@article{KuckelkornStueblerTextorisTaubeetal.2019, author = {Kuckelkorn, Ulrike and St{\"u}bler, Sabine and Textoris-Taube, Kathrin and Kilian, Christiane and Niewienda, Agathe and Henklein, Petra and Janek, Katharina and Stumpf, Michael P. H. and Mishto, Michele and Liepe, Juliane}, title = {Proteolytic dynamics of human 20S thymoproteasome}, series = {The journal of biological chemistry}, volume = {294}, journal = {The journal of biological chemistry}, number = {19}, publisher = {American Society for Biochemistry and Molecular Biology}, address = {Bethesda}, issn = {1083-351X}, doi = {10.1074/jbc.RA118.007347}, pages = {7740 -- 7754}, year = {2019}, abstract = {An efficient immunosurveillance of CD8(+) T cells in the periphery depends on positive/negative selection of thymocytes and thus on the dynamics of antigen degradation and epitope production by thymoproteasome and immunoproteasome in the thymus. Although studies in mouse systems have shown how thymoproteasome activity differs from that of immunoproteasome and strongly impacts the T cell repertoire, the proteolytic dynamics and the regulation of human thymoproteasome are unknown. By combining biochemical and computational modeling approaches, we show here that human 20S thymoproteasome and immunoproteasome differ not only in the proteolytic activity of the catalytic sites but also in the peptide transport. These differences impinge upon the quantity of peptide products rather than where the substrates are cleaved. The comparison of the two human 20S proteasome isoforms depicts different processing of antigens that are associated to tumors and autoimmune diseases.}, language = {en} } @article{ChristopherAshwoodBittremieuxDeutschetal.2020, author = {Christopher Ashwood, Wout Bittremieux and Bittremieux, Wout and Deutsch, Eric W. and Doncheva, Nadezhda T. and Dorfer, Viktoria and Gabriels, Ralf and Gorshkov, Vladimir and Gupta, Surya and Jones, Andrew R. and K{\"a}ll, Lukas and Kopczynski, Dominik and Lane, Lydie and Lautenbacher, Ludwig and Legeay, Marc and Locard-Paulet, Marie and Mesuere, Bart and Sachsenberg, Timo and Salz, Renee and Samaras, Patroklos and Schiebenhoefer, Henning and Schmidt, Tobias and Schw{\"a}mmle, Veit and Soggiu, Alessio and Uszkoreit, Julian and Van Den Bossche, Tim and Van Puyvelde, Bart and Van Strien, Joeri and Verschaffelt, Pieter and Webel, Henry and Willems, Sander and Perez-Riverolab, Yasset and Netz, Eugen and Pfeuffer, Julianus}, title = {Proceedings of the EuBIC-MS 2020 Developers' Meeting}, series = {EuPA Open Proteomics}, volume = {24}, journal = {EuPA Open Proteomics}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-9685}, doi = {10.1016/j.euprot.2020.11.001}, pages = {1 -- 6}, year = {2020}, abstract = {The 2020 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers' meeting was held from January 13th to January 17th 2020 in Nyborg, Denmark. Among the participants were scientists as well as developers working in the field of computational mass spectrometry (MS) and proteomics. The 4-day program was split between introductory keynote lectures and parallel hackathon sessions. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts, and to actively contribute to highly relevant research projects. We successfully produced several new tools that will be useful to the proteomics community by improving data analysis as well as facilitating future research. All keynote recordings are available on https://doi.org/10.5281/zenodo.3890181.}, language = {en} } @article{FriouxSchaubSchellhornetal.2019, author = {Frioux, Cl{\´e}mence and Schaub, Torsten H. and Schellhorn, Sebastian and Siegel, Anne and Wanko, Philipp}, title = {Hybrid metabolic network completion}, series = {Theory and practice of logic programming}, volume = {19}, journal = {Theory and practice of logic programming}, number = {1}, publisher = {Cambridge University Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068418000455}, pages = {83 -- 108}, year = {2019}, abstract = {Metabolic networks play a crucial role in biology since they capture all chemical reactions in an organism. While there are networks of high quality for many model organisms, networks for less studied organisms are often of poor quality and suffer from incompleteness. To this end, we introduced in previous work an answer set programming (ASP)-based approach to metabolic network completion. Although this qualitative approach allows for restoring moderately degraded networks, it fails to restore highly degraded ones. This is because it ignores quantitative constraints capturing reaction rates. To address this problem, we propose a hybrid approach to metabolic network completion that integrates our qualitative ASP approach with quantitative means for capturing reaction rates. We begin by formally reconciling existing stoichiometric and topological approaches to network completion in a unified formalism. With it, we develop a hybrid ASP encoding and rely upon the theory reasoning capacities of the ASP system dingo for solving the resulting logic program with linear constraints over reals. We empirically evaluate our approach by means of the metabolic network of Escherichia coli. Our analysis shows that our novel approach yields greatly superior results than obtainable from purely qualitative or quantitative approaches.