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. To address this research gap, we propose "Kuura-An automated workflow for analyzing WES and WGS data", which is optimized for both whole exome and whole genome sequencing data. This workflow is based on the nextflow pipeline scripting language and uses docker to manage and deploy the workflow. The workflow consists of four analysis stages-quality control, mapping to reference genome & quality score recalibration, variant calling & variant recalibration and variant consensus & annotation. An important feature of the DNA-seq workflow is that it uses the combination of multiple variant callers (GATK Haplotypecaller, DeepVariant, VarScan2, Freebayes and Strelka2), generating a list of high-confidence variants in a consensus call file. The workflow is flexible as it integrates the fragmented tools and can be easily extended by adding or updating tools or amending the parameters list. The use of a single parameters file enhances reproducibility of the results. The ease of deployment and usage of the workflow further increases computational reproducibility providing researchers with a standardized tool for the variant calling step in different projects. The source code, instructions for installation and use of the tool are publicly available at our github repository https://github.com/dhanaprakashj/kuura_pipeline.

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. One such variant is rs77559646 located in ANO7. This variant has a dual function. It constitutes a missense mutation in the short isoform of ANO7 and a splice region mutation in full-length ANO7. In this study, we have analyzed the impact of the variant allele of rs77559646 on ANO7 mRNA splicing using a minigene splicing assay and by performing splicing analysis with the tools IRFinder (intron retention finder), rMATS (replicate multivariate analysis of transcript splicing) and LeafCutter on RNA sequencing data from prostate tissue of six rs77559646 variant allele carriers and 43 non-carriers. The results revealed a severe disruption of ANO7 mRNA splicing in rs77559646 variant allele carriers. Immunohistochemical analysis of prostate samples from patients homozygous for the rs77559646 variant allele demonstrated a loss of apically localized ANO7 protein. Our study is the first to provide a mechanistic explanation for the impact of a prostate cancer risk SNP on ANO7 protein production. Furthermore, the rs77559646 variant is the first known germline loss-of-function mutation described for ANO7. We suggest that loss of ANO7 contributes to prostate cancer progression.

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. RESULTS: We have developed an interactive application, which harnesses the easy usability of R Shiny and combines it with the versatile annotation features of ANNOVAR. This application is easy to use and gives comprehensive annotations for user supplied vcf files using multiples databases. The output table contains the list of variants and their corresponding annotation presented within the graphical interface. In addition, the annotation results are downloadable as text file.

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). We describe how to use our Python tool, Sargasso, to separate the reads from conventional RNA-seq according to species and to eliminate any artifacts borne of imperfect genome annotation (10 h). We show how this procedure, which requires no special skills beyond those that might normally be expected of wet lab and bioinformatics researchers, enables the simultaneous transcriptomic profiling of different cell types, revealing the distinct influence of microglia on astrocytic and neuronal transcriptomes under inflammatory conditions.

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.

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

The influence that neurons exert on astrocytic function is poorly understood. To investigate this, we first developed a system combining cortical neurons and astrocytes from closely related species, followed by RNA-seq and in silico species separation. This approach uncovers a wide programme of neuron-induced astrocytic gene expression, involving Notch signalling, which drives and maintains astrocytic maturity and neurotransmitter uptake function, is conserved in human development, and is disrupted by neurodegeneration. Separately, hundreds of astrocytic genes are acutely regulated by synaptic activity via mechanisms involving cAMP/PKA-dependent CREB activation. This includes the coordinated activity-dependent upregulation of major astrocytic components of the astrocyte-neuron lactate shuttle, leading to a CREB-dependent increase in astrocytic glucose metabolism and elevated lactate export. Moreover, the groups of astrocytic genes induced by neurons or neuronal activity both show age-dependent decline in humans. Thus, neurons and neuronal activity regulate the astrocytic transcriptome with the potential to shape astrocyte-neuron metabolic cooperation.

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. While activity-dependent gene-responsiveness showed little dependence on developmental stage or origin (primary tissue vs. stem cell), notable species-dependent differences were observed. Moreover, differential species-specific gene ortholog regulation was recapitulated in aneuploid mouse neurons carrying human chromosome-21, implicating promoter/enhancer sequence divergence as a factor, including human-specific activity-responsive AP-1 sites. These findings support the use of human neuronal systems for probing transcriptional responses to physiological stimuli or indeed pharmaceutical agents.

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. astrocytes), differing neuronal subtype (CA3 vs. CA1 hippocampus) and in response to Ca2+ influx, using a combination of qPCR and RNA-seq analysis. Of note, we find that the Mcu-regulating Micu gene family profile differs substantially between neurons and astrocytes, while expression of Mcu itself is markedly different between CA3 and CA1 regions in the adult hippocampus. Moreover, dynamic control of MCRG mRNA expression in response to membrane depolarization-induced Ca2+ influx is also apparent, resulting in repression of Letm1, as well as Mcu. Thus, the mRNA expression profile of MCRGs is not fixed, which may cause differences in the coupling between cytoplasmic and mitochondrial Ca2+, as well as diversity of mitochondrial Ca2+ uptake mechanisms.

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. GOssTo is very fast, easy to use, and it allows the calculation of similarities on a genomic scale in a few minutes on a regular desktop machine. CONTACT: alberto@cs.rhul.ac.uk AVAILABILITY: GOssTo is available both as a stand-alone application running on GNU/Linux, Windows and MacOS from www.paccanarolab.org/gossto and as a web application from www.paccanarolab.org/gosstoweb. The stand-alone application features a simple and concise command line interface for easy integration into high-throughput data processing pipelines.