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tschmenger/README.md

About me

Hello everyone! I was working non-stop in wet-lab laboratories between 2009 and 2024. In 2009 I started my training to become a lab technician and later I finished my PhD in december 2022 in the group of Prof. Rob Russell. My job there included managing 'my own' laboratory, which included supervising a permanent technical assistant & the occasional students. I am now a project engineer in nuclear waste storage and disposal!

Research

My research focus still lies on interrogating human health and disease.

During my PhD I started to delve into Python and R and I try to moderate between the wet-lab point of view and the bioinformation point of view when approaching bioinformatics problems.

Current Projects

Kinase Resistance

Collaborative project with other members of the Russell lab. This is a data-driven approach to predict the functional consequence of genetic changes in protein kinases. You can find the preprint here and the associated webserver is called Activark. My contributions were the wet lab experiments, the alignment viewer (also see my other associated project below) and conceptual ideas (see Proteorizer for familiar ideas).

A variant of this is already used in the Kinase Resistance project. The version here will be more generalized and hence hopefully more useful for the wider scientific community. This work does not yet have it's own technical paper, if you are using it please cite my proteorizer prepring:

Torsten Schmenger, Gaurav Diwan, Robert Bruce Russell. "PROTEORIZER: A holistic approach to untangle functional consequences of variants of unknown significance", https://doi.org/10.1101/2024.07.16.603688.

This is my pet project. It follows a holistic approach by combining a trove of publicly available information on a protein of interest to determine whether a variants may be functional, and then also providing hints at how the particular variants may influence protein function. I am using data mining as well as interrogation of 3D structures combined with naive bayesian combination and a random forest classifier to arrive at a verdict. This aproach has a fully functional web app where the user simply provides a protein (gene name or uniprot accession) and a variant (or multiple variants), coded in R-Shiny.

Please find my preprint here:

Torsten Schmenger, Gaurav Diwan, Robert Bruce Russell. "PROTEORIZER: A holistic approach to untangle functional consequences of variants of unknown significance", https://doi.org/10.1101/2024.07.16.603688.

Paper Selection

  • Schmenger, T., Diwan, G.D., Singh, G. et al. Never-homozygous genetic variants in healthy populations are potential recessive disease candidates. npj Genom. Med. 7, 54 (2022). https://doi.org/10.1038/s41525-022-00322-z

  • Samantha Ebersoll, Blessing Musunda, Torsten Schmenger, Natalie Dirdjaja, Mariana Bonilla, Bruno Manta, Kathrin Ulrich, Marcelo A. Comini, R. Luise Krauth-Siegel, A glutaredoxin in the mitochondrial intermembrane space has stage-specific functions in the thermo-tolerance and proliferation of African trypanosomes, Redox Biology, Volume 15, 2018, https://doi.org/10.1016/j.redox.2018.01.011

  • Osswald, M., Jung, E., Sahm, F. et al. Brain tumour cells interconnect to a functional and resistant network. Nature 528, 93–98 (2015). https://doi.org/10.1038/nature16071

Links

Researchgate

Google Scholar

Linkedin

Popular repositories Loading

  1. Annotate_Alignments Annotate_Alignments Public

    A pythonic way to annotate protein alignments

    Python

  2. PROTEORIZER PROTEORIZER Public

    An explorative tool to identify functional hotspots

    Python

  3. tschmenger tschmenger Public

  4. imagej.github.io imagej.github.io Public

    Forked from imagej/imagej.github.io

    The ImageJ wiki

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  5. list-of-update-sites list-of-update-sites Public

    Forked from imagej/list-of-update-sites

    The update sites ImageJ2 knows about automatically

    Python