: an integrative toolkit for splicing analysis from short-read RNA-seq.

Motivation: Analysis of alternative splicing using short-read RNA-seq data is a complex process that involves several steps: alignment of reads to the reference genome, identification of alternatively spliced features, motif discovery, analysis of RNA-protein binding near donor and acceptor splice sites, and exploratory data visualization. To the best of our knowledge, there is currently no integrative open-source software dedicated to this task.

Results: Here, we introduce , a Python package that provides and integrates a set of existing and novel splicing analysis tools for conducting splicing analysis.

Targeting RNA structure in SMN2 reverses spinal muscular atrophy molecular phenotypes.

Modification of SMN2 exon 7 (E7) splicing is a validated therapeutic strategy against spinal muscular atrophy (SMA). However, a target-based approach to identify small-molecule E7 splicing modifiers has not been attempted, which could reveal novel therapies with improved mechanistic insight. Here, we chose as a target the stem-loop RNA structure TSL2, which overlaps with the 5' splicing site of E7.

Genomic analysis of the molecular neuropathology of tuberous sclerosis using a human stem cell model.

Background: Tuberous sclerosis complex (TSC) is a genetic disease characterized by benign tumor growths in multiple organs and neurological symptoms induced by mTOR hyperfunction. Because the molecular pathology is highly complex and the etiology poorly understood, we employed a defined human neuronal model with a single mTOR activating mutation to dissect the disease-relevant molecular responses driving the neuropathology and suggest new targets for treatment.

Methods: We investigate the disease phenotype of TSC by neural differentiation of a human stem cell model that had been deleted for TSC2 by genome editing.

Detection of attractors of large Boolean networks via exhaustive enumeration of appropriate subspaces of the state space.

Background: Boolean models are increasingly used to study biological signaling networks. In a Boolean network, nodes represent biological entities such as genes, proteins or protein complexes, and edges indicate activating or inhibiting influences of one node towards another. Depending on the input of activators or inhibitors, Boolean networks categorize nodes as either active or inactive.