Biology subjects

Vivian, J.

Publications and source records attributed to Vivian, J..

3 recordsLinked to original sources

bioRxiv2022

Accurate simultaneous sequencing of genetic and epigenetic bases in DNA

DNA comprises molecular information stored via genetic bases (G, C, T, A) and also epigenetic bases, principally 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Both genetic and epigenetic information are vital to our understanding of biology and disease states. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases typically fail to capture common C-to-T mutations or distinguish 5mC from 5hmC. Here, we present a single-base-resolution sequencing methodology that will simultaneously sequence complete genetics and complete epigenetics in a single workflow. The approach is non-destructive to DNA and provides a digital readout of bases, which we exemplify by simultaneous sequencing of G, C, T, A, 5mC and 5hmC; 6-Letter sequencing. We demonstrate sequencing of human genomic DNA and also cell-free DNA taken from a blood sample of a cancer patient. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. We envisage it will be versatile across many applications in life sciences.

genomics↗

bioRxiv2019

Framework for determining accuracy of RNA sequencing data for gene expression profiling of single samples

BackgroundThe accuracy of gene expression as measured by RNA sequencing (RNA-Seq) is dependent on the amount of sequencing performed. However, some types of reads are not informative for determining this accuracy. Unmapped and non-exonic reads do not contribute to gene expression quantification. Duplicate reads can be the product of high gene expression or technical errors. FindingsWe surveyed bulk RNA-Seq datasets from 2179 tumors in 48 cohorts to determine the fractions of uninformative reads. Total sequence depth was 0.2-668 million reads (median (med.) 61 million; interquartile range (IQR) 53 million). Unmapped reads constitute 1-77% of all reads (med. 3%; IQR 3%); duplicate reads constitute 3-100% of mapped reads (med. 27%; IQR 30%); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (med. 25%; IQR 21%). Informative reads--Mapped, Exonic, Non-duplicate (MEND) reads--constitute 0-79% of total reads (med. 50%; IQR 31%). Further, we find that MEND read counts have a 0.22 Pearson correlation to the number of genes expressed above 1 Transcript Per Million, while total reads have a correlation of -0.05. ConclusionsSince the fraction of uninformative reads vary, we propose using only definitively informative reads, MEND reads, for the purposes of asserting the accuracy of gene expression measured in a bulk RNA-Seq experiment. We provide a Docker image containing 1) the existing required tools (RSeQC, sambamba and samblaster) and 2) a custom script. We recommend that all results, sensitivity studies and depth recommendations use MEND units.

genomics↗

bioRxiv2019

A Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

ObjectiveMany antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high inter-sample variance. Moreover, some cancer samples have misidentified tissues or origin, or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparing to a single patient sample.\n\nMaterials and MethodsWe propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and under-expression.\n\nResultsWe demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissues samples. Further, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns.\n\nConclusionsThis exploratory method is suitable for identifying expression outliers from comparative RNA-seq analysis for individual samples and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing their pediatric cohort.

bioinformatics↗