Abstract
We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.
Highlights
The rapid growth of genomics data sets [1] and efforts to consolidate diverse data sets into common portals [2] have created an urgent need today for software frameworks that can be applied to these genomic “big data” to extract biological and medical insights from them [3]
Knowledge Engine for Genomics (KnowEnG) offers a vision of genomic computing that is complementary to the dominant paradigm where software packages are installed on the user’s computer and executed locally
The current paradigm is convenient as long as data sets predominantly reside locally, but with the on-going movement toward massive data sets in the public domain [71] and a clear need for moving tools to co-locate with these data, we expect the alternative paradigm embraced by KnowEnG to be increasingly relevant
Summary
The rapid growth of genomics data sets [1] and efforts to consolidate diverse data sets into common portals [2] have created an urgent need today for software frameworks that can be applied to these genomic “big data” to extract biological and medical insights from them [3]. We present “KnowEnG” (Knowledge Engine for Genomics, pronounced “knowing”), a cloud-based engine that provides a suite of powerful and easy-to-use machine learning tools for analysis of genomics data sets. These tools, referred to as “pipelines,” perform common bioinformatics analyses such as clustering of samples, gene prioritization, gene set characterization, and signature analysis. The pipelines help identify biologically meaningful patterns in the provided spreadsheet data, through ab initio analysis as well as by contextualizing with prior knowledge. The utility of KnowEnG is increased by co-localization of its tools with prior knowledge data sets from a large variety of sources
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