A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes.

Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an essential role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus' genotype and its phenotype.

Protein (multi-)location prediction: utilizing interdependencies via a generative model.

Motivation: Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein's function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein.

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.

Motivation: Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location.

Determining the subcellular location of new proteins from microscope images using local features.

Motivation: Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified.

Results: Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class.