dbRUSP

dbRUSP is a software tool and database developed for the analysis of newborn screening data, particularly focused on metabolic disorders included in the Recommended Uniform Screening Panel (RUSP). This panel consists of more than forty metabolic disorders recommended for universal newborn screening (NBS). The screening process involves using tandem-mass spectrometry to analyze metabolic analytes in dried blood spot samples. However, this method can result in the identification of both affected newborns and false positive results.
One of the key challenges in newborn screening is accounting for various continuous and categorical covariates that can influence blood metabolite levels. These covariates include factors such as gestational age, birth weight, age at blood collection, sex, parent-reported ethnicity, and parenteral nutrition status. These covariates can affect the accuracy of screening results.
dbRUSP is designed to address this challenge by providing a database and web-based tools for the analysis of metabolic data from a cohort of 500,539 screen-negative newborns reported by the California NBS program. The interactive database, built using the R shiny package, offers separate modules that allow users to study the influence of individual variables as well as the joint effects of multiple variables on metabolite levels. Users can input an individual's specific variables to obtain metabolite level reference ranges. Additionally, 'dbRUSP' can be utilized to select new candidate markers for the detection of metabolic conditions.
This open-source tool and database facilitate the development of data mining algorithms that consider the influence of covariates on metabolism. 'dbRUSP' aims to increase the accuracy of genetic disease screening, offering insights for researchers and healthcare professionals engaged in newborn screening.