III: Small: Learning Multi-scale Sequence Features for Predicting Gene to Microbiome Function

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Microbial communities play vital roles in health and the environment. In human health, they are referred to as our microbiomes; for example, healthy gut microbiomes can help digest and efficiently convert food to nutrients to be taken up in our gut. However, what constitutes “unhealthy” (dysbiotic) microbiomes and how they can affect or be affected by the body (or environment) is unknown. If we can understand microbes’ interactions with each other and the body, then we can design better treatments, therapies, and medicines (e.g. pre- and pro-biotics) to manipulate microbiomes. To understand the ``rules'' for microbial ecosystems, we must first solve the genotype-to-phenotype problem, i.e. identify microbial genetic changes which correlate to changes in microbiome functioning/traits. Most researchers have simply focused on predicting environmental or disease phenotypes by solely using microbiome community structure (ie: a population census of species in a community) and do not consider detailed DNA/RNA differences. It is not surprising that most studies have yielded modest prediction accuracy and little understanding of how microbiomes function. Attributing which “configurations” of organisms and/or genes contribute to a particular “microbiome state” can help us predict disease, understand how the environment may change microbial ecosystems, and be able to predict future changes of these systems (e.g. perturbations due to a chemical, temperature, etc.). Methods that can learn pertinent features at multiple scales (genome-, organism-, and community-level) simultaneously, are needed to interpret both the “species census” and microbial genetic changes (mutations that may lead to speciation and/or functional evolution) that influence community structure. Our educational activities will bring cutting edge research and topics to undergraduate and graduate education in Bioinformatics-related courses, which are part of Machine Learning and Bioinformatics Masters programs and a Bachelor’s bioinformatics minor at Drexel University. In addition, we plan to organize a Drexel College of Engineering-wide high school extracurricular program for mentoring of science projects for underserved public schools. A unified algorithm is needed to learn microbiome features on multiple levels to be able to predict microbiome functioning, thereby identifying biological processes (a.k.a harnessing data to understand the rules of life, NSF Big10 goals) that result in important “states” (e.g. disease or healthy). Doing so will transform our understanding of how large- and small-scale changes influence microbiome phenotypes. Current approaches are highly limited. Phenotype prediction based on 16S rRNA surveys is usually conducted solely on microbial operational taxonomic units (OTUs), which rarely capture the mutations that signify overall phenotypic changes. Phenotype prediction using metagenomes may perform better than 16S surveys, but many downstream analyses (feature selection, statistical tests) are needed to interpret (e.g. infer subcommunities relevant to phenotype) this classification. Therefore, we propose to develop a recurrent neural network (RNN) that can learn both community-level changes in the microbiome and genetic changes that relate to microbiome phenotypes. While most neural networks can ``learn'' features, it is usually difficult to get this information back out of the network (i.e.: interpretation). We will also use the recent advances in attention-based RNNs that will help us interpret which multi-scale features are most important to phenotype prediction. We will make our algorithms and software available to the microbiome community, whose potential applications include improving agriculture, environmental monitoring, personalized medicine, among others.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.