An integer linear programming approach for finding deregulated subgraphs in regulatory networks
Christina Backes,Alexander Rurainski,Gunnar W. Klau,Oliver Müller,Daniel Stöckel,Andreas Gerasch,Jan Küntzer,Daniela Maisel,Nicole Ludwig,Matthias Hein,Andreas Keller,Helmut Burtscher,Michael Kaufmann,Eckart Meese,Hans-Peter Lenhof +14 more
TLDR
The results suggest that oxidative stress plays an important role in epithelial cells of BRCA1 mutation carriers and that the activation of stress proteins may result in avoidance of apoptosis leading to an increased overall survival of cells with genetic alterations.Abstract:
Deregulation of cell signaling pathways plays a crucial role in the development of tumors. The identification of such pathways requires effective analysis tools that facilitate the interpretation of expression differences. Here, we present a novel and highly efficient method for identifying deregulated subnetworks in a regulatory network. Given a score for each node that measures the degree of deregulation of the corresponding gene or protein, the algorithm computes the heaviest connected
subnetwork of a specified size reachable from a designated root node. This root node can be interpreted as a molecular key player responsible for the observed deregulation. To demonstrate the potential of our approach, we analyzed three gene expression data sets. In one scenario, we compared expression profiles of non-malignant primary mammary epithelial cells derived from BRCA1 mutation carriers and of epithelial cells without BRCA1 mutation. Our results suggest that oxidative stress plays an important role in epithelial cells of BRCA1 mutation carriers and that the activation of stress proteins may result in avoidance of apoptosis leading to an increased overall survival of cells with genetic alterations. In summary, our approach opens new avenues for the elucidation of pathogenic mechanisms and for the detection of molecular key players.read more
Citations
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VirSorter: mining viral signal from microbial genomic data
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Integrative approaches for finding modular structure in biological networks
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Guidelines for Validation of Next-Generation Sequencing-Based Oncology Panels: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists.
Lawrence J. Jennings,Maria E. Arcila,Christopher L. Corless,Suzanne Kamel-Reid,Suzanne Kamel-Reid,Ira M. Lubin,John D. Pfeifer,Robyn L. Temple-Smolkin,Karl V. Voelkerding,Karl V. Voelkerding,Marina N. Nikiforova +10 more
TL;DR: The recommendations contained herein are intended to assist clinical laboratories with the validation and ongoing monitoring of NGS testing for detection of somatic variants and to ensure high quality of sequencing results.
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A scalable SCENIC workflow for single-cell gene regulatory network analysis.
Bram Van de Sande,Christopher Flerin,Kristofer Davie,Maxime de Waegeneer,Gert Hulselmans,Sara Aibar,Ruth Seurinck,Wouter Saelens,Robrecht Cannoodt,Robrecht Cannoodt,Quentin Rouchon,Toni Verbeiren,Dries De Maeyer,Joke Reumers,Yvan Saeys,Stein Aerts +15 more
TL;DR: A detailed protocol for pySCENIC: a faster, container-based implementation in Python, resulting in a tenfold increase in speed, and has been packaged into containers for ease of use is described.
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Viral dark matter and virus-host interactions resolved from publicly available microbial genomes
TL;DR: These data augment public data sets 10-fold, provide first viral sequences for 13 new bacterial phyla including ecologically abundant phyla, and help taxonomically identify 7–38% of ‘unknown’ sequence space in viromes, illustrating the value of mining viral signal from microbial genomes.
References
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