Transcriptomics s

Short talk: TimiRGeN R package - novel tool for longitudinal microRNA-mRNA integration and analysis

TimiRGeN R package - novel tool for longitudinal microRNA-mRNA integration and analysis Krutik Patel Newcastle University, UK Abstract I present the TimiRGeN R/ Bioconductor package as a tool to analyse large longitudinal microRNA-mRNA expression datasets. The TimiRGeN R/ Bioconductor package is a new (late 2020 induction) tool which can integrate, perform functional analysis, and generate small intelligible networks from big longitudinal microRNA-mRNA datasets. There are several features of TimiRGeN which makes it versatile enough to become part of most microRNA-mRNA data analysis projects.

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Short talk: shortRNA: a flexible framework for the analysis of short RNA sequencing data

shortRNA: a flexible framework for the analysis of short RNA sequencing data Deepak Tanwar,Pierre-Luc Germain,Isabelle M. Mansuy Laboratory of Neuroepigenetics, Brain Research Institute, Medical Faculty of the University of Zurich, Zurich, Switzerland; Institute for Neuroscience, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Zurich Neuroscience Center, ETH Zurich and University of Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics PhD Training Network, Zurich, Switzerland; Statistical Bioinformatics Group, University of Zurich, Zurich, Switzerland

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Short talk: scanMiR: a versatile toolkit for scanning microRNA binding sites and predicting miRNA-mediated transcript repression based on a biochemical model

scanMiR: a versatile toolkit for scanning microRNA binding sites and predicting miRNA-mediated transcript repression based on a biochemical model Michael Soutschek,Fridolin Gross,Gerhard Schratt,Pierre-Luc Germain ETH Zürich Abstract Despite the importance of miRNAs in regulating a broad variety of phenomena, there is a relative paucity of R-based tools for predicting and handling their binding sites. A recent study showed improved miRNA target prediction using a biochemical model combined with empirically-derived affinity predictions across 12mer sequences (McGeary, Lin et al.

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Short talk: EaSIeR: predicting immune response using quantitative descriptors of the tumor microenvironment extracted from RNA-seq data

EaSIeR: predicting immune response using quantitative descriptors of the tumor microenvironment extracted from RNA-seq data Óscar Lapuente-Santana,Federico Marini,Arsenij Ustjanzew,Francesca Finotello,Federica Eduati Eindhoven University of Technology Abstract Identification of biomarkers of immune response in the tumor microenvironment for prediction of patients’ response to immune checkpoint inhibitors is a major challenge in immuno-oncology. Tumors are complex systems, and understanding immune response in the tumor microenvironment requires holistic strategies. We have developed a user-friendly R package to Estimate Systems Immune Response (EaSIeR), which provides a systems-level characterization of anti-tumor immune responses.

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