dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening
The Recommended Uniform Screening Panel (RUSP) contains more than forty metabolic disorders recommended for inclusion in universal newborn screening (NBS). Tandem-mass-spectrometry-based screening of metabolic analytes in dried blood spot samples identifies most affected newborns, along with a numbe...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | International Journal of Neonatal Screening |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2409-515X/8/3/48 |
| _version_ | 1797486975067881472 |
|---|---|
| author | Gang Peng Yunxuan Zhang Hongyu Zhao Curt Scharfe |
| author_facet | Gang Peng Yunxuan Zhang Hongyu Zhao Curt Scharfe |
| author_sort | Gang Peng |
| collection | DOAJ |
| description | The Recommended Uniform Screening Panel (RUSP) contains more than forty metabolic disorders recommended for inclusion in universal newborn screening (NBS). Tandem-mass-spectrometry-based screening of metabolic analytes in dried blood spot samples identifies most affected newborns, along with a number of false positive results. Due to their influence on blood metabolite levels, continuous and categorical covariates such as gestational age, birth weight, age at blood collection, sex, parent-reported ethnicity, and parenteral nutrition status have been shown to reduce the accuracy of screening. Here, we developed a database and web-based tools (dbRUSP) for the analysis of 41 NBS metabolites and six variables for a cohort of 500,539 screen-negative newborns reported by the California NBS program. The interactive database, built using the R shiny package, contains separate modules to study the influence of single variables and joint effects of multiple variables on metabolite levels. Users can input an individual’s variables to obtain metabolite level reference ranges and utilize dbRUSP to select new candidate markers for the detection of metabolic conditions. The open-source format facilitates the development of data mining algorithms that incorporate the influence of covariates on metabolism to increase accuracy in genetic disease screening. |
| first_indexed | 2024-03-09T23:41:00Z |
| format | Article |
| id | doaj.art-817474f9477a44a2a28ac7cbb36f7d0c |
| institution | Directory Open Access Journal |
| issn | 2409-515X |
| language | English |
| last_indexed | 2024-03-09T23:41:00Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Neonatal Screening |
| spelling | doaj.art-817474f9477a44a2a28ac7cbb36f7d0c2023-11-23T16:51:53ZengMDPI AGInternational Journal of Neonatal Screening2409-515X2022-08-01834810.3390/ijns8030048dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease ScreeningGang Peng0Yunxuan Zhang1Hongyu Zhao2Curt Scharfe3Department of Biostatistics, Yale University School of Public Health, New Haven, CT 06520, USADepartment of Biostatistics, Yale University School of Public Health, New Haven, CT 06520, USADepartment of Biostatistics, Yale University School of Public Health, New Haven, CT 06520, USADepartment of Genetics, Yale University School of Medicine, New Haven, CT 06520, USAThe Recommended Uniform Screening Panel (RUSP) contains more than forty metabolic disorders recommended for inclusion in universal newborn screening (NBS). Tandem-mass-spectrometry-based screening of metabolic analytes in dried blood spot samples identifies most affected newborns, along with a number of false positive results. Due to their influence on blood metabolite levels, continuous and categorical covariates such as gestational age, birth weight, age at blood collection, sex, parent-reported ethnicity, and parenteral nutrition status have been shown to reduce the accuracy of screening. Here, we developed a database and web-based tools (dbRUSP) for the analysis of 41 NBS metabolites and six variables for a cohort of 500,539 screen-negative newborns reported by the California NBS program. The interactive database, built using the R shiny package, contains separate modules to study the influence of single variables and joint effects of multiple variables on metabolite levels. Users can input an individual’s variables to obtain metabolite level reference ranges and utilize dbRUSP to select new candidate markers for the detection of metabolic conditions. The open-source format facilitates the development of data mining algorithms that incorporate the influence of covariates on metabolism to increase accuracy in genetic disease screening.https://www.mdpi.com/2409-515X/8/3/48newborn screeninginborn metabolic disorderstandem mass spectrometryfalse positive screensecond-tier testing |
| spellingShingle | Gang Peng Yunxuan Zhang Hongyu Zhao Curt Scharfe dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening International Journal of Neonatal Screening newborn screening inborn metabolic disorders tandem mass spectrometry false positive screen second-tier testing |
| title | dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening |
| title_full | dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening |
| title_fullStr | dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening |
| title_full_unstemmed | dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening |
| title_short | dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening |
| title_sort | dbrusp an interactive database to investigate inborn metabolic differences for improved genetic disease screening |
| topic | newborn screening inborn metabolic disorders tandem mass spectrometry false positive screen second-tier testing |
| url | https://www.mdpi.com/2409-515X/8/3/48 |
| work_keys_str_mv | AT gangpeng dbruspaninteractivedatabasetoinvestigateinbornmetabolicdifferencesforimprovedgeneticdiseasescreening AT yunxuanzhang dbruspaninteractivedatabasetoinvestigateinbornmetabolicdifferencesforimprovedgeneticdiseasescreening AT hongyuzhao dbruspaninteractivedatabasetoinvestigateinbornmetabolicdifferencesforimprovedgeneticdiseasescreening AT curtscharfe dbruspaninteractivedatabasetoinvestigateinbornmetabolicdifferencesforimprovedgeneticdiseasescreening |