Biology subjects

Dobrovic, A.

Publications and source records attributed to Dobrovic, A..

3 recordsLinked to original sources

bioRxiv2018

A new normalization for the Nanostring nCounter gene expression assay

The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency, and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different data sets. We also offer suggestions on the design and analysis of studies involving this technology.

bioinformatics↗

bioRxiv2017

DNA methylation profiling of breast cancer cell lines along the epithelial mesenchymal spectrum - implications for the choice of circulating tumour DNA methylation markers

Epithelial-mesenchymal plasticity (EMP) is a dynamic process whereby epithelial carcinoma cells reversibly acquire morphological and invasive characteristics typical of mesenchymal cells, which facilitates metastasis. Understanding the methylation differences between epithelial and mesenchymal states may assist in the identification of optimal DNA methylation biomarkers for the blood-based monitoring of cancer. Methylation-sensitive high-resolution melting (MS-HRM) was used to examine the promoter methylation status of a panel of established and novel markers in a range of breast cancer cell lines spanning the epithelial-mesenchymal spectrum. Pyrosequencing was used to validate the MSHRM results. The results indicate an overall distinction in methylation between epithelial and mesenchymal phenotypes. The mesenchymal expression markers VIM, DKK3 and CRABP1 were methylated in the majority of epithelial breast cancer cell lines while methylation of the epithelial expression markers GRHL2, MIR200C and CDH1 was restricted to mesenchymal cell lines. We also examined EMP association of several methylation markers that have been used to assess minimal residual disease. Markers such as AKR1B1 and APC methylation proved to be selective for epithelial breast cell lines, however RASSF1A, RAR{beta}, TWIST1 and SFRP2 methylation was seen in both epithelial and mesenchymal cell lines, supporting their suitability for a multi-marker panel.

cancer biology↗

bioRxiv2017

GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly

The identification of genomic rearrangements, particularly in cancers, with high sensitivity and specificity using massively parallel sequencing remains a major challenge. Here, we describe the Genome Rearrangement IDentification Software Suite (GRIDSS), a high-speed structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph assembler. By combining assembly, split read and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line and patient tumour data, recently winning SV sub-challenge #5 of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods. GRIDSS identifies non-template sequence insertions, micro-homologies and large imperfect homologies, and supports multi-sample analysis. GRIDSS is freely available at https://github.com/PapenfussLab/gridss.

bioinformatics↗