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Publications in peer reviewed journals

2 Publications found
  • scikit-hubness: Hubness Reduction and Approximate Neighbor Search

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


    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.

  • The Signal and the Noise: Characteristics of Antisense RNA in Complex Microbial Communities

    Michaelsen TY, Brandt J, Singleton CM, Kirkegaard RH, Wiesinger J, Segata N, Albertsen M
    2020 - mSystems, 5: e00587-19


    High-throughput sequencing has allowed unprecedented insight into the composition and function of complex microbial communities. With metatran- scriptomics, it is possible to interrogate the transcriptomes of multiple organisms si- multaneously to get an overview of the gene expression of the entire community. Studies have successfully used metatranscriptomics to identify and describe rela- tionships between gene expression levels and community characteristics. How- ever, metatranscriptomic data sets contain a rich suite of additional information that is just beginning to be explored. Here, we focus on antisense expression in meta- transcriptomics, discuss the different computational strategies for handling it, and highlight the strengths but also potentially detrimental effects on downstream anal- ysis and interpretation. We also analyzed the antisense transcriptomes of multiple genomes and metagenome-assembled genomes (MAGs) from five different data sets and found high variability in the levels of antisense transcription for individual spe- cies, which were consistent across samples. Importantly, we challenged the concep- tual framework that antisense transcription is primarily the product of transcriptional noise and found mixed support, suggesting that the total observed antisense RNA in complex communities arises from the combined effect of unknown biological and technical factors. Antisense transcription can be highly informative, including techni- cal details about data quality and novel insight into the biology of complex micro- bial communities.

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