ArticleMetabolites2020
Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics.
Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 31 citations in OpenAlex.
- Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis.Metabolites · 2026Review
- Elucidating therapeutic mechanisms of naringin and phloridzin on ulcerative colitis mice by metabolomics-based comparative analysis.Natural products and bioprospecting · 2026Article
- Next-generation sequencing in newborn screening: Current status, challenges, and future perspectives.Pediatric investigation · 2026Review
- Integrating the Genomic Revolution into Newborn Screening: Current Challenges and Future Perspectives.Pediatric reports · 2026Review
- Towards Next-Generation Sequencing as a First-Tier Diagnostic Test for Fructose-1,6-Bisphosphatase Deficiency.Metabolites · 2026Article
- Multimodal Metabolomic Analysis Reveals Novel Metabolic Disturbances in Adults With Early Treated Phenylketonuria.JIMD reports · 2025Article
- Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases.Orphanet journal of rare diseases · 2024Review
- Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.Computers in biology and medicine · 2024Article
- The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges.International journal of neonatal screening · 2024Review
- Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics.ArXiv · 2024Article
- Dried Blood Spots-A Platform for Therapeutic Drug Monitoring (TDM) and Drug/Disease Response Monitoring (DRM).European journal of drug metabolism and pharmacokinetics · 2023Review
- A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism.Translational pediatrics · 2022Review
- How to proceed after "negative" exome: A review on genetic diagnostics, limitations, challenges, and emerging new multiomics techniques.Journal of inherited metabolic disease · 2022Review
- Computational methods and translational applications for targeted next-generation sequencing platforms.Genes, chromosomes & cancer · 2022Review
- Review
- Research progress and perspective in metabolism and metabolomics of psoriasis.Chinese medical journal · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 1 institution in 1 country.
Funding
Abstract
Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two -omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: a method that uses untargeted metabolomics results of patient's dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.