paper-with-me

홈 › Papers

Absolute quantification of real-time PCR data with stage signal difference analysis

2021-02-28 · Chuanbo Liu, Jin Wang

Real-time PCR, or Real-time Quantitative PCR (qPCR) is an effective approach to quantify nucleic acid samples. Given the complicated reaction system along with thermal cycles, there has been long-term confusion on accurately calculating the initial nucleic acid amounts from the fluorescence signals. Although many improved algorithms had been proposed, the classical threshold method is still the primary choice in the routine application. In this study, we will first illustrate the origin of the linear relationship between the threshold value and logarithm of the initial nucleic acid amount by reconstructing the PCR reaction process with stochastic simulations. We then develop a new method for the absolute quantification of nucleic acid samples with qPCR. By monitoring the fluorescence signal changes in every stage of the thermal cycle, we are able to calculate a representation of the step-wise efficiency change. This is the first work calculated PCR efficiency change directly from the fluorescence signal, without fitting or sophisticated analysis. Our results revealed that the efficiency change during the PCR process is complicated and can not be modeled simply by monotone function model. Based on the calculated efficiency, we illustrate a new absolute qPCR analysis method for accurately determining nucleic acid amount. The efficiency problem is completely avoided in this new method.

📄 PDF Abstract BibTeX arXiv:2103.00408

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

SAM-dPCR: Real-Time and High-throughput Absolute Quantification of Biological Samples Using Zero-Shot Segment Anything Model

2024-01-22 · Yuanyuan Wei, Shanhang Luo, Changran Xu, Yingqi Fu 외

Digital PCR (dPCR) has revolutionized nucleic acid diagnostics by enabling absolute quantification of rare mutations and target sequences. However, current detection methodologies face challenges, as flow cytometers are …

Self-Supervised Learning

Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors

2023-09-04 · Yuanyuan Wei, Sai Mu Dalike Abaxi, Nawaz Mehmood, Luoquan Li 외

Absolute quantification of biological samples entails determining expression levels in precise numerical copies, offering enhanced accuracy and superior performance for rare templates. However, existing methodologies suf…

Transfer Learning

A superpixel-driven deep learning approach for the analysis of dermatological wounds

2019-09-13 · Gustavo Blanco, Agma J. M. Traina, Caetano Traina Jr., Paulo M. Azevedo-Marques 외

Background. The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU, that co…

BIG-bench Machine LearningDeep LearningSegmentationSpecificity+1

SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation

2020-07-29 · ECCV 2020 8 · Jiale Cao, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan 외

Single-stage instance segmentation approaches have recently gained popularity due to their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage methods. We propose a fast single-stage ins…

Instance Segmentationobject-detectionObject DetectionReal-time Instance Segmentation+3

A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation

2025-01-14 · Steven Landgraf, Rongjun Qin, Markus Ulrich

While recent foundation models have enabled significant breakthroughs in monocular depth estimation, a clear path towards safe and reliable deployment in the real-world remains elusive. Metric depth estimation, which inv…

Computational EfficiencyDepth EstimationMonocular Depth EstimationPose Estimation+2