E-Mail: daria.meyer@uni-jena.de
Room: 4003
Phone: +49-3641-9-45498
Publications
2026
Meyer, Daria; Göttsch, Winfried; Spangenberg, Jannes; Stieber, Bettina; Krautwurst, Sebastian; Hölzer, Martin; Brandt, Christian; Linde, Jörg; zu Siederdissen, Christian Höner; Srivastava, Akash; Zarkovic, Milena; Wollny, Damian; Marz, Manja
Unlocking the full potential of nanopore sequencing: tips, tricks, and advanced data analysis techniques Journal Article
In: Nucleic Acids Research, vol. 54, iss. 3, no. gkag023, 2026.
@article{nokey_104,
title = {Unlocking the full potential of nanopore sequencing: tips, tricks, and advanced data analysis techniques},
author = {Daria Meyer and Winfried Göttsch and Jannes Spangenberg and Bettina Stieber and Sebastian Krautwurst and Martin Hölzer and Christian Brandt and Jörg Linde and Christian Höner zu Siederdissen and Akash Srivastava and Milena Zarkovic and Damian Wollny and Manja Marz},
doi = {10.1093/nar/gkag023},
year = {2026},
date = {2026-02-10},
urldate = {2025-01-27},
journal = {Nucleic Acids Research},
volume = {54},
number = {gkag023},
issue = {3},
abstract = {Nucleic acid sequencing is the process of identifying the sequence of DNA or RNA, with DNA used for genomes and RNA for transcriptomes. Deciphering this information has the potential to greatly advance our understanding of genomic features and cellular functions. In comparison to other available sequencing methods, nanopore sequencing stands out due to its unique advantages of processing long nucleic acid strands in real time, within a small portable device, enabling the rapid analysis of samples in diverse settings. Evolving over the past decade, nanopore sequencing remains in a state of ongoing development and refinement, resulting in persistent challenges in protocols and technology. This article employs an interdisciplinary approach, evaluating experimental and computational methods to address critical gaps in our understanding in order to maximize the information gain from this advancing technology. Here, we present both overview and analysis of all aspects of nanopore sequencing by providing statistically supported insights. Thus, we aim to provide fresh perspectives on nanopore sequencing and give comprehensive guidelines for the diverse challenges that frequently impede optimal experimental outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nucleic acid sequencing is the process of identifying the sequence of DNA or RNA, with DNA used for genomes and RNA for transcriptomes. Deciphering this information has the potential to greatly advance our understanding of genomic features and cellular functions. In comparison to other available sequencing methods, nanopore sequencing stands out due to its unique advantages of processing long nucleic acid strands in real time, within a small portable device, enabling the rapid analysis of samples in diverse settings. Evolving over the past decade, nanopore sequencing remains in a state of ongoing development and refinement, resulting in persistent challenges in protocols and technology. This article employs an interdisciplinary approach, evaluating experimental and computational methods to address critical gaps in our understanding in order to maximize the information gain from this advancing technology. Here, we present both overview and analysis of all aspects of nanopore sequencing by providing statistically supported insights. Thus, we aim to provide fresh perspectives on nanopore sequencing and give comprehensive guidelines for the diverse challenges that frequently impede optimal experimental outcomes.
Meyer, Daria; Barth, Emanuel; Wiehle, Laura; Marz, Manja
diffMONT: predicting methylation-specific PCR biomarkers based on nanopore sequencing data for clinical application Journal Article
In: Bioinformatics, vol. 42, iss. 2, no. btag039, 2026.
