TY  - JOUR
A1  - Luo, Ting
A1  - Chen, Xiaoyi
A1  - Zeng, Shufei
A1  - Guan, Baozhang
A1  - Hu, Bo
A1  - Meng, Yu
A1  - Liu, Fanna
A1  - Wong, Taksui
A1  - Lu, Yongpin
A1  - Yun, Chen
A1  - Hocher, Berthold
A1  - Yin, Lianghong
T1  - Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma
JF  - Oncology Letters
N2  - 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.
KW  - clear cell renal cell carcinoma
KW  - bioinformatics
KW  - differentially expressed genes
KW  - biomarkers
KW  - Kaplan-Meier plot
Y1  - 2018
U6  - https://doi.org/10.3892/ol.2018.8842
SN  - 1792-1074
SN  - 1792-1082
VL  - 16
IS  - 2
SP  - 1747
EP  - 1757
PB  - Spandidos publ LTD
CY  - Athens
ER  - 
TY  - JOUR
A1  - Barlow, Axel
A1  - Hartmann, Stefanie
A1  - Gonzalez, Javier
A1  - Hofreiter, Michael
A1  - Paijmans, Johanna L. A.
T1  - Consensify
BT  - a method for generating pseudohaploid genome sequences from palaeogenomic datasets with reduced error rates
JF  - Genes / Molecular Diversity Preservation International
N2  - 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.
KW  - palaeogenomics
KW  - ancient DNA
KW  - sequencing error
KW  - error reduction
KW  - D statistics
KW  - bioinformatics
Y1  - 2020
U6  - https://doi.org/10.3390/genes11010050
SN  - 2073-4425
VL  - 11
IS  - 1
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Gebser, Martin
A1  - Schaub, Torsten H.
A1  - Thiele, Sven
A1  - Veber, Philippe
T1  - Detecting inconsistencies in large biological networks with answer set programming
JF  - Theory and practice of logic programming
N2  - 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.
KW  - answer set programming
KW  - bioinformatics
KW  - consistency
KW  - diagnosis
Y1  - 2011
U6  - https://doi.org/10.1017/S1471068410000554
SN  - 1471-0684
VL  - 11
IS  - 5-6
SP  - 323
EP  - 360
PB  - Cambridge Univ. Press
CY  - New York
ER  - 
TY  - JOUR
A1  - Frioux, Clémence
A1  - Schaub, Torsten H.
A1  - Schellhorn, Sebastian
A1  - Siegel, Anne
A1  - Wanko, Philipp
T1  - Hybrid metabolic network completion
JF  - Theory and practice of logic programming
N2  - 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.
KW  - answer set programming
KW  - metabolic network
KW  - gap-filling
KW  - linear programming
KW  - hybrid solving
KW  - bioinformatics
Y1  - 2018
U6  - https://doi.org/10.1017/S1471068418000455
SN  - 1471-0684
SN  - 1475-3081
VL  - 19
IS  - 1
SP  - 83
EP  - 108
PB  - Cambridge University Press
CY  - New York
ER  - 
TY  - JOUR
A1  - Christopher Ashwood, Wout Bittremieux
A1  - Bittremieux, Wout
A1  - Deutsch, Eric W.
A1  - Doncheva, Nadezhda T.
A1  - Dorfer, Viktoria
A1  - Gabriels, Ralf
A1  - Gorshkov, Vladimir
A1  - Gupta, Surya
A1  - Jones, Andrew R.
A1  - Käll, Lukas
A1  - Kopczynski, Dominik
A1  - Lane, Lydie
A1  - Lautenbacher, Ludwig
A1  - Legeay, Marc
A1  - Locard-Paulet, Marie
A1  - Mesuere, Bart
A1  - Sachsenberg, Timo
A1  - Salz, Renee
A1  - Samaras, Patroklos
A1  - Schiebenhoefer, Henning
A1  - Schmidt, Tobias
A1  - Schwämmle, Veit
A1  - Soggiu, Alessio
A1  - Uszkoreit, Julian
A1  - Van Den Bossche, Tim
A1  - Van Puyvelde, Bart
A1  - Van Strien, Joeri
A1  - Verschaffelt, Pieter
A1  - Webel, Henry
A1  - Willems, Sander
A1  - Perez-Riverolab, Yasset
A1  - Netz, Eugen
A1  - Pfeuffer, Julianus
T1  - Proceedings of the EuBIC-MS 2020 Developers’ Meeting
JF  - EuPA Open Proteomics
N2  - 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.
KW  - computational mass spectrometry
KW  - proteomics
KW  - bioinformatics
KW  - spectrum clustering
KW  - phosphoproteomics
KW  - XIC extraction
KW  - proteomics graph networks
KW  - predicted spectra
Y1  - 2020
U6  - https://doi.org/10.1016/j.euprot.2020.11.001
SN  - 2212-9685
VL  - 24
SP  - 1
EP  - 6
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Kuckelkorn, Ulrike
A1  - Stübler, Sabine
A1  - Textoris-Taube, Kathrin
A1  - Kilian, Christiane
A1  - Niewienda, Agathe
A1  - Henklein, Petra
A1  - Janek, Katharina
A1  - Stumpf, Michael P. H.
A1  - Mishto, Michele
A1  - Liepe, Juliane
T1  - Proteolytic dynamics of human 20S thymoproteasome
JF  - The journal of biological chemistry
N2  - 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.
KW  - proteasome
KW  - protein degradation
KW  - antigen processing
KW  - computational biology
KW  - bioinformatics
KW  - thymoproteasome
KW  - thymus
KW  - proteolysis
Y1  - 2019
U6  - https://doi.org/10.1074/jbc.RA118.007347
SN  - 1083-351X
VL  - 294
IS  - 19
SP  - 7740
EP  - 7754
PB  - American Society for Biochemistry and Molecular Biology
CY  - Bethesda
ER  -