Biology subjects

Ioannis Xenarios

Publications and source records attributed to Ioannis Xenarios.

4 recordsLinked to original sources

bioRxiv2016

Varapp: A reactive web-application for variants filtering

Summary: Varapp is an open-source web application to filter variants from large sets of exome data stored in a relational database. Varapp offers a reactive graphical user interface, very fast data pro-cessing, security and facility to save, reproduce and shareresults. Typically, a few seconds suffice to apply non-trivial filters to a set of half a million variants and extract a handful of potential clinically relevant targets. Varapp implements different scenarios for Mendelian diseases (dominant, recessive, de novo, X-linked, andcompound heterozygous), and allows searching for variants in genes or chro-mosomal regions of interest.\n\nAvailability: The application is made of a Javascript front-end and a Python back-end. Its source code is hosted at https://github.com/varapp. A demo version isavailable at https://varapp-demo.vital-it.ch. The full documentation can be found at https://varapp-demo.vital-it.ch/docs.\n\nContact: sylvain.pradervand@chuv.ch

Bioinformatics↗

bioRxiv2016

SourceData - a semantic platform for curating and searching figures

Here we present SourceData (http://sourcedata.embo.org), a platform that allows researchers and publishers to share scientific figures and, when available, the underlying source data in a way that is machine-readable and findable. SourceData is unique in its focus on the core of scientific evidence--data presented in figures--and its capability to make papers searchable based on their data content and hence directly couple data to improved discoverability. SourceData aims at establishing a selfreinforcing data ecosystem that bridges the conventional visual and narrative description of research findings with a machine-readable representation of data and hypotheses.\n\nIn molecular and cell biology, most of the data that result from hypothesis-driven research are exclusively available in the form of figures or tables in published papers. In spite of their importance for the understanding of biological processes and human di ...

Bioinformatics↗

bioRxiv2014

Cooperative development of logical modelling standards and tools with CoLoMoTo

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour, and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Here, we outline the logical modelling framework and present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. This open community welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments.

Systems Biology↗

bioRxiv2014

Analysis of stop-gain and frameshift variants in human innate immunity genes

Loss-of-function variants in innate immunity genes are associated with Mendelian disorders in the form of primary immunodeficiencies. Recent resequencing projects report that stop-gains and frameshifts are collectively prevalent in humans and could be responsible for some of the inter-individual variability in innate immune response. Current computational approaches evaluating loss-of-function in genes carrying these variants rely on gene-level characteristics such as evolutionary conservation and functional redundancy across the genome. However, innate immunity genes represent a particular case because they are more likely to be under positive selection and duplicated. To create a ranking of severity that would be applicable to the innate immunity genes we first evaluated 17764 stop-gain and 13915 frameshift variants from the NHLBI Exome Sequencing Project and 1000 Genomes Project. Sequence-based features such as loss of functional domains, isoform-specific truncation and non-sense mediated decay were found to correlate with variant allele frequency and validated with gene expression data. We integrated these features in a Bayesian classification scheme and benchmarked its use in predicting pathogenic variants against OMIM disease stop-gains and frameshifts. The classification scheme was applied in the assessment of 335 stop-gains and 236 frameshifts affecting 227 interferon-stimulated genes. The sequence-based score ranks variants in innate immunity genes according to their potential to cause disease, and complements existing gene-based pathogenicity scores.

Bioinformatics↗