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Nurse scientists are generating, acquiring, distributing, processing, storing, and analyzing greater volumes of complex omics data than ever before. To take full advantage of big omics data, to address core biological questions, and to enhance patient care, however, genomic nurse scientists must embrace data science. Intended for readership with limited but expanding data science knowledge and skills, this article aims to provide a brief overview of the state of data science in genomic nursing. Our goal is to introduce key data science concepts to genomic nurses who participate at any stage of the data science lifecycle, from research patient recruitment to data wrangling, preprocessing, and analysis to implementation in clinical practice to policy creation. We address three major components in this review: (1) fundamental terminology for the field of genomic nursing data science, (2) current genomic nursing data science research exemplars, and (3) the spectrum of genomic nursing data science roles as well as education pathways and training opportunities. Links to helpful resources are included throughout the article.

Background: Maternal obesity is a well-known risk factor for significant obstetric and neonatal complications. The influence of the gastrointestinal microbiome in the setting of maternal obesity during pregnancy is less understood. The purpose of this systematic review is to synthesize the literature on the relationships between maternal obesity and excessive gestational weight gain (EGWG) and the composition of maternal and child gastrointestinal microbiomes. Method: We searched CINHAL, OVID Medline, Web of Science, and PubMed for relevant literature using medical subject heading terms related to obesity, pregnancy, and the gastrointestinal microbiome. We assessed 249 articles for potential inclusion using the preferred reporting items for systematic review and meta-analyses framework and deemed 11 articles as relevant for this review. Results: Maternal obesity was associated with significant microbial changes in both maternal and infant fecal microbiome biospecimens including increases in Bacteroidetes, Firmicutes, and the Actinobacteria phyla and decreases in Bifidobacteria. However, inconsistencies in uniform taxonomic results across all studies mean that evidence of specific microbial associations with obesity and EGWG is inconclusive. Conclusion: Our findings suggest that both maternal and child gastrointestinal microbiome composition is altered in the setting of maternal obesity and EGWG during pregnancy. Future microbiome studies should concentrate on the investigation of metagenomic sequencing to elucidate microbial function rather than solely taxonomic composition. More diverse populations of mothers should be sampled to address health disparities and adverse outcomes of underrepresented populations. Finally, analytic pipelines should be standardized across studies to aid in reproducibility.

Novel discoveries in genomics and other omics sciences are rapidly redefining our understanding of health and disease as well as advancing the development of targeted therapeutics for improving health outcomes. The scale of these findings, fueled by vast increases in computing power and new techniques in data analytics, easily supersedes that of phenomena observed using more traditional research approaches. Until recently, the classification and diagnosis of disease has involved rather subjective processes, whereby signs and late symptom patterns are linked with clinical outcomes. However, symptom patterns, disease trajectories, and health outcomes are complex entities characterized by a wide range of clinical manifestations and progression patterns. The burgeoning fields of data science and bioinformatics are opening opportunities for nurse scientists to quantify disease heterogeneity by defining and categorizing disease phenotypes and endotypes. Nurse scientists and clinicians can play a critical role in engaging patients and the larger scientific community in these efforts. The purpose of this article is to provide an introduction to concepts critical to understanding and quantifying heterogeneity in the onset and progression of chronic disease. To present and exemplify key concepts, we (1) discuss evidence for heterogeneity in the onset and progression of Type 1 diabetes, (2) link emerging research approaches in data science with principles in network science and systems biology to lay the groundwork for stratifying subclinical and advanced chronic disease, thus expanding the purview of symptom science, and (3) describe the computational skills needed to engage in these analyses.