Circuit Dynamics and Computational Neuroscience I.1.h Decision making Monday AM + Wednesday AM

2471 - Development of a web application to analyse the quantitative transcriptomic and proteomic data for neuro infections

Topic / Sub Topic
I.1.h Decision making
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https://us04web.zoom.us/j/77690755344?pwd=dHFkb2NFNDJ4cjFiMHFOOW95TUdQQT09

Abstract

Abstract Body

High-throughput techniques like RNA-seq and LC-MS are extensively being used to study the quantitative expression of genes and proteins. Quantitative data generated from both techniques are cumbersome to analyse when it comes to complex biological processes like neuro infections (bacterial or viral origin). The generated giga-bytes data from both techniques is challenging for researchers with limited computational skills. To this background, we are developing a user-friendly R/shiny-based bioinformatics tool “OMnalysis” to handle the differentially expressed quantitative transcriptomic and proteomic data. OMnalysis integrates various open-source Bioconductor/R packages to annotate the metadata to meaningful biological information. The workflow streamlines 6 updated supporting databases including Ensembl and more than 90 open-source packages with multiple dependencies to handle the input metadata. The OMnalysis tool is categorised into 5 major levels, data upload, pre-processing (statistical filtering and dimension reduction), visualization, gene ontology enrichment analysis, pathway enrichment analysis and text mining to get information from the supporting research articles. In each level of analysis, the user can alter the parameters to mine the more relevant information. Additionally, the user can download the processed data at each level to perform customized analysis with third party bioinformatics tool. We believe that, with comprehensive and advance R packages, OMnalysis will help the researcher to interpret the quantitative differential transcriptomics and proteomics data to biological insight.

Acknowledgements: PT is supported by H2020-MSCA-ITN-2017-EJD: Marie Skłodowska-Curie Innovative Training Networks 765423. Research of other authors is supported by VEGA1/0105/19, VEGA1/0439/18 and APVV-18-0259.

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