Independent Teams

The members of the Applied Computational Biology group at the Institute of Experimental Genetics (IEG) at the Helmholtz Zentrum München (HMGU) cover a wide range of scientific disciplines, all possessing deep IT knowledge and experience and a strong command of the IT toolbox. As a team, we work at the interface between biology and applied computer sciences in our institute as well as in external collaborations.

This is for testing purposes only

Download all genome from https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/virus?SeqType_s=Nucleotide with filter host:viridiplantae and Refseq on.

Poster and presentation video by Ambarish Kumar

Workflow for quality assessment of paired reads and classification using NGTax 2.0 and functional annotation using picrust2. In addition files are exported to their respective subfolders for easier data management in a later stage. Steps: - FastQC (read quality control) - NGTax 2.0 - Picrust 2 - Export module for ngtax

ASPICov was developed to provide a rapid, reliable and complete analysis of NGS SARS-Cov2 samples to the biologist. This broad application tool allows to process samples from either capture or amplicon strategy and Illumina or Ion Torrent technology. To ensure FAIR data analysis, this Nextflow pipeline follows nf-core guidelines and use Singularity containers. Availability and Implementation: https://gitlab.com/vtilloy/aspicov

Analysis of RNA-seq data starting from BAM and focusing on mRNA, lncRNA and miRNA

This workflow is based on the idea of comparing different gene sets through their semantic interpretation. In many cases, the user studies a specific phenotype (e.g. disease) by analyzing lists of genes resulting from different samples or patients. Their pathway analysis could result in different semantic networks, revealing mechanistic and phenotypic divergence between these gene sets. The workflow of BioTranslator Comparative Analysis compares quantitatively the outputs of pathway analysis, ...

BioTranslator performs sequentially pathway analysis and gene prioritization: A specific operator is executed for each task to translate the input gene set into semantic terms and pinpoint the pivotal-role genes on the derived semantic network. The output consists of the set of statistically significant semantic terms and the associated hub genes (the gene signature), prioritized according to their involvement in the underlying semantic topology.