TY - CONF A1 - Holstein, Tanja A1 - Verschaffelt, P. A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - Probability based taxonomic profiling of viral and microbiome samples using PepGM and Unipept N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference in samples of unknown taxonomic origin. PepGM uses a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets providing taxonomic confidence scores. To build the graphical model, a list of potentially present taxa needs to be inferred. To this end, we integrate Unipept, which enables the fast querying of potentially present taxa. Together, they allow for taxonomic inference with statistically sound confidence scores. T2 - HUPO 2022 world congress CY - Cancun, Mexico DA - 04.12.2022 KW - Bioinformatics KW - Mass spectrometry KW - Taxonomic analysis KW - Microbiomes PY - 2022 AN - OPUS4-56748 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. heuristics. T2 - International conference on systems biology 2022 CY - Berlin, Germany DA - 07.10.2022 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2022 AN - OPUS4-56749 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. heuristics. T2 - European proteomics association conference 2022 CY - Leipzig, Germany DA - 03.04.2022 KW - graphical models KW - Taxonomic inference KW - Bioinformatics KW - Virus protoemics PY - 2022 AN - OPUS4-56750 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. T2 - European bioinformatics community winter school 2022 CY - Lisbon, Portugal DA - 21.03.2022 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2022 AN - OPUS4-56751 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. T2 - HUPO conference 2021 CY - Online meeting DA - 04.12.2021 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2021 AN - OPUS4-56752 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: A graphical model for taxonomic profiling of viral proteomes N2 - In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates taxonomic inference. inference. We introduce PepGM, a graphical model for taxonomic profiling of viral proteomes and metaproteomic datasets. Using the graphical model, our approach computes statistically sound scores for taxa based on peptide scores from a previous database search, eliminating the need for commonly used heuristics. T2 - European bioinformatics community winter school 2022 CY - Lisbon, Portugal DA - 21.03.2022 KW - Bioinformatics KW - Virus protoemics KW - Taxonomic analysis KW - Graphical models PY - 2022 AN - OPUS4-56753 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Holstein, Tanja A1 - Kurths, J. A1 - Wiedermann, M. T1 - Optimization of coupling and global collapse in diffusively coupled socio-ecological resource exploitation networks JF - New Journal of Physics N2 - Single- and multi-layer complex networks have been proven as a powerful tool to study the dynamics within social, technological, or natural systems. An often observed common goal is to optimize these systems for specific purposes by minimizing certain costs while maximizing a desired output. Acknowledging that especially real-world systems from the coupled socio-ecological realm are highly intertwined this work exemplifies that in such systems the optimization of a certain subsystem, e.g. to increase the resilience against external pressure in an ecological network, may unexpectedly diminish the stability of the whole coupled system. For this purpose we utilize an adaptation of a previously proposed conceptual bi-layer network model composed of an ecological network of diffusively coupled resources co-evolving with a social network of interacting agents that harvest these resources and learn each other’s strategies depending on individual success. We derive an optimal coupling strength that prevents collapse in as many resources as possible if one assumes that the agents’ strategies remain constant over time. We then show that if agents socially learn and adapt strategies according to their neighbors’ success, this optimal coupling strength is revealed to be a critical parameter above which the probability for a global collapse in terms of irreversibly depleted resources is high—an effect that we denote the tragedy of the optimizer. We thus find that measures which stabilize the Dynamics within a certain part of a larger co-evolutionary system may unexpectedly cause the emergence of novel undesired globally stable states. Our results therefore underline the importance of holistic approaches for managing socio-ecological systems because stabilizing effects which focus on single subsystems may be counter-beneficial for the system as a whole. KW - Network science KW - Complex systems KW - Socio-ecological models KW - Non linear dynamics KW - Physics KW - Physics and society PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-524176 DO - https://doi.org/10.1088/1367-2630/abe0db VL - 23 IS - 3 SP - 033027 PB - IOP Publishing CY - Bristol, England AN - OPUS4-52417 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Holstein, Tanja A1 - Kistner, Franziska A1 - Martens, L. A1 - Muth, Thilo T1 - PepGM: a probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores JF - Bioinformatics N2 - Motivation: Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides, which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, the correct taxonomic inference is crucial when identifying different viral strains with high-sequence homology—considering, e.g., the different epidemiological characteristics of the various strains of severe acute respiratory syndrome-related coronavirus-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples. Results: We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores. Availability and implementation: PepGM is written in Python and embedded into a Snakemake workflow. It is available at https://github.com/BAMeScience/PepGM. KW - Graphical model KW - Protemics KW - Virus KW - Ayesian models PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-577488 SN - 1367-4803 VL - 39 IS - 5 SP - 1 EP - 11 PB - Oxford University Press CY - Oxford, UK AN - OPUS4-57748 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -