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Next generation sequencing reveals no change in mtDNA deletions upon the loss of mitochondrial fusion in mouse hearts

ABSTRACT: Aim: To determine whether mtDNA deletions arise upon the loss of mitochondria fusion in heart tissue Method: DNA was isolated heart mitochondria from control and heart-specific double mitofusin1-2 (dMfn) KO animals and used to generate libraries for sequencing to detect mtDNA deletions. Total genomic DNA from the Deletor mouse was provided by Anu Suomalainen-Wartiovaara and used as a positive control for the detection of mtDNA rearrangements. A standard Illumina TrueSeq paired-end library was prepared with ~500 base pair fragment inserts. Paired-end 100 base pair sequencing was conducted using an Illumina HiSeq 2500. The reads were mapped to the genomic sequence without the mitochondria using bowtie (VN: 2.1.0) to remove nuclear-genomic sequences. The unmapped reads were then mapped to the mitochondrial sequence (GenBank JF286601.1) using bwa (n=0.04, VN: 0.6.2-r126) with unmapped reads undergoing an additional mapping round after trimming fastx_trimmer, VN: 0.0.13.2) to ensure higher mapping results. Using samtools (VN: 1.0: samtools –f1 –F14) reads, where the two paired sequences were observed to be greater than 600 base pair apart were identified as those containing deletion breakpoints. These reads were extracted for additional analysis. Results: Control and dMfn KO samples showed similar frequencys of breakpoints, while the positive control (Deletor mouse) showed a high frequecny of large breakpoints.

ORGANISM(S): Mus musculus

PROVIDER: GSE124420 | GEO | 2018/12/28

REPOSITORIES: GEO

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Project description:Next generation sequencing platforms have become essential tools for understanding DNA in a wide range of contexts. Their success heavily relies on the accuracy, sensitivity and specificity of methods used to discern differences between the reference genome and genomes under investigation. Here we compare the relative performances of five popular single nucleotide variant callers with and without their associated recommended hard filtering criteria. We compare: FreeBayes; the Genome Analysis Toolkit’s Haplotype Caller and Unified Genotyper; SAMtools; and VarScan. We tailor this comparison to suit smaller projects with modest sample numbers (n = 10) and coverage (~10X) to fill a current gap in the literature. Other comparison studies are generally applicable only to larger projects in model species, where there is access to large amounts of sequencing data and curated callsets for base and variant quality score recalibration. We estimated the accuracy, sensitivity and specificity of each pipeline according to the genotype concordance rate and number with the “truth” dataset for 10 canine samples. The truth dataset was defined as genotypes obtained from the CanineHD BeadChip array. Whole genome sequencing data was performed on the Illumina HiSeq2000 or HiSeq2500 platform as 100-101 base pair, paired end reads to an average sample coverage of 10.3X. Apart from GATK Haplotype Caller, applying recommended hard filters did not improve the performance of genotyping concordance at the tested levels of minimum coverage. The default VarScan pipeline with no additional filters applied (VarScan uses SAMtools mpileup, without base alignment quality computation) generally outperformed other callers in terms of accuracy, sensitivity and specificity. The results of this study demonstrate that hard filtering of variant calls from low-powered genome studies can impair accuracy, sensitivity and specificity of callsets and provides some benchmark performance metrics on a range of low coverage levels.

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Next generation sequencing reveals no change in mtDNA deletions upon the loss of mitochondrial fusion in mouse hearts

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