Bayesian modelling of high-throughput sequencing assays with malacoda.

NGS studies have uncovered an ever-growing catalog of human variation while leaving an enormous gap between observed variation and experimental characterization of variant function. High-throughput screens powered by NGS have greatly increased the rate of variant functionalization, but the development of comprehensive statistical methods to analyze screen data has lagged. In the massively parallel reporter assay (MPRA), short barcodes are counted by sequencing DNA libraries transfected into cells and the cell's output RNA in order to simultaneously measure the shifts in transcription induced by thousands of genetic variants. These counts present many statistical challenges, including overdispersion, depth dependence, and uncertain DNA concentrations. So far, the statistical methods used have been rudimentary, employing transformations on count level data and disregarding experimental and technical structure while failing to quantify uncertainty in the statistical model. We have developed an extensive framework for the analysis of NGS functionalization screens available as an R package called malacoda (available from github.com/andrewGhazi/malacoda). Our software implements a probabilistic, fully Bayesian model of screen data. The model uses the negative binomial distribution with gamma priors to model sequencing counts while accounting for effects from input library preparation and sequencing depth. The method leverages the high-throughput nature of the assay to estimate the priors empirically. External annotations such as ENCODE data or DeepSea predictions can also be incorporated to obtain more informative priors-a transformative capability for data integration. The package also includes quality control and utility functions, including automated barcode counting and visualization methods. To validate our method, we analyzed several datasets using malacoda and alternative MPRA analysis methods. These data include experiments from the literature, simulated assays, and primary MPRA data. We also used luciferase assays to experimentally validate several hits from our primary data, as well as variants for which the various methods disagree and variants detectable only with the aid of external annotations.

Low-level parental somatic mosaic SNVs in exomes from a large cohort of trios with diverse suspected Mendelian conditions.

PURPOSE: The goal of this study was to assess the scale of low-level parental mosaicism in exome sequencing (ES) databases. METHODS: We analyzed approximately 2000 family trio ES data sets from the Baylor-Hopkins Center for Mendelian Genomics (BHCMG) and Baylor Genetics (BG). Among apparent de novo single-nucleotide variants identified in the affected probands, we selected rare unique variants with variant allele fraction (VAF) between 30% and 70% in the probands and lower than 10% in one of the parents. RESULTS: Of 102 candidate mosaic variants validated using amplicon-based next-generation sequencing, droplet digital polymerase chain reaction, or blocker displacement amplification, 27 (26.4%) were confirmed to be low- (VAF between 1% and 10%) or very low (VAF <1%) level mosaic. Detection precision in parental samples with two or more alternate reads was 63.6% (BHCMG) and 43.6% (BG). In nine investigated individuals, we observed variability of mosaic ratios among blood, saliva, fibroblast, buccal, hair, and urine samples. CONCLUSION: Our computational pipeline enables robust discrimination between true and false positive candidate mosaic variants and efficient detection of low-level mosaicism in ES samples. We confirm that the presence of two or more alternate reads in the parental sample is a reliable predictor of low-level parental somatic mosaicism.

Expression of HIV transgene aggravates kidney injury in diabetic mice.

With the widespread use of combination antiretroviral agents, the incidence of HIV-associated nephropathy has decreased. Currently, HIV-infected patients live much longer and often suffer from comorbidities such as diabetes mellitus. Recent epidemiological studies suggest that concurrent HIV infection and diabetes mellitus may have a synergistic effect on the incidence of chronic kidney disease. To address this, we determined whether HIV-1 transgene expression accelerates diabetic kidney injury using a diabetic HIV-1 transgenic (Tg26) murine model. Diabetes was initially induced with low-dose streptozotocin in both Tg26 and wild-type mice on a C57BL/6 background, which is resistant to classic HIV-associated nephropathy. Although diabetic nephropathy is minimally observed on the C57BL/6 background, diabetic Tg26 mice exhibited a significant increase in glomerular injury compared with nondiabetic Tg26 mice and diabetic wild-type mice. Validation of microarray gene expression analysis from isolated glomeruli showed a significant upregulation of proinflammatory pathways in diabetic Tg26 mice. Thus, our study found that expression of HIV-1 genes aggravates diabetic kidney disease.