Accurate quantification and detection of intron retention levels require specialized software. Building on our previous software, we create a suite of tools called IRFinder-S, to analyze and explore intron retention events in multiple samples. Specifically, IRFinder-S allows a better identification of true intron retention events using a convolutional neural network, allows the sharing of intron retention results between labs, integrates a dynamic database to explore and contrast available samples, and provides a tested method to detect differential levels of intron retention.
View Article and Find Full Text PDFBackground: Breast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments.
View Article and Find Full Text PDFBackground: Faecal contamination from dairy farm effluent is a major risk to water quality in New Zealand. In this experiment we have tested the efficacy of Kombucha SCOBY (symbiotic culture of bacteria and yeast), to reduce the concentration of Escherichia coli in dairy shed effluent (DSE).
Results: Kombucha SCOBY was highly effective in lowering the number of E.
Intron retention (IR) occurs when a complete and unspliced intron remains in mature mRNA. An increasing body of literature has demonstrated a major role for IR in numerous biological functions, including several that impact human health and disease. Although experimental technologies used to study other forms of mRNA splicing can also be used to investigate IR, a specialized downstream computational analysis is optimal for IR discovery and analysis.
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