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. Availability and Implementation fastMitoCalc is available at https://lgsun.irp.nia.nih.gov/hsgu/software/mitoAnalyzer/index.html Supplementary information Supplementary data are available at Bioinformatics online.

A key parameter in metabolic and pathologic studies is the estimation of body tissue distribution. This is a laborious and operator-dependent process. In this work we introduce an unsupervised muscle and fat quantification algorithm based on water only, fat only and water-and-fat MRI images of the mid-thigh area. We first use parametric deformable models to segment the subcutaneous fat and then apply centroid clustering in the feature domain defined by the voxel intensities in water only and fat only images to detect the inter-muscular fat, muscle and bone. This tissue decomposition permits the computation of volumetric and area measures of fat and muscle. We tested the proposed method on 9 participants and validated these measures against values obtained from a semi-manual clinician-driven analysis of single-slice mid-thigh CT images of the same participants. Our approach was found to be statistically consistent with the semi-manual reference method, and was able to address inter-participant anatomic variability and intensity in homogeneity effects.