circ-NOL10 regulated by MTDH/CASC3 inhibits breast cancer progression and metastasis via multiple miRNAs and PDCD4.

Circular RNAs (circRNAs) play important roles in carcinogenesis. Here, we investigated the mechanisms and clinical significance of , a highly repressed circRNA in breast cancer. Subsequently, we also identified RNA-binding proteins (RBPs) that regulate .

TRA2A Binds With LncRNA MALAT1 To Promote Esophageal Cancer Progression By Regulating EZH2/β-catenin Pathway.

The RNA binding protein TRA2A, a member of the transformer 2 homolog family, plays a crucial role in the alternative splicing of pre-mRNA. However, it remains unclear whether TRA2A is involved in non-coding RNA regulation and, if so, what are the functional consequences. By analyzing expression profiling data, we found that TRA2A is highly expressed in esophageal cancer and is associated with disease-free survival and overall survival time.

Revisiting the Relationship Between Alzheimer's Disease and Cancer With a circRNA Perspective.

Background: Increasing evidence indicates an association between the incidence of Alzheimer's disease (AD) and cancer development. Despite advances being made by comparisons from epidemiological studies, common pathways and molecular mechanisms, little is known about the identities of the circular RNAs (circRNAs) involved in the development and progression of these two pathologies and their possible correlations. The aim of this study was to explore the circRNA relationship between AD and cancer.

OmicsARules: a R package for integration of multi-omics datasets via association rules mining.

Background: The improvements of high throughput technologies have produced large amounts of multi-omics experiments datasets. Initial analysis of these data has revealed many concurrent gene alterations within single dataset or/and among multiple omics datasets. Although powerful bioinformatics pipelines have been developed to store, manipulate and analyze these data, few explicitly find and assess the recurrent co-occurring aberrations across multiple regulation levels.

Long Read Single-Molecule Real-Time Sequencing Elucidates Transcriptome-Wide Heterogeneity and Complexity in Esophageal Squamous Cells.

Esophageal squamous cell carcinoma is a leading cause of cancer death. Mapping the transcriptional landscapes such as isoforms, fusion transcripts, as well as long noncoding RNAs have played a central role to understand the regulating mechanism during malignant processes. However, canonical methods such as short-read RNA-seq are difficult to define the entire polyadenylated RNA molecules.

TGStools: A Bioinformatics Suit to Facilitate Transcriptome Analysis of Long Reads from Third Generation Sequencing Platform.

Recent analyses show that transcriptome sequencing can be utilized as a diagnostic tool for rare Mendelian diseases. The third generation sequencing de novo detects long reads of thousands of base pairs, thus greatly expanding the isoform discovery and identification of novel long noncoding RNAs. In this study, we developed TGStools, a bioinformatics suite to facilitate routine tasks such as characterizing full-length transcripts, detecting shifted types of alternative splicing, and long noncoding RNAs (lncRNAs) identification in transcriptome analysis.

RBPvsMIR: A Computational Pipeline to Identify Competing miRNAs and RNA-Binding Protein Pairs Regulating the Shared Transcripts.

Gene post-transcription regulation involves several critical regulators such as microRNAs (miRNAs) and RNA-binding proteins (RBPs). Accumulated experimental evidences have shown that miRNAs and RBPs can competitively regulate the shared targeting transcripts. Although this establishes a novel post-transcription regulation mechanism, there are currently no computational tools to scan for the possible competing miRNA and RBP pairs.

RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.

RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction.