• Our aim is to advance our understanding of biological systems,

    ranging from single species to multi-species systems and ecosystems,

    based on data from large-scale bioanalytical methods.

  • We develop, improve and apply

    computational methods

    for the interpretation of molecular information in biology.

  • We establish and analyse

    quantitative mathematical models.

CUBE News

  • CUBE part of new Innovative Training Network VIROINF

    04.06.20
    News

    A team of CUBE and DOME researchers, and 14 other beneficiaries under the lead of the Friedrich Schiller University Jena in Germany, have recently applied for a Innovative Training Network that aims at the understanding of (harmful) virus-host interactions 15 by linking ...

  • Adrian Tett new group leader at CUBE

    20.04.20
    Personal

    Adrian Tett has been appointed as new group leader at CUBE. Adrian has worked before as Senior Research Associate in the Computational Metagenomics group of Nicola Segata in Trento. His scientific interests include detailing the microbiome in relation to health, investigating ...

  • Dr. rer nat Hans-Jörg Hellinger

    13.04.20
    Personal

    Even if the University is closed due to the ongoing COVID-19 pandemy, CUBE keeps going on. On Wednesday, April 8, Hans-Jörg Hellinger finished his PhD in an online defensio. In his dissertation, he worked on comparative genomics of viruses and prokaryotes. Well done, ...

  • CUBE is part of new research platform MetaBac

    01.03.20
    News

    The research platform "Secondary Metabolomes of Bacterial Communities (MetaBac)”, linking the research groups of Sergey Zotchev, Martion Zehl, Alex Loy and Thomas Rattei from CUBE, has been selected for funding by the University of Vienna. 

    Many bacterial species ...

Latest publications

SciPy 1.0: fundamental algorithms for scientific computing in Python.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat İ, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P
2020 - Nat. Methods, in press

scikit-hubness: Hubness Reduction and Approximate Neighbor Search

scikit-hubness is a Python package for efficient nearest neighbor search in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality in nearest neighbor graphs. Specifically, it describes the increasing occurrence of hubs and antihubs with growing data dimensionality: Hubs are objects, that appear unexpectedly often among the nearest neighbors of others objects, while antihubs are never retrieved as neighbors. As a consequence, hubs may propagate their information (for example, class labels) too widely within the neighbor graph, while information from antihubs is depleted. These semantically distorted graphs can reduce learning performance in various tasks, such as classification, clustering, or visualization. Hubness is known to affect a variety of applied learning systems, or improper transport mode detection.

Currently, there is a lack of fully-featured, up-to-date, user-friendly software dealing with hubness. Available packages miss critical features and have not been updated in years, or are not particularly user-friendly. In this paper we describe scikit-hubness, which provides powerful, readily available, and easy-to-use hubness-related methods.

Feldbauer R, Rattei T, Flexer A
2020 - The Journal of Open Source Software, 5: 1957

Exploring Actinobacteria Associated With Rhizosphere and Endosphere of the Native Alpine Medicinal Plant Subspecies .

The rhizosphere of plants is enriched in nutrients facilitating growth of microorganisms, some of which are recruited as endophytes. Endophytes, especially Actinobacteria, are known to produce a plethora of bioactive compounds. We hypothesized that subsp. (Edelweiss), a rare alpine medicinal plant, may serve as yet untapped source for uncommon Actinobacteria associated with this plant. Rhizosphere soil of native Alpine plants was used, after physical and chemical pre-treatments, for isolating Actinobacteria. Isolates were selected based on morphology and identified by 16S rRNA gene-based barcoding. Resulting 77 Actinobacteria isolates represented the genera , , , , , , , and . In parallel, Edelweiss plants from the same location were surface-sterilized, separated into leaves, roots, rhizomes, and inflorescence and pooled within tissues before genomic DNA extraction. Metagenomic 16S rRNA gene amplicons confirmed large numbers of actinobacterial operational taxonomic units (OTUs) descending in diversity from roots to rhizomes, leaves and inflorescences. These metagenomic data, when queried with isolate sequences, revealed an overlap between the two datasets, suggesting recruitment of soil bacteria by the plant. Moreover, this study uncovered a profound diversity of uncultured Actinobacteria from Rubrobacteridae, Thermoleophilales, Acidimicrobiales and unclassified Actinobacteria specifically in belowground tissues, which may be exploited by a targeted isolation approach in the future.

Oberhofer M, Hess J, Leutgeb M, Gössnitzer F, Rattei T, Wawrosch C, Zotchev SB
2019 - Front Microbiol, 2531