| Title |
Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review
|
|---|---|
| Published in |
Journal of Inherited Metabolic Disease, May 2018
|
| DOI | 10.1007/s10545-018-0139-6 |
| Pubmed ID | |
| Authors |
Emma Graham, Jessica Lee, Magda Price, Maja Tarailo‐Graovac, Allison Matthews, Udo Engelke, Jeffrey Tang, Leo A. J. Kluijtmans, Ron A. Wevers, Wyeth W. Wasserman, Clara D. M. van Karnebeek, Sara Mostafavi |
| Abstract |
Many inborn errors of metabolism (IEMs) are amenable to treatment; therefore, early diagnosis and treatment is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, whole exome sequencing (WES) variant prioritization coupled with clinical and bioinformatics expertise is the primary method used to identify novel disease-causing variants; however, causation is often difficult to establish due to the number of plausible variants. Integrated analysis of untargeted metabolomics (UM) and WES or whole genome sequencing (WGS) data is a promising systematic approach for identifying disease-causing variants. In this review, we provide a literature-based overview of UM methods utilizing liquid chromatography mass spectrometry (LC-MS), and assess approaches to integrating WES/WGS and LC-MS UM data for the discovery and prioritization of variants causing IEMs. To embed this integrated -omics approach in the clinic, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 17% |
| Austria | 1 | 17% |
| Unknown | 4 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 17% |
| Science communicators (journalists, bloggers, editors) | 1 | 17% |
| Members of the public | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 122 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 27 | 22% |
| Researcher | 26 | 21% |
| Student > Master | 14 | 11% |
| Student > Bachelor | 7 | 6% |
| Student > Doctoral Student | 4 | 3% |
| Other | 17 | 14% |
| Unknown | 27 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 41 | 34% |
| Agricultural and Biological Sciences | 14 | 11% |
| Medicine and Dentistry | 13 | 11% |
| Chemistry | 9 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 3% |
| Other | 11 | 9% |
| Unknown | 30 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 March 2020.
All research outputs
#12,882,417
of 23,047,237 outputs
Outputs from Journal of Inherited Metabolic Disease
#1,189
of 1,870 outputs
Outputs of similar age
#153,931
of 326,328 outputs
Outputs of similar age from Journal of Inherited Metabolic Disease
#15
of 43 outputs
Altmetric has tracked 23,047,237 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,870 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 326,328 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.