BioTuring’s BBrowser is a software solution that helps scientists effectively analyze single-cell omics data. It combines big data with big computation and modern data visualization to create a unique platform where scientists can interact and obtain important biological insights from the massive amounts of single-cell data. BBrowser has three main components: a curated single-cell database, a big-data analytics layer, and a data visualization module. BBrowser is available for download at: https://bioturing.com/bbrowser/download.

The incidence of Alzheimer’s Disease in females is almost double that of males. To search for sex-specific gene associations, we build a machine learning approach focused on functionally impactful coding variants. This method can detect differences between sequenced cases and controls in small cohorts. In the Alzheimer’s Disease Sequencing Project with mixed sexes, this approach identified genes enriched for immune response pathways. After sex-separation, genes become specifically enriched for stress-response pathways in male and cell-cycle pathways in female. These genes improve disease risk prediction in silico and modulate Drosophila neurodegeneration in vivo. Thus, a general approach for machine learning on functionally impactful variants can uncover sex-specific candidates towards diagnostic biomarkers and therapeutic targets.

No abstract

Motivation In light of the massive growth of the scientific literature, text mining is increasingly used to extract biological pathways. Though multiple tools explore individual connections between genes, diseases and drugs, few extensively synthesize pathways for specific diseases and drugs. Results Through community detection of a literature network, we extracted 3444 functional gene groups that represented biological pathways for specific diseases and drugs. The network linked Medical Subject Headings (MeSH) terms of genes, diseases and drugs that co-occurred in publications. The resulting communities detected highly associated genes, diseases and drugs. These significantly matched current knowledge of biological pathways and predicted future ones in time-stamped experiments. Likewise, disease- and drug-specific communities also recapitulated known pathways for those given diseases and drugs. Moreover, diseases sharing communities had high comorbidity with each other and drugs sharing communities had many common side effects, consistent with related mechanisms. Indeed, the communities robustly recovered mutual targets for drugs [area under Receiver Operating Characteristic curve (AUROC)=0.75] and shared pathogenic genes for diseases (AUROC=0.82). These data show that literature communities inform not only just known biological processes but also suggest novel disease- and drug-specific mechanisms that may guide disease gene discovery and drug repurposing. Availability and implementation Application tools are available at http://meteor.lichtargelab.org. Supplementary information Supplementary data are available at Bioinformatics online.

Developments in large scale computing environments have led to design of workflows that rely on containers and analytics platform that are well supported by the commercial cloud. The National Science Foundation also envisions a future in science and engineering that includes commercial cloud service providers (CSPs) such as Amazon Web Services, Azure and Google Cloud. These twin forces have made researchers consider the commercial cloud as an alternative option to current high performance computing (HPC) environments. Training and knowledge on how to migrate workflows, cost control, data management, and system administration remain some of the commonly listed concerns with adoption of cloud computing. In an effort to ameliorate this situation, CSPs have developed online and in-person training platforms to help address this problem. Scalability, ability to impart knowledge, evaluating knowledge gain, and accreditation are the core concepts that have driven this approach. Here, we present a review of our experience using Google's Qwiklabs online platform for remote and in-person training from the perspective of a HPC user. For this study, we completed over 50 online courses, earned five badges and attended a one-day session. We identify the strengths of the approach, identify avenues to refine them, and consider means to further community engagement. We further evaluate the readiness of these resources for a cloud-curious researcher who is familiar with HPC. Finally, we present recommendations on how the large scale computing community can leverage these opportunities to work with CSPs to assist researchers nationally and at their home institutions.