Fellow group "Efficient Algorithms for Omics Data”
Prof. Dr. Knut Reinert
Our group is always looking for highly motivated and thoughtful computational doctoral candidates. We do algorithmic work and method development, and want to apply our methods to real data, preferably with experimental partners within the IMPRS-BAC. As such, we encourage computational-interested students to apply for a PhD in our group.
- We are working on all aspects to design and implement compact data structures for searching.
- We are working on detecting ecDNA in cancer.
- We address the problem of analyzing pangenomes.
In particular, we will work with the Waterfall group at the Institut Marie Curie in Paris to improve the Needle tool [1] for RNA-Sequencing quantification on hundreds of terabytes scale using the HIBF [2] data structure. If you are interested in how to handle truly big data and if you like developing tools, you should apply.
[1] Darvish, M., Seiler, E., Mehringer, S., Rahn, R., & Reinert, K. (2022).
Needle: a fast and space-efficient prefilter for estimating the quantification of very large collections of expression experiments.
Bioinformatics, 38(17), 4100–4108. https://doi.org/10.1093/bioinformatics/btac492
[2] Mehringer, S., Seiler, E., Droop, F., Darvish, M., Rahn, R., Vingron, M., & Reinert, K. (2023).
Hierarchical Interleaved Bloom Filter: Enabling ultrafast, approximate sequence queries.
Genome Biology, 1–25. https://doi.org/10.1186/s13059-023-02971-4
More information can be found on the website of the Reinert Lab.
© Reinert
