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SG-ADVISER mtDNA: a web server for mitochondrial DNA annotation with data from 200 samples of a healthy aging cohort

Overview of attention for article published in BMC Bioinformatics, August 2017
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Title
SG-ADVISER mtDNA: a web server for mitochondrial DNA annotation with data from 200 samples of a healthy aging cohort
Published in
BMC Bioinformatics, August 2017
DOI 10.1186/s12859-017-1778-6
Pubmed ID
Authors

Manuel Rueda, Ali Torkamani

Abstract

Whole genome and exome sequencing usually include reads containing mitochondrial DNA (mtDNA). Yet, state-of-the-art pipelines and services for human nuclear genome variant calling and annotation do not handle mitochondrial genome data appropriately. As a consequence, any researcher desiring to add mtDNA variant analysis to their investigations is forced to explore the literature for mtDNA pipelines, evaluate them, and implement their own instance of the desired tool. This task is far from trivial, and can be prohibitive for non-bioinformaticians. We have developed SG-ADVISER mtDNA, a web server to facilitate the analysis and interpretation of mtDNA genomic data coming from next generation sequencing (NGS) experiments. The server was built in the context of our SG-ADVISER framework and on top of the MtoolBox platform (Calabrese et al., Bioinformatics 30(21):3115-3117, 2014), and includes most of its functionalities (i.e., assembly of mitochondrial genomes, heteroplasmic fractions, haplogroup assignment, functional and prioritization analysis of mitochondrial variants) as well as a back-end and a front-end interface. The server has been tested with unpublished data from 200 individuals of a healthy aging cohort (Erikson et al., Cell 165(4):1002-1011, 2016) and their data is made publicly available here along with a preliminary analysis of the variants. We observed that individuals over ~90 years old carried low levels of heteroplasmic variants in their genomes. SG-ADVISER mtDNA is a fast and functional tool that allows for variant calling and annotation of human mtDNA data coming from NGS experiments. The server was built with simplicity in mind, and builds on our own experience in interpreting mtDNA variants in the context of sudden death and rare diseases. Our objective is to provide an interface for non-bioinformaticians aiming to acquire (or contrast) mtDNA annotations via MToolBox. SG-ADVISER web server is freely available to all users at https://genomics.scripps.edu/mtdna .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 20%
Student > Bachelor 5 11%
Student > Doctoral Student 5 11%
Student > Postgraduate 4 9%
Student > Master 4 9%
Other 10 22%
Unknown 9 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 24%
Medicine and Dentistry 7 15%
Computer Science 4 9%
Agricultural and Biological Sciences 3 7%
Unspecified 3 7%
Other 8 17%
Unknown 10 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 September 2017.
All research outputs
#14,077,971
of 22,997,544 outputs
Outputs from BMC Bioinformatics
#4,494
of 7,312 outputs
Outputs of similar age
#170,592
of 318,830 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 87 outputs
Altmetric has tracked 22,997,544 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 318,830 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 87 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.