Seifert Lab: Medical Bioinformatics
The fast progress in the development of experimental technologies enables to measure molecular data of complex diseases with unprecedented detail. The analysis of such heterogeneous data sets requires specific bioinformatic tools to disentangle the complex interplay of different regulatory layers associated with disease-causing processes.
The focus of the research group Medical Bioinformatics is on the analysis of molecular high-throughput data for specific research questions connected to human diseases and on the development of corresponding innovative bioinformatic approaches and methods for the integrative analysis of the different molecular layers of such data sets. The research group can also offer support for data analyses based on bioinformatic consulting and extended service projects.
Research and service focus
- Analysis of molecular high-throughput data (e.g. transcriptome, epigenome, genome, proteome, metabolome) measured by bulk sequencing, single-cell sequencing, microarrays or other technologies
- Development of network-based methods and probabilistic models for the integrative analysis of heterogeneous molecular high-throughput data
- Identification and characterization of molecular alterations and their associations with subgroups and clinical data
- Application of existing and newly developed data analysis strategies for computational cancer research (e.g. melanoma, astrocytoma, oligodendroglioma, leukemia, head and neck cancer, ovarian cancer)
Selected Projects
- Junior Associations MelBrainSys — Model-based prediction and experimental validation of novel therapeutic interventions for melanoma brain metastases (BMBF)
- Reconstruction of the evolutionary development of ovarian cancer
- Identification of novel cancer candidate genes for oligodendrogliomas
- Comparative preclinical drug response analysis of T-cell prolymphocytic leukemia
- Identification and characterization of gene expression differences between melanoma metastases in different organs
- Determination of molecular signatures associated with survival of acute myeloid leukemia
- Single-cell based transcriptome comparisons of brain and non-brain metastases of melanomas
Group leader
© Michael Seifert
Group leader
NameMr PD Dr. rer. nat. habil. Michael Seifert
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Members of the research group
© Luca Penso Dolfin
Research fellow
NameMr Dr. Luca Penso Dolfin
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Students (MD/ Ph.D. students, student assistants)
© Natalie Labitzke
MD student
NameMs Natalie Labitzke
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© Niclas Lange
MD student
NameMr Niclas Lange
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© Inja Lenhardt
MD student
NameMs J. M. Inja Lenhardt
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© Xuliang Xu
research assistant
NameMr Xuliang Xu B.Sc.
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Former staff members
© Felix Broghammer
Research fellow (external: OncoRay)
NameMr Felix Broghammer M.Sc.
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© Felicitas-Berenike Broll
MD student
NameMs Felicitas-Berenike Broll
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© Julia Figueroa Martinez
Master student
NameMs Julia Figueroa Martinez
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MD student
NameMr Josef Gladitz
Else-Kröner-Stiftung
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© Konrad Gruetzmann
Research fellow
NameMr Dr. rer. nat. Konrad Grützmann
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© Cosima Hufnagel
MD student
NameMs Cosima Hufnagel
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© Theresa Kraft
Research fellow
NameMs Theresa Kraft M.Sc.
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© Chris Lauber
Research fellow
NameMr Dr. Chris Lauber
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student assistant
NameMs Olena Maiakovska B.Sc.
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student assistant
NameMr Assad Majid B.Sc.
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Research fellow
NameMr Dr. rer. nat. Nathan Mikhaylenko
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Ph.D. student
NameMr Adrian Ortiz Cervantes M.Sc.
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© Pirasteh Pahlavan
Research fellow (external: NCT)
NameMs Dr. rer. nat. Pirasteh Pahlavan
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© Michael Rank
Research fellow
NameMr Michael Rank M.Sc.
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© Lisa Schöne
Research fellow (external: NCT)
NameMs Lisa Schöne M.Sc.
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© Annemarie Schwarz
MD student
NameMs Annemarie Schwarz
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MD student
NameMs Lisa Sturm
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© Marc-Christoph Wagner
Research fellow
NameMr Marc-Christoph Wagner M.Sc.
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Publications of the lab
2026
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Network-Based Prediction of Oligodendroglioma Driver Gene Candidates within the Region of the 1p/19q Co-deletion Utilizing Single-Cell Transcriptomes, 4 May 2026, In: Computational and Structural Biotechnology Journal. 35, 1, 0059Electronic (full-text) versionResearch output: Contribution to journal > Research article
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GOT1 Inhibition Induces Extracellular Matrix Remodeling in Pancreatic Cancer, 3 Mar 2026, In: Advanced Science. 13, 13, e16578Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Development of a highly differentiated rat brain organoid model for exploring glioblastoma invasion dynamics and therapy, Mar 2026, In: Neuro-oncology. 28, 3, p. 626-642, 17 p., noaf271Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Molecular genotype-phenotype correlation in ACTB- and ACTG1-related non-muscle actinopathies, 5 Feb 2026, In: American journal of human genetics. 113, 2, p. 324-341, 18 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article
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A biomaterial-based platform of pancreatic cancer reveals kallikrein-related peptidase 6 (KLK6) as a mediator of neutrophil recruitment and immunosuppression, Jan 2026, In: Biomaterials. 324, 123456Electronic (full-text) versionResearch output: Contribution to journal > Research article
2025
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Human fingertip regeneration follows clinical phases with distinct proteomic signatures, 5 Nov 2025, In: npj Regenerative Medicine. 10, 1, 51Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Comparative preclinical drug response analyses of T-prolymphocytic leukemia reveal no differences between known gene expression subgroups, 27 Oct 2025, In: Biology direct. 20, 1, 106Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Single-cell transcriptome analysis suggests cells of the tumor microenvironment as a major discriminator between brain and extracranial melanoma metastases, 16 Sep 2025, In: Biology direct. 20, 1, 97Electronic (full-text) versionResearch output: Contribution to journal > Research article
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A novel role of exostosin glycosyltransferase 2 (EXT2) in glioblastoma cell metabolism, radiosensitivity and ferroptosis, Sep 2025, In: Cell death and differentiation. 32, 9, p. 1664–1678, 15 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article
2024
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Identification of novel snoRNA-based biomarkers for clear cell renal cell carcinoma from urine-derived extracellular vesicles, 13 May 2024, In: Biology direct. 19, 1, 15 p., 38Electronic (full-text) versionResearch output: Contribution to journal > Research article
This information is provided by the FIS.