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.

Abstract MicroRNAs (miRNAs) are predicted to regulate the expression of >60% of mammalian genes and play fundamental roles in most biological processes. Deregulation of miRNA expression is a hallmark of most cancers and further investigation of mechanisms controlling miRNA biogenesis is needed. The double stranded RNA-binding protein, NF90 has been shown to act as a competitor of Microprocessor for a limited number of primary miRNAs (pri-miRNAs). Here, we show that NF90 has a more widespread effect on pri-miRNA biogenesis than previously thought. Genome-wide approaches revealed that NF90 is associated with the stem region of 38 pri-miRNAs, in a manner that is largely exclusive of Microprocessor. Following loss of NF90, 22 NF90-bound pri-miRNAs showed increased abundance of mature miRNA products. NF90-targeted pri-miRNAs are highly stable, having a lower free energy and fewer mismatches compared to all pri-miRNAs. Mutations leading to less stable structures reduced NF90 binding while increasing pri-miRNA stability led to acquisition of NF90 association, as determined by RNA electrophoretic mobility shift assay (EMSA). NF90-bound and downregulated pri-miRNAs are embedded in introns of host genes and expression of several host genes is concomitantly reduced. These data suggest that NF90 controls the processing of a subset of highly stable, intronic miRNAs.

Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data.