Biology subjects

Huggett, J.

Publications and source records attributed to Huggett, J..

4 recordsLinked to original sources

bioRxiv2023

Standardisation of cell-free DNA measurements: An International Study on Comparability of Low Concentration DNA Measurements using cancer variants

For the impact of genomic testing from liquid biopsies to be maximized, mechanisms to ensure reproducible and comparable test performance will be required. This can be established and maintained through reference measurement procedures and materials with property values that are internationally comparable through traceability to a common standard. To achieve this objective, an interlaboratory study was organised to explore digital PCR (dPCR) for standardisation of cell-free DNA (cfDNA) quantification. Blinded samples of wild-type/variant mixtures of two DNA sequences (BRAF p.V600E single nucleotide variant or EGFR exon 19 deletion) were provided to 12 laboratories. Laboratories independently designed and applied dPCR assays to determine absolute and relative quantities, with no guidance provided to harmonise the approach. The mean and coefficient of variation (CV) of copy number concentrations for variant sequences were 18 copies/L (CV 7.2%) (BRAF variant sample) and 9 copies/L (CV 25%) (EGFR variant sample) while the mean variant allele frequencies (vAF) were 8.0% (CV 5.3%) and 0.080% (CV 29%) respectively. This study demonstrated that dPCR was capable of exceptional technical accuracy for variant copy number concentration and vAF, even when different assays and platforms were used. This implies that dPCR offers a unique analytical methodology that can be deployed globally in supporting comparability for cfDNA testing based on the existing framework of the International System of units of measurement.

genetics↗

bioRxiv2021

PCRedux: A Data Mining and Machine Learning Toolkit for qPCR Experiments

MotivationQuantitative Real-time PCR (qPCR) is a widely used -omics method for the precise quantification of nucleic acids, in which the result is associated with the presence/absence or quantity of a specific nucleic acid sequence. As the amount of qPCR data increases worldwide, the manual assessment of results becomes challenging and difficult to reproduce. To overcome this, some automatable characteristics of amplification curves have been described in the literature, often with an appropriate "rule of thumb". ResultsWe developed PCRedux to analyze and calculate 90 numerical qPCR amplification curve descriptors ( features") from large datasets of qPCR amplification curves that are aimed for interpretable machine learning and development of decision support systems. In a case study of a diverse dataset with 3181 positive, negative and ambiguous amplification curves, as assessed by three human raters, we demonstrate a sensitivity >99 % and specificity >97 % in detecting positive and negative amplification. PCRedux is unique as it goes beyond traditional qPCR analysis to capture curvature properties that improve the characterization and classification of amplification curves. The calculation of the features is reproducible and objective, since R is used as a controllable working environment. PCRedux is not a black box, but open source software following on the principle of mathematically interpretable features. These can be combined with user-defined labels for automatic multi-category classification and regression in machine learning. Availabilityhttps://cran.r-project.org/package=PCRedux. Web server: http://shtest.evrogen.net/PCRedux-app/. Documentation: https://PCRuniversum.github.io/PCRedux/.

bioinformatics↗

bioRxiv2020

Comparison of SARS-CoV2 N gene real-time RT-PCR targets and commercially available mastermixes

We aim to test four one-step RT real-time mastermix options for use in SARS-CoV2 real-time PCR, with three primer/probe assays targeting the N gene. The lower limit of detection is determined using a SARS CoV2 N gene RNA transcript dilution series (to 1 copy/{micro}l) and verified using 74 nose and throat swabs. The N2 assay demonstrates the most sensitive detection of SARS-Cov-2 RNA. Three of the four mastermixes performed well, with the Takara One Step PrimeScript III RT-PCR Kit mastermix demonstrating improved performance at the lower limit of detection.

microbiology↗

bioRxiv2019

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: An inter-laboratory study

BackgroundAntimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a one-stop test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data sequenced from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants and identify problem cases and factors that lead to discordant results.\n\nMethodsWe produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ( participants) were provided these sequence data without any other contextual information. Each participant used their own pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime.\n\nResultsIndividual participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment a different antibiotic would have been recommended for each isolate by at least one participant.\n\nConclusionsWe found that participants produced discordant predictions from identical WGS data. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases and standardisation in the comparisons between genotype and resistance phenotypes will be fundamental before AST prediction using WGS can be successfully implemented in standard clinical microbiology laboratories.

microbiology↗