Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

"Jumping Jack": Genomic Microsatellites Underscore the Distinctiveness of Closely Related Pseudoperonospora cubensis and Pseudoperonospora humuli and Provide New Insights Into Their Evolutionary Past

Downy mildews caused by obligate biotrophic oomycetes result in severe crop losses worldwide. Among these pathogens, Pseudoperonospora cubensis and P. humuli, two closely related oomycetes, adversely affect cucurbits and hop, respectively. Discordant hypotheses concerning their taxonomic relationships have been proposed based on host-pathogen interactions and specificity evidence and gene sequences of a few individuals, but population genetics evidence supporting these scenarios is missing. Furthermore, nuclear and mitochondrial regions of both pathogens have been analyzed using microsatellites and phylogenetically informative molecular markers, but extensive comparative population genetics research has not been done. Here, we genotyped 138 current and historical herbarium specimens of those two taxa using microsatellites (SSRs). Our goals were to assess genetic diversity and spatial distribution, to infer the evolutionary history of P. cubensis and P. humuli, and to visualize genome-scale organizational relationship between both pathogens. High genetic diversity, modest gene flow, and presence of population structure, particularly in P. cubensis, were observed. When tested for cross-amplification, 20 out of 27 P. cubensis-derived gSSRs cross-amplified DNA of P. humuli individuals, but few amplified DNA of downy mildew pathogens from related genera. Collectively, our analyses provided a definite argument for the hypothesis that both pathogens are distinct species, and suggested further speciation in the P. cubensis complex

Removing Systemic Barriers to Equity, Diversity, and Inclusion: Report of the 2019 Plant Science Research Network Workshop “Inclusivity in the Plant Sciences”

A future in which scientific discoveries are valued and trusted by the general public cannot be achieved without greater inclusion and participation of diverse communities. To envision a path towards this future, in January 2019 a diverse group of researchers, educators, students, and administrators gathered to hear and share personal perspectives on equity, diversity, and inclusion (EDI) in the plant sciences. From these broad perspectives, the group developed strategies and identified tactics to facilitate and support EDI within and beyond the plant science community. The workshop leveraged scenario planning and the richness of its participants to develop recommendations aimed at promoting systemic change at the institutional level through the actions of scientific societies, universities, and individuals and through new funding models to support research and training. While these initiatives were formulated specifically for the plant science community, they can also serve as a model to advance EDI in other disciplines. The proposed actions are thematically broad, integrating into discovery, applied and translational science, requiring and embracing multidisciplinarity, and giving voice to previously unheard perspectives. We offer a vision of barrier-free access to participation in science, and a plant science community that reflects the diversity of our rapidly changing nation, and supports and invests in the training and well-being of all its members. The relevance and robustness of our recommendations has been tested by dramatic and global events since the workshop. The time to act upon them is now