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Harbin: a quantitation PCR analysis tool.

Rachelle Bester¹, Pieter T Pepler², Dirk J Aldrich¹

¹Department of Genetics, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa.

Biotechnology Letters

|October 4, 2016

Summary

This summary is machine-generated.

Harbin, a new web application, enables relative quantitation experiment analysis using a quantile scoring system. A novel bootstrap test improves data set comparison for applications like virus detection.

Keywords:

Bootstrap Concentration ratios GLRaV-3 Genetic variants Harbin Location-scale-shape problem Quantile-based scoring RT-qPCR

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Area of Science:

Bioinformatics
Molecular Biology
Data Analysis

Background:

Quantitative biological experiments often yield complex datasets.
Comparing results across different experiments or time points can be challenging.
Standard methods may lack sensitivity for detecting subtle data distributional differences.

Purpose of the Study:

To develop a web-based application, Harbin, for analyzing and comparing relative quantitation experiments.
To introduce a quantile-based scoring system for robust sample comparison.
To propose a novel statistical test for assessing the compatibility of different datasets.

Main Methods:

Harbin utilizes the standard curve method for relative quantitation, calculating concentration ratios (CRs).
A Harbin quantile bootstrap test is introduced to evaluate dataset comparability.
The Kolmogorov-Smirnov test is used as a benchmark for comparison.

Main Results:

The Harbin quantile bootstrap test demonstrates higher sensitivity in detecting distributional differences between datasets compared to the Kolmogorov-Smirnov test.
The system's utility is validated using RT-qPCR data from grapevine leafroll associated virus 3 (GLRaV-3) experiments.
Concentration ratios (CRs) are calculated for inter-experiment sample comparisons.

Conclusions:

The quantile-based scoring system facilitates monitoring of virus titers or gene expression over time.
Harbin and its associated testing method are valuable for genomic applications requiring data set integration.
This approach enhances the reliability and comparability of quantitative biological data.