Biology subjects

Lynch, A. G.

Publications and source records attributed to Lynch, A. G..

3 recordsLinked to original sources

bioRxiv2018

Low-cost and clinically applicable copy number profiling using repeat DNA

Large-scale cancer genome studies suggest that tumors are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Due to the low-cost, the clinical use of genomics assays is biased towards targeted gene panels, which identify SNVs. There is a need for a comparably low-cost and simple assay for high-resolution SCNA profiling. Here we present our method, conliga, which infers SCNA profiles from a low-cost and simple assay.

genomics↗

bioRxiv2017

Telomerecat: A Ploidy-Agnostic Method For Estimating Telomere Length From Whole Genome Sequencing Data

Telomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, technical replicates, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype.

bioinformatics↗

bioRxiv2017

Mining human prostate cancer datasets: The "camcAPP" shiny app

Obtaining access to robust, well-annotated human genomic datasets is an important step in demonstrating relevance of experimental findings and, often, in generating the hypotheses that led to those experiments being conducted in the first place. We recently published data from two cohorts of men with prostate cancer who had undergone prostatectomy, men from Cambridge, UK and Stockholm, Sweden.1 We considered how we might best share our output with those who wish to interrogate the data with their own ideas, gene lists and clinical questions. We recognised that finding, down-loading, processing and assimilating any such dataset into a usable format is daunting and may put off even the more persistent researcher. We also felt that interrogation tools generated to date (e.g. cBioPortal) lack functionality as they either cover too many organ types, or fall short in terms of clinical annotation. We therefore determined to produce an accessible web-based platform ...

cancer biology↗