Yanick Hagemeijer

Anna NiehuesI’m a PhD student at the University of Groningen (UG), where I work at the European Research Institute for the Biology of Ageing (ERIBA) and at Groningen Research Institute of Pharmacy (GRIP), my 2 promoters are Peter Horvatovich, from the department of Analytical Biochemistry at the UG and Victor Guryev from the Group of Genome Structure and Ageing (University Medical Center Groningen, UMCG) are both part of X-omics Netherlands Infrastructure Consortium as well. Currently, I am involved in the X-omics project with the goal of making a ‘portable’ pipeline to perform proteogenomics analysis from start to end.

I previously studied Bioinformatics at the Hogeschool van Arnhem & Nijmegen. During my Bsc I did an internship at the bacterial genomics group at the Center for Molecular and Biomolecular Informatics (CMBI). Following this research experience, I did another at the Hubrecht institute in Utrecht where Victor Guryev was my supervisor. After I graduated I went to Wageningen University & Research centre (WUR). Here, I did my master theses at the departments of Animal Breeding and Genetics (ABG) and Systems & Synthetic Biology (SSB). After finishing I applied for a job at the department of Experimental Cardiology at the UMCG. During my research there, I was working as a data-analyst/bioinformatician focused on performing GWAS analysis in the context of medical/cardiac phenotypes.

I regretted focusing solely on genomics and transcriptomics and not learning more about proteomics and other omics layers. This PhD within the X-omics Consortium allows me to work at the crossroads of sequencing and mass spectrometry data as part of the data integration and stewardship pillar. I’m interested in bringing the complexities of genomics and transcriptomics to the proteomics field. The integration of the patient specific protein variants to create personalized protein sequence databases, which can be used for database searching large LC-MS/MS datasets and to identify patient specific variants that have implications in complex diseases such as cancer and COPD. Currently, proteomics analysis pipelines rely on canonical sequences from (curated) public databases such as Ensembl and Uniprot. Simply including all possible ‘translate-able’ sequences leads to a large search space and low statistical power to identify protein variants. The goal of my PhD project is to provide a proteogenomics pipeline, which uses genomics and/or transcriptomics data to make a protein database containing all protein variants present in a clinical/biological proteomics sample that is both small and accurate without including large amounts of hypothetical proteins for which there is no support in the genomics or transcriptomics data. In case you have similar interests - let’s collaborate!

Anna Niehues

Anna NiehuesAnna Niehues is a postdoc at Radboudumc, Nijmegen, where she works at the Center for Molecular and Biomolecular Informatics (CMBI), led by Peter-Bram ’t Hoen, and the Translational Metabolic Laboratory, led by Alain van Gool. She is currently involved in the X-omics project and the European EATRIS-Plus project. Anna previously studied Biosciences at the University of Münster (WWU) in Germany, where she also obtained her PhD. She has been working as a bioinformatician on mass spectrometry data analysis in the context of quantitative proteomics, and structural analysis of carbohydrate oligo- and polymers. 

Within the X-omics project, she works as data scientist in the data analysis, integration and stewardship pillar, together with Peter-Bram ’t Hoen (work package leader) and Gurnoor Singh (data steward) from Radboudumc and other X-omics collaborators across the Netherlands. She is interested in the integration of mass spectrometry-derived data such as metabolomics data with other types of omics data in order to e.g. increase the power of biomarker studies or gain additional insights into the relationships between different molecular levels and their association with disease phenotypes. She is also interested in FAIR data analysis workflows which, alongside FAIR data, provide the means for reproducibility of multi-omics data analyses.

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NWO logoThis research was (partially) funded by NWO, project 184.034.019