Kuura-An automated workflow for analyzing WES and WGS data.

The advent of high-throughput sequencing technologies has revolutionized the field of genomic sciences by cutting down the cost and time associated with standard sequencing methods. This advancement has not only provided the research community with an abundance of data but has also presented the challenge of analyzing it. The paramount challenge in analyzing the copious amount of data is in using the optimal resources in terms of available tools.

The variant rs77559646 associated with aggressive prostate cancer disrupts ANO7 mRNA splicing and protein expression.

Prostate cancer is among the most common cancers in men, with a large fraction of the individual risk attributable to heritable factors. A majority of the diagnosed cases does not lead to a lethal disease, and hence biological markers that can distinguish between indolent and fatal forms of the disease are of great importance for guiding treatment decisions. Although over 300 genetic variants are known to be associated with prostate cancer risk, few have been associated with the risk of an aggressive disease.

ShAn: An easy-to-use tool for interactive and integrated variant annotation.

Motivation: Annotation of large amounts of generated sequencing data is a demanding task. Most of the currently available robust annotation tools, like ANNOVAR, are command-line based tools which require a certain degree of programming skills. User-friendly tools for variant annotation of sequencing data with graphical interface are under-represented.

Mixed-species RNA-seq for elucidation of non-cell-autonomous control of gene transcription.

Transcriptomic changes induced in one cell type by another mediate many biological processes in the brain and elsewhere; however, achieving artifact-free physical separation of cell types to study them is challenging and generally allows for analysis of only a single cell type. We describe an approach using a co-culture of distinct cell types from different species that enables physical cell sorting to be replaced by in silico RNA sequencing (RNA-seq) read sorting, which is possible because of evolutionary divergence of messenger RNA (mRNA) sequences. As an exemplary experiment, we describe the co-culture of purified neurons, astrocytes, and microglia from different species (12-14 d).

Author Correction: Neurons and neuronal activity control gene expression in astrocytes to regulate their development and metabolism.

This corrects the article DOI: 10.1038/ncomms15132.

Evidence for evolutionary divergence of activity-dependent gene expression in developing neurons.

Evolutionary differences in gene regulation between humans and lower mammalian experimental systems are incompletely understood, a potential translational obstacle that is challenging to surmount in neurons, where primary tissue availability is poor. Rodent-based studies show that activity-dependent transcriptional programs mediate myriad functions in neuronal development, but the extent of their conservation in human neurons is unknown. We compared activity-dependent transcriptional responses in developing human stem cell-derived cortical neurons with those induced in developing primary- or stem cell-derived mouse cortical neurons.

Expression of mRNA Encoding Mcu and Other Mitochondrial Calcium Regulatory Genes Depends on Cell Type, Neuronal Subtype, and Ca2+ Signaling.

Uptake of Ca2+ into the mitochondrial matrix controls cellular metabolism and survival-death pathways. Several genes are implicated in controlling mitochondrial Ca2+ uptake (mitochondrial calcium regulatory genes, MCRGs), however, less is known about the factors which influence their expression level. Here we have compared MCRG mRNA expression, in neural cells of differing type (cortical neurons vs.

GOssTo: a stand-alone application and a web tool for calculating semantic similarities on the Gene Ontology.

Summary: We present GOssTo, the Gene Ontology semantic similarity Tool, a user-friendly software system for calculating semantic similarities between gene products according to the Gene Ontology. GOssTo is bundled with six semantic similarity measures, including both term- and graph-based measures, and has extension capabilities to allow the user to add new similarities. Importantly, for any measure, GOssTo can also calculate the Random Walk Contribution that has been shown to greatly improve the accuracy of similarity measures.