@article{MeierGrawBeyerleinetal.2017, author = {Meier, Richard and Graw, Stefan and Beyerlein, Peter and Koestler, Devin and Molina, Julian R. and Chien, Jeremy}, title = {digit—a tool for detection and identification of genomic interchromosomal translocations}, series = {Nucleic Acids Research}, volume = {45}, journal = {Nucleic Acids Research}, number = {9}, issn = {1362-4962}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-9697}, pages = {e72}, year = {2017}, abstract = {Structural variations (SVs) in genomic DNA can have profound effects on the evolution of living organisms, on phenotypic variations and on disease processes. A critical step in discovering the full extent of structural variations is the development of tools to characterize these variations accurately in next generation sequencing data. Toward this goal, we developed a software pipeline named digit that implements a novel measure of mapping ambiguity to discover interchromosomal SVs from mate-pair and pair-end sequencing data. The workflow robustly handles the high numbers of artifacts present in mate-pair sequencing and reduces the false positive rate while maintaining sensitivity. In the simulated data set, our workflow recovered 96\% of simulated SVs. It generates a self-updating library of common translocations and allows for the investigation of patient- or group-specific events, making it suitable for discovering and cataloging chromosomal translocations associated with specific groups, traits, diseases or population structures.}, language = {en} } @article{MunchelHoangYueetal.2015, author = {Munchel, Sarah and Hoang, Yen and Yue, Zhao and Cottrell, Joseph and Klotzle, Brandy and Godwin, Andrew K. and Koestler, Devin and Beyerlein, Peter and Fan, Jian-Bing and Bibikova, Marina and Chien, Jeremy}, title = {Targeted or whole genome sequencing of formalin fixed tissue samples: potential applications in cancer genomics}, series = {Oncotarget}, volume = {6}, journal = {Oncotarget}, number = {28}, issn = {1949-2553}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-6307}, pages = {25943 -- 25961}, year = {2015}, abstract = {Current genomic studies are limited by the poor availability of fresh-frozen tissue samples. Although formalin-fixed diagnostic samples are in abundance, they are seldom used in current genomic studies because of the concern of formalin-fixation artifacts. Better characterization of these artifacts will allow the use of archived clinical specimens in translational and clinical research studies. To provide a systematic analysis of formalin-fixation artifacts on Illumina sequencing, we generated 26 DNA sequencing data sets from 13 pairs of matched formalin-fixed paraffin-embedded (FFPE) and fresh-frozen (FF) tissue samples. The results indicate high rate of concordant calls between matched FF/FFPE pairs at reference and variant positions in three commonly used sequencing approaches (whole genome, whole exome, and targeted exon sequencing). Global mismatch rates and C·G > T·A substitutions were comparable between matched FF/FFPE samples, and discordant rates were low (<0.26\%) in all samples. Finally, low-pass whole genome sequencing produces similar pattern of copy number alterations between FF/FFPE pairs. The results from our studies suggest the potential use of diagnostic FFPE samples for cancer genomic studies to characterize and catalog variations in cancer genomes.}, language = {en} } @article{GrawMeierMinnetal.2015, author = {Graw, Stefan and Meier, Richard and Minn, Kay and Bloomer, Clark and Godwin, Andrew K. and Fridley, Brooke and Vlad, Anda and Beyerlein, Peter and Chien, Jeremy}, title = {Robust gene expression and mutation analyses of RNA-sequencing of formalin-fixed diagnostic tumor samples}, series = {Scientific Reports}, volume = {5}, journal = {Scientific Reports}, issn = {2045-2322}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-6319}, pages = {11}, year = {2015}, abstract = {Current genomic studies are limited by the availability of fresh tissue samples. Here, we show that Illumina RNA sequencing of formalin-fixed diagnostic tumor samples produces gene expression that is strongly correlated with matched frozen tumor samples (r > 0.89). In addition, sequence variations identified from FFPE RNA show 99.67\% concordance with that from exome sequencing of matched frozen tumor samples. Because FFPE is a routine diagnostic sample preparation, the feasibility results reported here will facilitate the setup of large-scale research and clinical studies in medical genomics that are currently limited by the availability of fresh frozen samples.}, language = {en} } @article{SchillingBeyerleinChien2023, author = {Schilling, Vincent and Beyerlein, Peter and Chien, Jeremy}, title = {A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning}, series = {Algorithms}, volume = {16}, journal = {Algorithms}, number = {7}, publisher = {MDPI}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17751}, year = {2023}, abstract = {The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients' outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan-Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1.}, language = {en} } @article{KallabisBeyerleinLisdat2024, author = {Kallabis, Conrad and Beyerlein, Peter and Lisdat, Fred}, title = {Quantitative determination of dopamine in the presence of interfering substances supported by machine learning tools}, series = {Bioelectrochemistry}, volume = {157}, journal = {Bioelectrochemistry}, number = {108667}, issn = {1567-5394}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19277}, year = {2024}, abstract = {In the field of neuroscience as well as in the clinical setting, the neurotransmitter dopamine (DA) is an analyte which is important for research as well as medical purposes. There are plenty of methods available to measure dopamine quantitatively, with voltammetric ones such as differential pulse voltammetry (DPV) being among the most convenient and simple ones. However, dopamine often occurs, either naturally or because of the requirements of involved enzymatic systems, alongside substances that can influence the signal it produces upon electrochemical conversion. An example for such substances is the magnesium ion, which itself is not electrochemically active in the potential range needed for DA oxidation, but influences the dopamine signal. We have characterized the properties of DPV signals subject to the interaction between DA and Mg2+ and show that, although these properties are changing in a nonlinear fashion when both concentrations are varying, relatively simple linear mathematical models can be used to determine dopamine concentrations quantitatively in the presence of magnesium ions. The focus of this study is thus, the mathematical treatment of experimental data in order to overcome an analytical problem and not the investigation of the chemical background of DA-Mg2+ interaction.}, language = {en} }