biogenesis and purpose. Prediction of circular RNA can be achieved by first distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs (lncRNAs), and finally pre dicting circular RNAs from other lncRNAs. However, available tools to distinguish circular RNAs from other lncRNAs have only reached 80% accuracy due to the difficulty of classifying circular RNAs from other lncRNAs. Therefore, the availability of a faster, more accurate machine learning method for the identification of circular RNAs, which will take into account the specific features of circular RNA, is essential in the development of systematic annotation. Here we present an End to-End multimodal deep learning framework, our tool, to classify circular RNA from other lncRNA. It fuses a RCM descriptor, an 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 our tool is not only faster compared to existing tools but also eclipses other tools by an over 12% increase in accuracy. Another interesting result found from analysis of a ACNN-BLSTM sequence descriptor is that circular RNA sequences share the characteristics of the coding sequence

Genome-wide detection of human variants that disrupt intronic branchpoints

The search for candidate variants underlying human disease in massive parallel sequencing data typically focuses on coding regions and essential splice sites, mostly ignoring noncoding variants. The RNA spliceosome recognizes intronic branchpoint (BP) motifs at the beginning of splicing and operates mostly within introns to define the exon-intron boundaries; however, BP variants have been paid little attention. We established a comprehensive genome-wide database and knowledgebase of BP and developed BPHunter for systematic and informative genome-wide detection of intronic variants that may disrupt BP and splicing, together with an effective strategy for prioritizing BP variant candidates. BPHunter not only constitutes an important resource for understanding BP, but should also drive discovery of BP variants in human genetic diseases and traits. Pre-messenger RNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Forty-eight rare variants in 43 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach was available to efficiently detect such variants in massively parallel sequencing data. We established a comprehensive human genome-wide BP database by integrating existing BP data and generating new BP data from RNA sequencing of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We characterized multiple features of BP in major and minor introns and found that BP and BP-2 (two nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, while being comparable to the exonic background. We developed BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition. BPHunter retrospectively identified 40 of the 48 known pathogenic BP variants, in which we summarized a strategy for prioritizing BP variant candidates. The remaining eight variants all create AG-dinucleotides between the BP and acceptor site, which is the likely reason for missplicing. We demonstrated the practical utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which were validated experimentally. BPHunter is publicly available from an

Stealing the Show: KSHV Hijacks Host RNA Regulatory Pathways to Promote Infection

Kaposi’s sarcoma-associated herpesvirus (KSHV) induces life-long infections and has evolved many ways to exert extensive control over its host’s transcriptional and post-transcriptional machinery to gain better access to resources and dampened immune sensing. The hallmark of this takeover is how KSHV reshapes RNA fate both to control expression of its own gene but also that of its host. From the nucleus to the cytoplasm, control of RNA expression, localization, and decay is a process that is carefully tuned by a multitude of factors and that can adapt or react to rapid changes in the environment. Intriguingly, it appears that KSHV has found ways to co-opt each of these pathways for its own benefit. Here we provide a comprehensive review of recent work in this area and in particular recent advances on the post-transcriptional modifications front. Overall, this review highlights the myriad of ways KSHV uses to control RNA fate and gathers novel insights gained from the past decade of research at the interface of RNA biology and the field of KSHV research

Unveiling ncRNA regulatory axes in atherosclerosis progression

Completion of the human genome sequencing project highlighted the richness of the cellular RNA world, and opened the door to the discovery of a plethora of short and long non-coding RNAs (the dark transcriptome) with regulatory or structural potential, which shifted the balance of pathological gene alterations from coding to non-coding RNAs. Thus, disease risk assessment currently has to also evaluate the expression of new RNAs such as small micro RNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), competing endogenous RNAs (ceRNAs), retrogressed elements, 3'UTRs of mRNAs, etc. We are interested in the pathogenic mechanisms of atherosclerosis (ATH) progression in patients suffering Chronic Kidney Disease, and in this review, we will focus in the role of the dark transcriptome (non-coding RNAs) in ATH progression. We will focus in miRNAs and in the formation of regulatory axes or networks with their mRNA targets and with the lncRNAs that function as miRNA sponges or competitive inhibitors of miRNA activity. In this sense, we will pay special attention to retrogressed genomic elements, such as processed pseudogenes and Alu repeated elements, that have been recently seen to also function as miRNA sponges, as well as to the use or miRNA derivatives in gene silencing, anti-ATH therapies. Along the review, we will discuss technical developments associated to research in lncRNAs, from sequencing technologies to databases, repositories and algorithms to predict miRNA targets, as well as new approaches to miRNA function, such as integrative or enrichment analysis and their potential to unveil RNA regulatory networks

Non-coding RNAs in the brain: new class of prospective biomarkers and therapeutics

The human genome encrypts around 20,000 protein coding genes, constituting around 1% of the total human genome sequence. The rest of it initially labeled as a “junk DNA” is transcribed to a distinct class of non-coding RNAs (ncRNAs) which do not code for any protein in the cell and their presence was quite intriguing to the researchers. The recent studies, however, have surprisingly revealed the vital roles of these ncRNAs in regulating an array of diverse cellular and biological processes in different organs including brain. The dysfunction of these regulatory ncRNAs in human brain causes certain neurological disorders and brain tumors which earlier have been widely linked to various risk factors such as oxidative stress, genetic mutations, aberrant protein degradation and dysfunctional neural network.  This review provides an    overview of different types of ncRNAs, their regulatory roles in brain functions and neurological disorders along with their   prospects to be used as potential biomarkers and therapeutics.