}, language = {en} } @article{GebserSchaubThieleetal.2011, author = {Gebser, Martin and Schaub, Torsten H. and Thiele, Sven and Veber, Philippe}, title = {Detecting inconsistencies in large biological networks with answer set programming}, series = {Theory and practice of logic programming}, volume = {11}, journal = {Theory and practice of logic programming}, number = {5-6}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068410000554}, pages = {323 -- 360}, year = {2011}, abstract = {We introduce an approach to detecting inconsistencies in large biological networks by using answer set programming. To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on answer set programming to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.}, language = {en} } @article{BarlowHartmannGonzalezetal.2020, author = {Barlow, Axel and Hartmann, Stefanie and Gonzalez, Javier and Hofreiter, Michael and Paijmans, Johanna L. A.}, title = {Consensify}, series = {Genes / Molecular Diversity Preservation International}, volume = {11}, journal = {Genes / Molecular Diversity Preservation International}, number = {1}, publisher = {MDPI}, address = {Basel}, issn = {2073-4425}, doi = {10.3390/genes11010050}, pages = {22}, year = {2020}, abstract = {A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, frequently by selecting a single high-quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage, but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences, which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic and population clustering analysis, we find that Consensify is less affected by artefacts than methods based on single read sampling. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other frequently used methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consensify will be a useful tool for future studies of palaeogenomes.}, language = {en} } @article{LuoChenZengetal.2018, author = {Luo, Ting and Chen, Xiaoyi and Zeng, Shufei and Guan, Baozhang and Hu, Bo and Meng, Yu and Liu, Fanna and Wong, Taksui and Lu, Yongpin and Yun, Chen and Hocher, Berthold and Yin, Lianghong}, title = {Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma}, series = {Oncology Letters}, volume = {16}, journal = {Oncology Letters}, number = {2}, publisher = {Spandidos publ LTD}, address = {Athens}, issn = {1792-1074}, doi = {10.3892/ol.2018.8842}, pages = {1747 -- 1757}, year = {2018}, abstract = {The present study aimed to identify new key genes as potential biomarkers for the diagnosis, prognosis or targeted therapy of clear cell renal cell carcinoma (ccRCC). Three expression profiles (GSE36895, GSE46699 and GSE71963) were collected from Gene Expression Omnibus. GEO2R was used to identify differentially expressed genes (DEGs) in ccRCC tissues and normal samples. The Database for Annotation, Visualization and Integrated Discovery was utilized for functional and pathway enrichment analysis. STRING v10.5 and Molecular Complex Detection were used for protein-protein interaction (PPI) network construction and module analysis, respectively. Regulation network analyses were performed with the WebGestal tool. UALCAN web-portal was used for expression validation and survival analysis of hub genes in ccRCC patients from The Cancer Genome Atlas (TCGA). A total of 65 up- and 164 downregulated genes were identified as DEGs. DEGs were enriched with functional terms and pathways compactly related to ccRCC pathogenesis. Seventeen hub genes and one significant module were filtered out and selected from the PPI network. The differential expression of hub genes was verified in TCGA patients. Kaplan-Meier plot showed that high mRNA expression of enolase 2 (ENO2) was associated with short overall survival in ccRCC patients (P=0.023). High mRNA expression of cyclin D1 (CCND1) (P<0.001), fms related tyrosine kinase 1 (FLT1) (P=0.004), plasminogen (PLG) (P<0.001) and von Willebrand factor (VWF) (P=0.008) appeared to serve as favorable factors in survival. These findings indicate that the DEGs may be key genes in ccRCC pathogenesis and five genes, including ENO2, CCND1, PLT1, PLG and VWF, may serve as potential prognostic biomarkers in ccRCC.}, language = {en} }