The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for ...efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies.
RAREMETAL is a computationally efficient tool for meta-analysis of rare variants genotyped using sequencing or arrays. RAREMETAL facilitates analyses of individual studies, accommodates a variety of ...input file formats, handles related and unrelated individuals, executes both single variant and burden tests and performs conditional association analyses.
http://genome.sph.umich.edu/wiki/RAREMETAL for executables, source code, documentation and tutorial.
DNA sequencing identifies common and rare genetic variants for association studies, but studies typically focus on variants in nuclear DNA and ignore the mitochondrial genome. In fact, analyzing ...variants in mitochondrial DNA (mtDNA) sequences presents special problems, which we resolve here with a general solution for the analysis of mtDNA in next-generation sequencing studies. The new program package comprises 1) an algorithm designed to identify mtDNA variants (i.e., homoplasmies and heteroplasmies), incorporating sequencing error rates at each base in a likelihood calculation and allowing allele fractions at a variant site to differ across individuals; and 2) an estimation of mtDNA copy number in a cell directly from whole-genome sequencing data. We also apply the methods to DNA sequence from lymphocytes of ~2,000 SardiNIA Project participants. As expected, mothers and offspring share all homoplasmies but a lesser proportion of heteroplasmies. Both homoplasmies and heteroplasmies show 5-fold higher transition/transversion ratios than variants in nuclear DNA. Also, heteroplasmy increases with age, though on average only ~1 heteroplasmy reaches the 4% level between ages 20 and 90. In addition, we find that mtDNA copy number averages ~110 copies/lymphocyte and is ~54% heritable, implying substantial genetic regulation of the level of mtDNA. Copy numbers also decrease modestly but significantly with age, and females on average have significantly more copies than males. The mtDNA copy numbers are significantly associated with waist circumference (p-value = 0.0031) and waist-hip ratio (p-value = 2.4×10-5), but not with body mass index, indicating an association with central fat distribution. To our knowledge, this is the largest population analysis to date of mtDNA dynamics, revealing the age-imposed increase in heteroplasmy, the relatively high heritability of copy number, and the association of copy number with metabolic traits.
Background. Next generation sequencing (NGS) is being widely used to identify genetic variants associated with human disease. Although the approach is cost effective, the underlying data is ...susceptible to many types of error. Importantly, since NGS technologies and protocols are rapidly evolving, with constantly changing steps ranging from sample preparation to data processing software updates, it is important to enable researchers to routinely assess the quality of sequencing and alignment data prior to downstream analyses. Results. Here we describe QPLOT, an automated tool that can facilitate the quality assessment of sequencing run performance. Taking standard sequence alignments as input, QPLOT generates a series of diagnostic metrics summarizing run quality and produces convenient graphical summaries for these metrics. QPLOT is computationally efficient, generates webpages for interactive exploration of detailed results, and can handle the joint output of many sequencing runs. Conclusion. QPLOT is an automated tool that facilitates assessment of sequence run quality. We routinely apply QPLOT to ensure quick detection of diagnostic of sequencing run problems. We hope that QPLOT will be useful to the community as well.
DNA sequencing identifies common and rare genetic variants for association studies, but studies typically focus on variants in nuclear DNA and ignore the mitochondrial genome. In fact, analyzing ...variants in mitochondrial DNA (mtDNA) sequences presents special problems, which we resolve here with a general solution for the analysis of mtDNA in next-generation sequencing studies. The new program package comprises 1) an algorithm designed to identify mtDNA variants (i.e., homoplasmies and heteroplasmies), incorporating sequencing error rates at each base in a likelihood calculation and allowing allele fractions at a variant site to differ across individuals; and 2) an estimation of mtDNA copy number in a cell directly from whole-genome sequencing data. We also apply the methods to DNA sequence from lymphocytes of ~2,000 SardiNIA Project participants. As expected, mothers and offspring share all homoplasmies but a lesser proportion of heteroplasmies. Both homoplasmies and heteroplasmies show 5-fold higher transition/transversion ratios than variants in nuclear DNA. Also, heteroplasmy increases with age, though on average only ~1 heteroplasmy reaches the 4% level between ages 20 and 90. In addition, we find that mtDNA copy number averages ~110 copies/lymphocyte and is ~54% heritable, implying substantial genetic regulation of the level of mtDNA. Copy numbers also decrease modestly but significantly with age, and females on average have significantly more copies than males. The mtDNA copy numbers are significantly associated with waist circumference (p-value = 0.0031) and waist-hip ratio (p-value = 2.4×10-5), but not with body mass index, indicating an association with central fat distribution. To our knowledge, this is the largest population analysis to date of mtDNA dynamics, revealing the age-imposed increase in heteroplasmy, the relatively high heritability of copy number, and the association of copy number with metabolic traits.
•Inter-laboratory study with 174 participants using STRmix™.•CE analysis settings resulted in larger differences in LR than PG software.•Differences in log(LR) due to MCMC variation were less than ...one order of magnitude.
An intra and inter-laboratory study using the probabilistic genotyping (PG) software STRmix™ is reported. Two complex mixtures from the PROVEDIt set, analysed on an Applied Biosystems™ 3500 Series Genetic Analyzer, were selected. 174 participants responded.
For Sample 1 (low template, in the order of 200 rfu for major contributors) five participants described the comparison as inconclusive with respect to the POI or excluded him. Where LRs were assigned, the point estimates ranging from 2 × 104 to 8 × 106. For Sample 2 (in the order of 2000 rfu for major contributors), LRs ranged from 2 × 1028 to 2 × 1029. Where LRs were calculated, the differences between participants can be attributed to (from largest to smallest impact):
•varying number of contributors (NoC),•the exclusion of some loci within the interpretation,•differences in local CE data analysis methods leading to variation in the peaks present and their heights in the input files used,•and run-to-run variation due to the random sampling inherent to all MCMC-based methods.
This study demonstrates a high level of repeatability and reproducibility among the participants. For those results that differed from the mode, the differences in LR were almost always minor or conservative.
Background
The COVID‐19 virtual ward was created to provide care for people at home with COVID‐19. Given this was a new model of care, little was known about the clinical characteristics and outcomes ...of patients requiring admission to hospital from the virtual ward platform.
The aims were to characterise hospital admission volume, patient epidemiology, clinical characteristics, and outcome from a virtual ward in the setting of an Omicron (BA.1, BA.2) outbreak.
Methods
A retrospective observational study was performed for all virtual ward patients admitted from 1st January 2022 to 25th March 2022 (over 16 years old). Epidemiological, clinical and laboratory data was reviewed on all patients who required hospital admission.
Results
A total of 7021 patients were cared for on the virtual ward over the study period with 473 referred to hospital for assessment. Twenty‐six (0.4%) patients were admitted to hospital during their care on the ward. Twenty‐two (84.6%) admissions were COVID‐19 related. Fifty three percent of the hospitalised patients were fully vaccinated and 11 had received prior therapeutics for COVID‐19. Shortness of breath was the most common reason for escalation to hospital. Chest pain was the second most common reason and the most common diagnosis after investigation was non‐cardiac chest pain.
Conclusions
Few patients required admission from the virtual ward in the setting of the Omicron variant (BA.1, BA.2) as a direct result of COVID‐19 disease and virtual ward care. Shortness of breath and chest pain were the most common symptoms driving further clinical care.
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