| Title |
A multi-parametric workflow for the prioritization of mitochondrial DNA variants of clinical interest
|
|---|---|
| Published in |
Human Genetics, November 2015
|
| DOI | 10.1007/s00439-015-1615-9 |
| Pubmed ID | |
| Authors |
Mariangela Santorsola, Claudia Calabrese, Giulia Girolimetti, Maria Angela Diroma, Giuseppe Gasparre, Marcella Attimonelli |
| Abstract |
Assigning a pathogenic role to mitochondrial DNA (mtDNA) variants and unveiling the potential involvement of the mitochondrial genome in diseases are challenging tasks in human medicine. Assuming that rare variants are more likely to be damaging, we designed a phylogeny-based prioritization workflow to obtain a reliable pool of candidate variants for further investigations. The prioritization workflow relies on an exhaustive functional annotation through the mtDNA extraction pipeline MToolBox and includes Macro Haplogroup Consensus Sequences to filter out fixed evolutionary variants and report rare or private variants, the nucleotide variability as reported in HmtDB and the disease score based on several predictors of pathogenicity for non-synonymous variants. Cutoffs for both the disease score as well as for the nucleotide variability index were established with the aim to discriminate sequence variants contributing to defective phenotypes. The workflow was validated on mitochondrial sequences from Leber's Hereditary Optic Neuropathy affected individuals, successfully identifying 23 variants including the majority of the known causative ones. The application of the prioritization workflow to cancer datasets allowed to trim down the number of candidate for subsequent functional analyses, unveiling among these a high percentage of somatic variants. Prioritization criteria were implemented in both standalone ( http://sourceforge.net/projects/mtoolbox/ ) and web version ( https://mseqdr.org/mtoolbox.php ) of MToolBox. |
X Demographics
The data shown below were collected from the profiles of 2 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 Kingdom | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 2% |
| Unknown | 54 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 27% |
| Researcher | 12 | 22% |
| Student > Master | 7 | 13% |
| Student > Doctoral Student | 5 | 9% |
| Other | 5 | 9% |
| Other | 10 | 18% |
| Unknown | 1 | 2% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 14 | 25% |
| Agricultural and Biological Sciences | 10 | 18% |
| Medicine and Dentistry | 8 | 15% |
| Neuroscience | 7 | 13% |
| Unspecified | 4 | 7% |
| Other | 9 | 16% |
| Unknown | 3 | 5% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 03 December 2015.
All research outputs
#15,329,366
of 23,577,761 outputs
Outputs from Human Genetics
#2,525
of 2,993 outputs
Outputs of similar age
#219,534
of 391,014 outputs
Outputs of similar age from Human Genetics
#10
of 22 outputs
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