Publications by authors named "Jinny Zhang"

Quantitative PCR (qPCR) is the gold standard for detection and quantitation of known DNA targets, but the scarcity of spectrally distinct fluorophores and filter sets limits the number of detectable targets. Here, we introduce color cycle multiplex amplification (CCMA) to significantly increase the number of detectable DNA targets in a single qPCR reaction using standard instrumentation. In CCMA, presence of one DNA target species results in a pre-programmed pattern of fluorescence increases.

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Background: Microsatellite instability (MSI) indicates DNA mismatch repair deficiency in certain types of cancer, such as colorectal cancer. The current gold standard technique, PCR-capillary electrophoresis (CE), requires matching normal samples and specialized instrumentation. We developed VarTrace, a rapid and low-cost quantitative PCR (qPCR) assay, to evaluate MSI using solely the tumor sample DNA, obviating the requirement for matching normal samples.

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According to the Fear-avoidance (FA) model, FA beliefs can lead to disability due to avoidance of activities expected to result in pain or further injury. Extensive research on the relationship of FA, pain, catastrophizing, and disability has been generated with patients suffering from chronic neck and back pain, but little research has been conducted with burn survivors. To address this need, the Burn Survivor FA Questionnaire (BSFAQ) was developed (Langlois J, Vincent-Toskin, S, Duchesne, P et al.

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Molecular detection of disease-associated mutations, especially those with low abundance, is essential for academic research and clinical diagnosis. Certain variant detection methods reach satisfactory sensitivity and specificity in detecting rare mutations based on the introduction of blocking oligos to prevent the amplification of wild-type or unwanted templates, thus selectively amplifying and enriching the mutations. These blocking oligos usually suppress PCR amplification through the 3' chemical modifications, with high price, slow synthesis, and reduced purity.

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Current gold standard for absolute quantitation of a specific DNA sequence is droplet digital PCR (ddPCR), which has been applied to copy number variation (CNV) detection. However, the number of quantitation modules in ddPCR is limited by fluorescence channels, which thus limits the CNV sensitivity due to sampling error following Poisson distribution. Here we develop a PCR-based molecular barcoding NGS approach, quantitative amplicon sequencing (QASeq), for accurate absolute quantitation scalable to over 200 quantitation modules.

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Cell-free DNA (cfDNA) in the circulating blood plasma of patients with cancer contains tumour-derived DNA sequences that can serve as biomarkers for guiding therapy, for the monitoring of drug resistance, and for the early detection of cancers. However, the analysis of cfDNA for clinical diagnostic applications remains challenging because of the low concentrations of cfDNA, and because cfDNA is fragmented into short lengths and is susceptible to chemical damage. Barcodes of unique molecular identifiers have been implemented to overcome the intrinsic errors of next-generation sequencing, which is the prevailing method for highly multiplexed cfDNA analysis.

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Quantitation of rare somatic mutations is essential for basic research and translational clinical applications including minimal residual disease (MRD) detection. Though unique molecular identifier (UMI) has suppressed errors for rare mutation detection, the sequencing depth requirement is high. Here, we present Quantitative Blocker Displacement Amplification (QBDA) which integrates sequence-selective variant enrichment into UMI quantitation for accurate quantitation of mutations below 0.

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Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences.

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Assays for the molecular detection of nucleic acids are typically constrained by the level of multiplexing (this is the case for the quantitative polymerase chain reaction (qPCR) and for isothermal amplification), turnaround times (as with microarrays and next-generation sequencing), quantification accuracy (isothermal amplification, microarrays and nanopore sequencing) or specificity for single-nucleotide differences (microarrays and nanopore sequencing). Here we show that a portable and battery-powered PCR assay performed in a toroidal convection chamber housing a microarray of fluorescently quenched oligonucleotide probes allows for the rapid and sensitive quantification of multiple DNA targets with single-nucleotide discrimination. The assay offers a limit of detection of 10 DNA copies within 30 min of turnaround time and a dynamic range spanning 4 orders of magnitude of DNA concentration, and we show its performance by detecting 20 genomic loci and 30 single-nucleotide polymorphisms in human genomic DNA samples, and 15 bacterial species in clinical isolates.

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Hybridization is a key molecular process in biology and biotechnology, but so far there is no predictive model for accurately determining hybridization rate constants based on sequence information. Here, we report a weighted neighbour voting (WNV) prediction algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants. To construct this algorithm we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (36 nt sub-sequences of the CYCS and VEGF genes) at temperatures ranging from 28 to 55 °C.

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