Ray, F. | bioRxiv Science

bioRxiv2020

coronapp: a Web Application to Annotate and Monitor SARS-CoV-2 Mutations

The avalanche of genomic data generated from the SARS-CoV-2 virus requires the development of tools to detect and monitor its mutations across the world. Here, we present a webtool, coronapp, dedicated to easily processing user-provided SARS-CoV-2 genomic sequences and visualizing current worldwide status of SARS-CoV-2 mutations. The webtool allows users to highlight mutations and categorize them by frequency, country, genomic location and effect on protein sequences, and to monitor their presence in the population over time. The tool is available at http://giorgilab.unibo.it/coronapp/ for the worldwide dataset and at http://giorgilab.unibo.it/coronannotator/ for the annotation of user-provided sequences. The full code is freely shared at https://github.com/federicogiorgi/giorgilab/tree/master/coronapp Data Availability StatementThe data that support the findings of this study derive from the GISAID consortium and are openly available in Github, in Rdata format for the R environment, in files results.rda and metadata.rda, at the following link: https://github.com/federicogiorgi/giorgilab/tree/master/coronapp/data

bioinformatics↗

bioRxiv2018

Pan-Cancer modelling of genomic alterations through gene expression

Cancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single cell sequencing. Author SummaryIn this article we show that transcript abundance can be used to predict the presence or absence of the majority of genomic alterations present in human cancer. We also show how these predictions can be improved by aggregating genes into small networks to counteract the effects of transcript measurement noise.

systems biology↗