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Graph Representation Learning methods opened new possibilities for addressing complex,real-world problems represented by graphs. However, many graphs used in these applicationscomprise millions of nodes and billions of edges and are beyond the capabilities of current methodsand software implementations. We present GRAPE, a software resource for graph processing andrepresentation learning that is able to scale with big graphs by using specialized and smart datastructures, algorithms, and a fast parallel implementation. When compared with state of the artsoftware resources, GRAPE shows an improvement of orders of magnitude in empirical space andtime complexity, as well as a substantial and statistically significant improvement in edge predictionand node label prediction performance. Furthermore, GRAPE provides over 80,000 graphs fromthe literature and other sources, standardized interfaces allowing a straightforward integration ofthird-party libraries, 61 node embedding methods, 25 inference models, and 3 modular pipelinesto allow a FAIR and reproducible comparison of methods and libraries for graph processing andembedding.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven’t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

D. vulgaris Hildenborough has 72 response regulators. The Desulfovibrio are sulfate reducing bacteria that are important in the sulfur and carbon cycles in anoxic habitats. Its large number of two componenent systems are probably critical to its ability to sense and respond to its environment. Our goal is to map these RRs to the genes they regulate using a DNA-affinity-purification-chip (DAP-chip) protocol. First target determined usuing EMSA. A positive target was determined for as many RRs as possible using EMSA. Targets were selected based on gene proximity, regulon predictions and/or predicted sigma54 dependent promoters. qPCR was used to ensure that the target was enriched from sheared genomic DNA before proceeding to the DAP-chip.

Sulfate reducing bacteria (SRB) play an important role in global sulfur and carbon cycling through their ability to completely mineralize organic matter while respiring sulfate to hydrogen sulfide. They are ubiquitous in anaerobic environments and have the ability to reduce toxic metals like Cr(VI) and U(VI). While SRB have been studied for over three decades, bioenergetic modes of this group of microbes are poorly understood. Desulfovibrio vulgaris strain Hildenborough (DvH) has served as a model SRB over the last decade with the accumulation of transcriptomic, proteomic and metabolic data under a wide variety of stressors. To further investigate the three hypothesized modes of energy generation in this anaerobe we conducted a systematic study involving multiple electron donor and acceptor combinations for growth. DvH was grown at 37oC in a defined medium with (a) lactate + thiosulfate, (b) lactate + sulfite (c) lactate + sulfate, (d) pyruvate + sulfate, (e) H2 + acetate + sulfate, (f) formate + acetate + sulfate, g) formate + sulfate and (h) pyruvate fermentation. Cells were harvested at mid-log phase of growth for all conditions for transcriptomics, when the optical density at 600nm was in the range 0.42-0.5. Initial results indicate that cells grown on lactate do not appear to significantly differentiate their gene expression profiles when presented with different electron acceptors. These profiles however differ significantly from those observed during growth with other electron donors such as H2 and formate, as well as during fermentative growth. Together the gene expression changes in the presence of different electron donors provide insights into the ability of DvH to differentially reduce metals such as Cr(VI). Here we present revised modes of energy generation in DvH in light of this new transcriptomic evidence.