| Title |
Mitochondrial genome sequence analysis: A custom bioinformatics pipeline substantially improves Affymetrix MitoChip v2.0 call rate and accuracy
|
|---|---|
| Published in |
BMC Bioinformatics, October 2011
|
| DOI | 10.1186/1471-2105-12-402 |
| Pubmed ID | |
| Authors |
Hongbo M Xie, Juan C Perin, Theodore G Schurr, Matthew C Dulik, Sergey I Zhadanov, Joseph A Baur, Michael P King, Emily Place, Colleen Clarke, Michael Grauer, Jonathan Schug, Avni Santani, Anthony Albano, Cecilia Kim, Vincent Procaccio, Hakon Hakonarson, Xiaowu Gai, Marni J Falk |
| Abstract |
Mitochondrial genome sequence analysis is critical to the diagnostic evaluation of mitochondrial disease. Existing methodologies differ widely in throughput, complexity, cost efficiency, and sensitivity of heteroplasmy detection. Affymetrix MitoChip v2.0, which uses a sequencing-by-genotyping technology, allows potentially accurate and high-throughput sequencing of the entire human mitochondrial genome to be completed in a cost-effective fashion. However, the relatively low call rate achieved using existing software tools has limited the wide adoption of this platform for either clinical or research applications. Here, we report the design and development of a custom bioinformatics software pipeline that achieves a much improved call rate and accuracy for the Affymetrix MitoChip v2.0 platform. We used this custom pipeline to analyze MitoChip v2.0 data from 24 DNA samples representing a broad range of tissue types (18 whole blood, 3 skeletal muscle, 3 cell lines), mutations (a 5.8 kilobase pair deletion and 6 known heteroplasmic mutations), and haplogroup origins. All results were compared to those obtained by at least one other mitochondrial DNA sequence analysis method, including Sanger sequencing, denaturing HPLC-based heteroduplex analysis, and/or the Illumina Genome Analyzer II next generation sequencing platform. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 8% |
| Unknown | 55 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 18% |
| Other | 6 | 10% |
| Student > Ph. D. Student | 6 | 10% |
| Student > Bachelor | 6 | 10% |
| Professor > Associate Professor | 6 | 10% |
| Other | 15 | 25% |
| Unknown | 10 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 38% |
| Biochemistry, Genetics and Molecular Biology | 11 | 18% |
| Medicine and Dentistry | 4 | 7% |
| Engineering | 3 | 5% |
| Neuroscience | 3 | 5% |
| Other | 6 | 10% |
| Unknown | 10 | 17% |
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 25 October 2011.
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#15,237,301
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Outputs from BMC Bioinformatics
#5,353
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#95,279
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Outputs of similar age from BMC Bioinformatics
#74
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