Circular RNAs in diseases

The paper introduces CIRCScan, a machine learning framework that predicts circRNA expression using sequence and epigenetic features, identifying H3K79me2 as a key regulator.

The paper investigates circRNA expression across tissues and developmental stages in rats, finding unique age-dependent patterns in testes linked to spermatogenesis.

CircMarker detects circular RNA using k-mers instead of read mapping, achieving higher accuracy and efficiency than existing tools.

The paper analyzes circRNA and miRNA expression profiles in essential hypertension using microarray and bioinformatics to identify potential circRNAs associated with the disease.

The paper identifies circRNAs and their regulatory networks in posterior cingulate astrocytes from elderly subjects, finding enriched immune response pathways.

STARChip detects circular RNA and fusion transcripts from chimeric alignments with high precision and speed, suitable for large-scale medical datasets.

The paper identifies artificial junction sequences formed by reverse transcriptases, leading to false circular RNA identification, and proposes methods to distinguish true circular RNAs.

This study identifies age-related accumulation of circRNAs in C. elegans and links it to postmitotic cell composition and circRNA stability.

AFCMEasyModel is a user-friendly Shiny app for simulating competing endogenous RNA networks using ODEs, allowing parameter modification and visualization of sponge complex formation.

The paper identifies the promoter of ciRS-7 as overlapping with LINC00632 and shows ciRS-7 exonic sequence is embedded in linear transcripts in human and mouse.

The paper identifies a novel class of circular RNAs in zebrafish embryonic cells that arise from tandem duplicated genes and are enriched on polysomes.

This study develops methods to detect, quantify, and visualize circRNAs in human tissues and shows their potential as diagnostic biomarkers in blood and serum.

The paper introduces circScan, a method to identify circRNA-RBP interactions from CLIP-seq data, finding thousands of novel interactions in human and mouse genomes.

The paper identifies circRNAs in maize formed by transposon reverse complementary pairs and links them to phenotypic variation through expression changes and gene interactions.