News

  • Improving Min Hash for Metagenomic Classification
    publication
    Tuesday, July 4, 2017
    This work improves upon the so called "min hash" technique (a "probabilistic data analysis" method) to develop a very fast and efficient way to estimate the similarity of two sets of objects (in terms of how much they overlap). The approach we present is orders of magnitude faster (and uses orders of magnitude less space) when two data sets under consideration are of very different size. The kinds of sets we consider are sets of sub-strings (called k-mers) of DNA sequences from communities of microorganisms.
  • IndeCut evaluates performance of network motif discovery algorithms
    publication
    Sunday, July 2, 2017
    A gene regulatory network is basically a representation of how genes interact with each other. In this work, we develop the only (to date) method to assess the accuracy of so called "motif discovery algorithms" that seek to find important sub-networks of a given gene regulatory network. We develop a provably correct mathematical approach (based on a variety of metrics that say how close two matrices are to each other) and use this to assess the performance of a variety of motif discovery algorithms.
  • OMBI Metagenomics conference
    Event
    Friday, May 12, 2017

    The Oregon State University Microbiome Initiative (OMBI) is a microbiome research and education program that centers on addressing pertinent problems in metagenomics.

  • Critical Assessment of Metagenome Interpretation − a benchmark of computational metagenomics software
    publication
    Sunday, January 1, 2017
    In a very reproducible fashion, we assess a wide variety of computational techniques in metagenomics, including assembly (putting together pieces of genomes, called contigs, from short reads), binning (figuring out where the contigs came from), and taxonomic profiling (determining which organisms are present in a sample and at what relative amount).
  • EMDUnifrac
    publication
    Sunday, January 1, 2017
    Rapidly answers “why are these data sets different” by leveraging hierarchical/relatedness information. In short, we develop an algorithm to quickly compute the Unifrac distance by leveraging the earth mover's distance, prove its correctness, and derive time and space complexity characterizations.