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fastMitoCalc: An ultra-fast program to estimate mitochondrial DNA copy number from whole-genome sequences

Academic Article
Publication Date:
2017
Short description:
fastMitoCalc: An ultra-fast program to estimate mitochondrial DNA copy number from whole-genome sequences / Qian, Y., Butler, T.J., Opsahl-Ong, K., Giroux, N.S., Sidore, C., Nagaraja, R., Cucca, F., Ferrucci, L., Abecasis, G.R., Schlessinger, D., Ding, J.. - In: BIOINFORMATICS. - ISSN 1367-4803. - 33:9(2017), pp. 1399-1401. [10.1093/bioinformatics/btw835]
abstract:
Mitochondrial DNA (mtDNA) copy number is tightly regulated in tissues, and is both a critical determinant of mitochondrial function and a potential biomarker for disease. We and other groups have shown that the mtDNA copy number per cell can be directly estimated from whole-genome sequencing. The computation is based on the rationale that sequencing coverage should be proportional to the underlying DNA copy number for autosomal and mitochondrial DNA, and most computing time is spent calculating the average autosomal DNA coverage across ∼3 billion bases. That makes analyzing tens of thousands of available samples very slow. Here we present fastMitoCalc, which takes advantage of the indexing of sequencing alignment files and uses a randomly selected small subset (0.1%) of the nuclear genome to estimate autosomal DNA coverage accurately. It is more than 100 times faster than current programs. fastMitoCalc also provides an option to estimate copy number using a single autosomal chromosome, which could also achieve high accuracy but is slower. Using fastMitoCalc, it becomes much more feasible now to conduct analyses on large-scale consortium data to test for association of mtDNA copy number with quantitative traits or nuclear variants.
Iris type:
1.1 Articolo in rivista
Keywords:
Genome, Human; Genomics; Humans; Sequence Analysis, DNA; Gene Dosage; Genome, Mitochondrial; Software; Statistics and Probability; Biochemistry; Molecular Biology; Computer Science Applications1707 Computer Vision and Pattern Recognition; Computational Theory and Mathematics; Computational Mathematics
List of contributors:
Qian, Yong; Butler, Thomas J.; Opsahl-Ong, Krista; Giroux, Nicholas S.; Sidore, Carlo; Nagaraja, Ramaiah; Cucca, Francesco; Ferrucci, Luigi; Abecasis, Gonçalo R.; Schlessinger, David; Ding, Jun
Authors of the University:
CUCCA Francesco
Handle:
https://iris.uniss.it/handle/11388/201803
Published in:
BIOINFORMATICS
Journal
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