@article{nokey_102,
title = {diffMONT: predicting methylation-specific PCR biomarkers based on nanopore sequencing data for clinical application},
author = {Daria Meyer and Emanuel Barth and Laura Wiehle and Manja Marz},
doi = {10.1093/bioinformatics/btag039},
year = {2026},
date = {2026-02-01},
urldate = {2026-02-01},
journal = {Bioinformatics},
volume = {42},
number = {btag039},
issue = {2},
abstract = {Motivation
DNA methylation serves as a key biomarker in clinical diagnostics, especially in cancer detection. With methylation-specific PCR (MSP), a widely used approach, patient samples can be screened fast and efficiently for differential methylation. During MSP, methylated regions are selectively amplified with specific primers. With nanopore sequencing, knowledge about DNA methylation is generated during direct DNA sequencing without needing pretreatment of the DNA. Multiple methods, mainly developed for whole-genome bisulfite sequencing (WGBS) data, exist to predict differentially methylated regions (DMRs) in the genome. However, the predicted DMRs are often very large and not sufficiently discriminating to generate meaningful results in MSP, creating a gap between theoretical cancer marker research and practical application, as no tool currently provides methylation difference predictions tailored for PCR-based diagnostics.
Results
Here, we present diffMONT, a tool that predicts differentially methylated regions specifically suited for MSP primer design, enabling rapid translation into practical applications. diffMONT takes into account (i) the specific length of primer and amplicon regions, (ii) the fact that one condition should be unmethylated, and (iii) a minimal required amount of differentially methylated cytosines within the primer regions. We compared the results of diffMONT to metilene and DSS based on a publicly available nanopore sequencing dataset and show that the regions predicted by diffMONT are more specific toward hypermethylated regions. diffMONT accelerates the design of methylation-specific diagnostic assays, bridging the gap between theoretical research and clinical application.
Availability and implementation
The source code for diffMONT, an open-source Python-based tool, is available at https://github.com/rnajena/diffMONT/, with an archived release under https://zenodo.org/records/17641031.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Motivation
DNA methylation serves as a key biomarker in clinical diagnostics, especially in cancer detection. With methylation-specific PCR (MSP), a widely used approach, patient samples can be screened fast and efficiently for differential methylation. During MSP, methylated regions are selectively amplified with specific primers. With nanopore sequencing, knowledge about DNA methylation is generated during direct DNA sequencing without needing pretreatment of the DNA. Multiple methods, mainly developed for whole-genome bisulfite sequencing (WGBS) data, exist to predict differentially methylated regions (DMRs) in the genome. However, the predicted DMRs are often very large and not sufficiently discriminating to generate meaningful results in MSP, creating a gap between theoretical cancer marker research and practical application, as no tool currently provides methylation difference predictions tailored for PCR-based diagnostics.
Results
Here, we present diffMONT, a tool that predicts differentially methylated regions specifically suited for MSP primer design, enabling rapid translation into practical applications. diffMONT takes into account (i) the specific length of primer and amplicon regions, (ii) the fact that one condition should be unmethylated, and (iii) a minimal required amount of differentially methylated cytosines within the primer regions. We compared the results of diffMONT to metilene and DSS based on a publicly available nanopore sequencing dataset and show that the regions predicted by diffMONT are more specific toward hypermethylated regions. diffMONT accelerates the design of methylation-specific diagnostic assays, bridging the gap between theoretical research and clinical application.
Availability and implementation
The source code for diffMONT, an open-source Python-based tool, is available at https://github.com/rnajena/diffMONT/, with an archived release under https://zenodo.org/records/17641031.
DNA methylation serves as a key biomarker in clinical diagnostics, especially in cancer detection. With methylation-specific PCR (MSP), a widely used approach, patient samples can be screened fast and efficiently for differential methylation. During MSP, methylated regions are selectively amplified with specific primers. With nanopore sequencing, knowledge about DNA methylation is generated during direct DNA sequencing without needing pretreatment of the DNA. Multiple methods, mainly developed for whole-genome bisulfite sequencing (WGBS) data, exist to predict differentially methylated regions (DMRs) in the genome. However, the predicted DMRs are often very large and not sufficiently discriminating to generate meaningful results in MSP, creating a gap between theoretical cancer marker research and practical application, as no tool currently provides methylation difference predictions tailored for PCR-based diagnostics.
