GeCoAgent: A Conversational Agent for Empowering Genomic Data Extraction and Analysis

With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools. This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.

Guideline for feedback of individual genetic research findings for genomics research in Africa

As human genomics research in Africa continues to generate large amounts of data, ethical issues arise regarding how actionable genetic information is shared with research participants. The Human Heredity and Health in Africa Consortium (H3Africa) Ethics and Community Engagement Working group acknowledged the need for such guidance, identified key issues and principles relevant to genomics research in Africa and developed a practical guideline for consideration of feeding back individual genetic results of health importance in African research projects. This included a decision flowchart, providing a logical framework to assist in decision-making and planning for human genomics research projects. Although presented in the context of the H3Africa Consortium, we believe the principles described, and the decision flowchart presented here is applicable more broadly in African genomics research.

Artificial intelligence in human genomics and biomedicine

The increasing availability of extensive and complex data has made human genomics and its applications in (bio)medicine an at­ tractive domain for artificial intelligence (AI) in the form of advanced machine learning (ML) methods. These methods are linked not only to the hope of improving diagnosis and drug development. Rather, they may also advance key issues in biomedicine, e. g. understanding how individual differences in the human genome may cause specific traits or diseases. We analyze the increasing convergence of AI and genom­ics, the emergence of a corresponding innovation system, and how these associative AI methods relate to the need for causal knowledge in biomedical research and development (R&D) and in medical prac­tice. Finally, we look at the opportunities and challenges for clinical practice and the implications for governance issues arising from this convergence.

Sampling, Logistics, and Analytics of Urine for RT-qPCR-based Diagnostics

Body fluids in the context of cancer diagnosis are the primary source of liquid biopsy, i.e., biomarker detection that includes blood and serum, urine, and saliva. RNA represents a particular class of biomarkers because it is thought to monitor the current status of gene expression in humans, in organs, and if present, also in tumors. In case of bladder cancer, we developed a scheme that describes, in detail, all steps from the collection of urine samples from patients, stabilization of samples, their transportation, storage, and marker analysis by qPCR-based technology. We find that urine samples prepared according to this protocol show stability of RNA over more than 10 days at unchilled temperatures during shipping. A specific procedure of primer design and amplicon evaluation allows a specific assignment of PCR products to human genomics and transcriptomics data collections. In summary, we describe a technical option for the robust acquisition of urine samples and the quantitative detection of RNA-based tumor markers in case of bladder cancer patients. This protocol is for general use, and we describe that it works for any RNA-based tumor marker in urine of cancer patients.

A dimensional perspective on the genetics of obsessive-compulsive disorder

This review covers recent findings in the genomics of obsessive-compulsive disorder (OCD), obsessive-compulsive symptoms, and related traits from a dimensional perspective. We focus on discoveries stemming from technical and methodological advances of the past five years and present a synthesis of human genomics research on OCD. On balance, reviewed studies demonstrate that OCD is a dimensional trait with a highly polygenic architecture and genetic correlations to multiple, often comorbid psychiatric phenotypes. We discuss the phenotypic and genetic findings of these studies in the context of the dimensional framework, relying on a continuous phenotype definition, and contrast these observations with discoveries based on a categorical diagnostic framework, relying on a dichotomous case/control definition. Finally, we highlight gaps in knowledge and new directions for OCD genetics research.