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In this review, we examine recent efforts to adapt informatics strategies to cardiovascular biomedical research automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of Cardiovascular Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently-formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of cardiovascular (CV) diseases and unification of cardiovascular knowledge.<br /><br /> The growing production of massive heterogeneous biological data offers opportunities for new discoveries. However, performing multi-omics data analysis is challenging, and researchers are forced to handle the ever-increasing complexity of both data management and evolution of our biological understanding. Substantial efforts have been made to unify biological datasets into integrated systems. Unfortunately, they are not easily scalable, deployable and searchable, locally or globally.<br /><br /> This publication presents two tools with a simple structure that can help any data provider, organization or researcher, requiring a reliable data search and analysis base. The first tool is Kibio, a scalable and adaptable data storage based on Elasticsearch search engine. The second tool is KibioR, a R package to pull, push and search Kibio datasets or any accessible Elasticsearch-based databases. These tools apply a uniform data exchange model and minimize the burden of data management by organizing data into a decentralized, versatile, searchable and shareable structure. Several case studies are presented using multiple databases, from drug characterization to miRNAs and pathways identification, emphasizing the ease of use and versatility of the Kibio/KibioR framework.<br /><br /> Both KibioR and Elasticsearch are open source. KibioR package source is available at https//github.com/regisoc/kibior and the library on CRAN at https//cran.r-project.org/package=kibior.<br /><br /> Supplementary data are available at Bioinformatics online.<br /><br />Supplementary data are available at Bioinformatics online.Atrial fibrillation (AF) is a common cardiac arrhythmia leading to many adverse outcomes and increased mortality. Yet the molecular mechanisms underlying AF remain largely unknown. Recent advances in high-throughput technologies make large-scale molecular profiling possible. [https://www.selleckchem.com/products/irak-1-4-inhibitor-i.html selleck kinase inhibitor] In the past decade, multiomics studies of AF have identified a number of potential biomarkers of AF. In this review, we focus on the studies of multiomics profiles with AF risk. We summarize recent advances in the discovery of novel biomarkers for AF through multiomics studies. We also discuss limitations and future directions in risk assessment and discovery of therapeutic targets for AF.Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals IRF8 (P = 1.0 × 10-6), EOMES (P = 6.0 × 10-6) and BTNL2 (P = 2.1 × 10-3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.Studies of anthropometric measures and prostate cancer risk conducted primarily in White men have reported positive associations with advanced disease. We assessed body size in relation to incident prostate cancer risk in 79,950 men from the Multiethnic Cohort, with 8,819 cases identified over a 22-year period (1993-2015). Height was associated with increased risk of advanced prostate cancer (hazard ratio=1.24, 95% CI 1.04, 1.48; ≥68 inches versus less then 66 inches) and high-grade disease (hazard ratio=1.15, 95% CI 1.02, 1.31). Compared to men of normal weight, men overweight at baseline were at higher risk of high-grade cancer (hazard ratio=1.15, 95% CI 1.04, 1.26). Greater weight was positively associated with localized and low-grade disease in African Americans and Native Hawaiians (Pheterogeneity by race 0.0002 and 0.008 respectively). Weight change since age 21 was positively associated with high-grade disease (hazard ratio=1.20, 95% CI 1.05, 1.37; for ≥40 lb vs 10 lb; Ptrend=0.005). Comparing highest versus lowest quartile, waist-to-hip ratio was associated with a 1.78-fold increase (95% CI 1.28, 2.46) in the risk of advanced prostate cancer. Positive associations with the majority of anthropometric measures were observed in all five racial/ethnic groups, suggesting a general impact of anthropometric measures on risk across populations.