Biology subjects

Turner, I.

Publications and source records attributed to Turner, I..

4 recordsLinked to original sources

bioRxiv2024

ASPYRE-Lung: Validation of a simple, fast, robust and novel method for multi-variant genomic analysis of actionable NSCLC variants in tissue

Genomic variant testing of tumors is a critical gateway for patients to access the full potential of personalized oncology therapeutics. Current methods such as next-generation sequencing are costly and challenging to interpret, while PCR assays are limited in the number of variants they can cover. We developed ASPYRE(R) (Allele-Specific PYrophosphorolysis REaction) technology to address the urgent need for rapid, accessible and affordable diagnostics informing actionable genomic target variants of a given cancer. The targeted ASPYRE-Lung panel for non-small cell carcinoma covers 114 variants in 11 genes (ALK, BRAF, EGFR, ERBB2, KRAS, RET, ROS1, MET & NTRK1/2/3) to robustly inform clinical management. The assay detects single nucleotide variants, insertions, deletions, and gene fusions from tissue-derived DNA and RNA simultaneously. We tested the limit of detection, specificity, analytical accuracy and analytical precision of ASPYRE-Lung using FFPE lung tissue samples from patients with non-small cell lung carcinoma, variant-negative FFPE tissue from healthy donors, and FFPE-based contrived samples with controllable variant allele fractions. The sensitivity of ASPYRE-Lung was determined to be [≤] 3% variant allele fraction for single nucleotide variants and insertions or deletions, 100 copies for fusions, and 200 copies for MET exon 14 skipping. The specificity was 100% with no false positive results. The analytical accuracy test yielded no discordant calls between ASPYRE-Lung and expected results for clinical samples (via orthogonal testing) or contrived samples, and results were replicable across operators, reagent lots, runs, and real-time PCR instruments with a high degree of precision. The technology is simple and fast, requiring only four reagent transfer steps using standard laboratory equipment (PCR and qPCR instruments) with analysis via a cloud-based analysis algorithm. The ASPYRE-Lung assay has the potential to be transformative in facilitating access to rapid, actionable molecular profiling of tissue for patients with non-small cell carcinoma.

cancer biology↗

bioRxiv2019

Fatigue, effort perception and central activation failure in chronic stroke survivors: a TMS and fMRI investigation

Fatigue is commonly seen in stroke survivors and the most common manifestation of fatigue is the requirement of high effort for activities of daily life. In this study we set out to identify the neural correlates of perceived effort and central activation failure, a neurophysiological measure correlated with perceived effort. Methods: Twelve chronic stroke survivors participated in this study. Fatigue levels were quantified using the Fatigue Severity Scale -7, perceived effort was quantified using a 1-10 numerical rating scale while performing an isometric biceps hold task, Central Activation Failure was quantified using the modified twitch interpolation technique using Transcranial Magnetic Stimulation and functional Magnetic Resonance Imaging was used to measure blood-oxygen-level dependent signal (BOLD) from the brain while the participant performed a hand grip task. Analysis: Following standard pre-processing procedures for fMRI data using SPM software, co-variance of BOLD signal with perceived effort levels and central activation failure was evaluated. Correlation analysis was performed between measures of fatigue and effort. Results: The main findings of this study were 1) high fatigue was associated with high perceived effort 2) higher perceived effort was associated with greater increase in BOLD fMRI activity in pre-SMA and the ipsilateral inferior frontal gyrus with increasing force 3) greater Central Activation Failure was associated with higher increase in BOLD fMRI activity in bilateral caudate, contralateral superior frontal gyrus and pre-motor cortices with increasing force.

neuroscience↗

bioRxiv2017

Mapping the drivers of within-host pathogen evolution using massive data sets

Differences among hosts, resulting from genetic variation in the immune system or heterogeneity in drug treatment, can impact within-host pathogen evolution. Identifying such interactions can potentially be achieved through genetic association studies. However, extensive and correlated genetic population structure in hosts and pathogens presents a substantial risk of confounding analyses. Moreover, the multiple testing burden of interaction scanning can potentially limit power. To address these problems, we have developed a Bayesian approach for detecting host influences on pathogen evolution that makes use of vast existing data sets of pathogen diversity to improve power and control for stratification. The approach models key processes, including recombination and selection, and identifies regions of the pathogen genome affected by host factors. Using simulations and empirical analysis of drug-induced selection on the HIV-1 genome we demonstrate the power of the method to recover known associations and show greatly improved precision-recall characteristics compared to other approaches. We build a high-resolution map of HLA-induced selection in the HIV-1 genome, identifying novel epitope-allele combinations.

genetics↗

bioRxiv2017

Integrating long-range connectivity information into de Bruijn graphs

MotivationThe de Bruijn graph is a simple and efficient data structure that is used in many areas of sequence analysis including genome assembly, read error correction and variant calling. The data structure has a single parameter k, is straightforward to implement and is tractable for large genomes with high sequencing depth. It also enables representation of multiple samples simultaneously to facilitate comparison. However, unlike the string graph, a de Bruijn graph does not retain long range information that is inherent in the read data. For this reason, applications that rely on de Bruijn graphs can produce sub-optimal results given their input.\n\nResultsWe present a novel assembly graph data structure: the Linked de Bruijn Graph (LdBG). Constructed by adding annotations on top of a de Bruijn graph, it stores long range connectivity information through the graph. We show that with error-free data it is possible to losslessly store and recover sequence from a Linked de Bruijn graph. With assembly simulations we demonstrate that the LdBG data structure outperforms both the de Bruijn graph and the String Graph Assembler (SGA). Finally we apply the LdBG to Klebsiella pneumoniae short read data to make large (12 kbp) variant calls, which we validate using PacBio sequencing data, and to characterise the genomic context of drug-resistance genes.\n\nAvailabilityLinked de Bruijn Graphs and associated algorithms are implemented as part of McCortex, available under the MIT license at https://github.com/mcvean/mccortex.\n\nContactturner.isaac@gmail.com.

bioinformatics↗