Biology subjects

Troup, M.

Publications and source records attributed to Troup, M..

3 recordsLinked to original sources

bioRxiv2024

Systematic evaluation of de novo mutation calling tools using whole genome sequencing data

De novo mutations (DNMs) are genetic alterations that occur for the first time in an offspring. DNMs have been found to be a significant cause of severe developmental disorders. With the widespread use of next-generation sequencing (NGS) technologies, accurate detection of DNMs is crucial. Several bioinformatics tools have been developed to call DNMs from NGS data, but no study to date has systematically compared these tools. We used both real whole genome sequencing (WGS) data from a trio from the 1000 Genomes Project (1000G) and an in-house simulated trio dataset to evaluate five DNM calling tools: DeNovoGear, TrioDeNovo, PhaseByTransmission, VarScan2, and DeNovoCNN. For DNMs called in the real dataset, we observed 8.4% concordance of variants between all tools, while 83.8% of DNMs variants were identified by only one caller. For simulated trio WGS dataset spiked with 100 DNMs, the concordance rate was also low at 3.9%. DeNovoGear achieved the highest F1 score on the real 1000G dataset, while DeNovoCNN had the highest F1 score on the simulated data. Our study provides valuable recommendations for the selection and application of DNM callers on WGS trio data.

bioinformatics↗

bioRxiv2020

Maternal iron deficiency perturbs embryonic cardiovascular development

Congenital heart disease (CHD) is the most common type of birth defect, with a global prevalence of 0.9% of live births1. Most research in the last 30 years has focused on finding genetic causes of CHD. However, despite the association of over 100 genes with CHD, mutations in these genes only explain ~30% of cases2. Many of the remaining cases of CHD are caused by in utero exposure to environmental factors3. Here we have identified a completely new environmental teratogen causing CHD: maternal iron deficiency. In humans, iron deficiency anaemia is a major global health problem. 38% of pregnant women worldwide are anaemic4, and at least half of these are due to iron deficiency, the most prevalent micronutrient deficiency. We describe a mouse model of maternal iron deficiency anaemia that causes severe cardiovascular defects in her offspring. We show that these defects likely arise from increased retinoic acid signalling in iron deficient embryos, probably due to reduced activity of the iron-dependent retinoic acid catabolic CYP26 enzymes. The defects can be prevented by maternal iron administration early in pregnancy, and are also greatly reduced in offspring of mothers deficient in both iron and the retinoic acid precursor vitamin A. Finally, one puzzling feature of many genetic forms of CHD in humans is the considerable variation in penetrance and severity of defects. We show that maternal iron deficiency acts as a significant modifier of heart and craniofacial phenotype in a mouse model of Down syndrome. Given the high incidence of maternal iron deficiency, peri-conceptional iron monitoring and supplementation could be a viable strategy to reduce the prevalence and severity of CHD in human populations worldwide.

developmental biology↗

bioRxiv2018

Scavenger: A pipeline for recovery of unaligned reads utilising similarity with aligned reads

MotivationRead alignment is an important step in RNA-seq analysis as the result of alignment forms the basis for further downstream analyses. However, recent studies have shown that published alignment tools have variable mapping sensitivity and do not necessarily align reads which should have been aligned, a problem we termed as the false-negative non-alignment problem.\n\nResultsWe have developed Scavenger, a pipeline for recovering unaligned reads using a novel mechanism which utilises information from aligned reads. Scavenger performs recovery of unaligned reads by re-aligning unaligned reads against a putative location derived from aligned reads with sequence similarity against unaligned reads. We show that Scavenger can successfully recover unaligned reads in both simulated and real RNA-seq datasets, including single-cell RNA-seq data. The reads recovered contain more genetic variants compared to previously aligned reads, indicating that divergence between personal and reference genomes plays a role in the false-negative non-alignment problem. We also explored the impact of read recovery on downstream analyses, in particular gene expression analysis, and showed that Scavenger is able to both recover genes which were previously non-expressed and also increase gene expression, with lowly expressed genes having the most impact from the addition of recovered reads. We also found that the majority of genes with >1 fold change in expression after recovery are categorised as pseudogenes, indicating that pseudogene expression can be affected by the false-negative non-alignment problem. Scavenger helps to solve the false-negative non-alignment problem through recovery of unaligned reads using information from previously aligned reads.\n\nAvailabilityScavenger is available via an open source license in https://github.com/VCCRI/Scavenger/\n\nContactj.ho@victorchang.edu.au

bioinformatics↗