Results
Here, we present diffMONT, a tool that predicts differentially methylated regions specifically suited for MSP primer design, enabling rapid translation into practical applications. diffMONT takes into account (i) the specific length of primer and amplicon regions, (ii) the fact that one condition should be unmethylated, and (iii) a minimal required amount of differentially methylated cytosines within the primer regions. We compared the results of diffMONT to metilene and DSS based on a publicly available nanopore sequencing dataset and show that the regions predicted by diffMONT are more specific toward hypermethylated regions. diffMONT accelerates the design of methylation-specific diagnostic assays, bridging the gap between theoretical research and clinical application.
Availability and implementation
The source code for diffMONT, an open-source Python-based tool, is available at https://github.com/rnajena/diffMONT/, with an archived release under https://zenodo.org/records/17641031.
2025
Meyer, Daria; Hennig, Anne; Hums, Anna-Bawany; Guntinas-Lichius, Orlando; Schmitz, Martina; Marz, Manja
Nanopore sequencing-derived methylation biomarker prediction for methylation-specific PCR in patients with head and neck squamous cell carcinoma Journal Article
In: Clinical Epigenetics, vol. 17, 2025.
@article{nokey_88,
title = {Nanopore sequencing-derived methylation biomarker prediction for methylation-specific PCR in patients with head and neck squamous cell carcinoma},
author = {Daria Meyer and Anne Hennig and Anna-Bawany Hums and Orlando Guntinas-Lichius and Martina Schmitz and Manja Marz },
doi = {10.1186/s13148-025-01960-7},
year = {2025},
date = {2025-09-13},
journal = {Clinical Epigenetics},
volume = {17},
abstract = {DNA methylation of CpG islands is altered in cancer cells. Hypermethylation of single CpG islands in the promoter regions of tumor-suppressor genes occurs already in the early stages of cancer. These methylation changes are cancer-type specific and therefore can serve as early cancer biomarker. Identifying good and reliable biomarkers is crucial for the development of diagnostic tests and their application in clinical practice and remains the most significant challenge to date.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
DNA methylation of CpG islands is altered in cancer cells. Hypermethylation of single CpG islands in the promoter regions of tumor-suppressor genes occurs already in the early stages of cancer. These methylation changes are cancer-type specific and therefore can serve as early cancer biomarker. Identifying good and reliable biomarkers is crucial for the development of diagnostic tests and their application in clinical practice and remains the most significant challenge to date.
2024
Meyer, Daria; Kosacka, Joanna; von Bergen, Martin; Christ, Bruno; Marz, Manja
Data report on gene expression after hepatic portal vein ligation (PVL) in rats Journal Article
In: Front Genet, vol. 15, pp. 1421955, 2024.
@article{nokey,
title = {Data report on gene expression after hepatic portal vein ligation (PVL) in rats},
author = {Daria Meyer and Joanna Kosacka and Martin von Bergen and Bruno Christ and Manja Marz},
doi = {10.3389/fgene.2024.1421955},
year = {2024},
date = {2024-08-21},
journal = {Front Genet},
volume = {15},
pages = {1421955},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2021
Christ, Bruno; Collatz, Maximilian; Dahmen, Uta; Herrmann, Karl-Heinz; Höpfl, Sebastian; König, Matthias; Lambers, Lena; Marz, Manja; Meyer, Daria; Radde, Nicole; Reichenbach, Jürgen R.; Ricken, Tim; Tautenhahn, Hans-Michael
In: Front Physiol, vol. 12, pp. 733868, 2021.
@article{Christ2021,
title = {Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function},
author = {Bruno Christ and Maximilian Collatz and Uta Dahmen and Karl-Heinz Herrmann and Sebastian Höpfl and Matthias König and Lena Lambers and Manja Marz and Daria Meyer and Nicole Radde and Jürgen R. Reichenbach and Tim Ricken and Hans-Michael Tautenhahn},
doi = {10.3389/fphys.2021.733868},
year = {2021},
date = {2021-11-18},
urldate = {2021-11-18},
journal = {Front Physiol},
volume = {12},
pages = {733868},
abstract = {Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.