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Biology subjects

Mattanovich, M.

Publications and source records attributed to Mattanovich, M..

2 recordsLinked to original sources

iTraNet: A Web-Based Platform for integrated Trans-Omics Network Visualization and Analysis

ABSTRACTA major goal in biology is to comprehensively understand molecular interactions within living systems. Visualization and analysis of biological networks play crucial roles in understanding these biochemical processes. Biological networks include diverse types, from gene regulatory networks and protein-protein interactions (PPIs) to metabolic networks. Metabolic networks include substrates, products, and enzymes, which are regulated by allosteric mechanisms and gene expression. Given this complexity, there is a pressing need to investigate trans-omics networks that include these various regulations to understand living systems. However, analyzing various omics layers is laborious due to the diversity of databases and the intricate nature of network analysis. We developed iTraNet, a user-friendly interactive web application that visualizes and analyzes trans-omics networks involving four major types of networks: gene regulatory networks (including transcription factor, microRNA, and mRNA); PPIs; metabolic networks (including enzyme, mRNA, and metabolite); and metabolite exchange networks (including transporter, mRNA, and metabolite). Using iTraNet, we found that in wild-type mice, hub molecules within the network tended to respond to glucose administration, whereas in ob/ob mice, this tendency disappeared. With its ability to facilitate network visualization and analysis, we anticipate that iTraNet will help researchers gain insights into biological systems. iTraNet is available at (https://transomics.streamlit.app/). GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/569499v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@12bb1c1org.highwire.dtl.DTLVardef@1a326b7org.highwire.dtl.DTLVardef@42cfc9org.highwire.dtl.DTLVardef@57aee6_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation is needed to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

systems biology↗