About — qPCR Guru

Our Mission

Quantitative PCR is one of the most widely used techniques in molecular biology — yet the software landscape has long been divided between expensive commercial tools and fragmented free scripts that require programming knowledge.

qPCR Guru was built to close that gap. We believe every researcher, student, and clinician should have access to rigorous, MIQE-compliant qPCR analysis without financial barriers, software installation, or coding expertise.

Our Values

🔓 Accessible Science

We believe professional-grade qPCR analysis should be accessible to every researcher. qPCR Guru offers powerful analysis tools at no cost, removing barriers for students, small labs, and researchers worldwide.

🔒 Privacy First

Your data is never stored on our servers. We don't save, share, or sell your experimental data. Your files are processed to generate results and then discarded.

📐 Scientific Rigor

Built on established methods (Livak, Pfaffl, Vandesompele, Andersen) and aligned with MIQE 2.0 guidelines. Every algorithm is transparent and citation-backed.

⚡ Zero Friction

No software to install, no account required to get started, no learning curve. Upload a file, get results. Works on any device with a modern browser.

Team

Amar M. Singh, PhD

Co-Founder & CEO

Dr. Singh has 20+ years of experience in molecular & cellular biology in both academia and biotech, with deep expertise in assay development and analysis. He has published numerous articles, presented at national and international meetings, and received several patents and awards for his work.

Owen Singh

Co-Founder & CTO

Mr. Singh is a full-time student with extensive experience across multiple coding languages and platforms including C/C++, HTML, and Node.js. He has developed several online programs and has been recognized with multiple awards for his work at state-wide technology competitions.

The Science Behind qPCR Guru

qPCR Guru implements established, peer-reviewed methods for qPCR data analysis:

ΔΔCq method — Livak & Schmittgen (2001). Relative quantification assuming equal primer efficiency.
Pfaffl method — Pfaffl (2001). Efficiency-corrected relative quantification using measured primer efficiencies.
geNorm M — Vandesompele et al. (2002). Pairwise stability measure for reference gene validation.
NormFinder ρ — Andersen et al. (2004). Model-based stability estimation for reference gene selection.
MIQE 2.0 — Bustin et al. (2025). Minimum Information for Publication of Quantitative Real-Time PCR Experiments, updated guidelines.
Global mean normalization — Mestdagh et al. (2009). Reference-free normalization for large target panels (≥15–20 genes).

The MIQE 2.0 guidelines emphasize that rigorous experimental design, proper assay validation, and transparent data reporting are essential for reliable and reproducible qPCR results.

— Based on Bustin et al., MIQE 2.0, Clinical Chemistry (2025)

Technical Architecture

qPCR Guru is built with React and uses modern web technologies to deliver a fast, responsive analysis experience.

Key technologies:

SheetJS (XLSX) for reading Excel, CSV, and tabular data formats
Canvas 2D API for high-resolution, publication-quality figure rendering
SVG for interactive heatmaps with hierarchical clustering dendrograms
Pako for ZIP/RDML decompression

Contact

Have questions, suggestions, or bug reports? We'd love to hear from you.

Email: support@qpcrguru.com

About qPCR Guru