sustc-xylab

sustc-xylab / NanoQMRA

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NanoQMRA is a bioinformatics tool for quantitative microbial risk assessment (QMRA) based on nanopore sequencing data. It is designed to calculate QMRA-related risk values for both individual reads in species level.

21
3
100% credibility
Found May 02, 2026 at 21 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

NanoQMRA analyzes nanopore sequencing data to compute quantitative microbial risk scores at the read and species levels by evaluating pathogen association, genetic mobility, and antibiotic resistance.

How It Works

1
🔬 Discover NanoQMRA

You learn about a handy tool that helps evaluate risks from genetic sequencing data by spotting dangerous germs, movable genes, and resistance to medicines.

2
💻 Get it ready

Download the tool to your computer and prepare the supporting helpers it needs to check your data.

3
📁 Prepare your data

Gather your sequencing reads file, making sure the name is simple without spaces.

4
▶️ Start the check

Launch the tool with your data file, the number of workers, and the right setup for analysis – it begins scanning right away!

5
⏳ Watch it process

Relax as it examines each genetic piece for threats, mobility, and resistance, creating scores along the way.

📊 Review your results

Open the output folder to find scored tables, ranked risk lists, and an overall sample risk number to guide your decisions.

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AI-Generated Review

What is NanoQMRA?

NanoQMRA is a Python-based bioinformatics tool designed to calculate QMRA-related risk values for quantitative microbial risk assessment from nanopore sequencing data. It processes individual reads at the species level, scoring them for pathogenic hosts, genetic mobility, and antibiotic resistance genes (ARGs), then delivers a per-read scored table, ranked risk list, and final species-level risk score. Run it via a simple bash CLI on FASTA inputs to get actionable microbial risk metrics without manual integration.

Why is it gaining traction?

It stands out by combining ARG annotation and plasmid mobility classification tailored for noisy nanopore data, outputting both per-read details and aggregated risks that alternatives often overlook. Developers appreciate the one-command workflow that handles threading and conda environments, saving hours on custom pipelines for QMRA assessment. The ranked lists let you pinpoint high-risk reads instantly, making it a practical hook for nanopore workflows.

Who should use this?

Bioinformatics analysts evaluating microbial risks in wastewater, hospital effluents, or environmental samples via nanopore sequencing. Microbiologists needing species-level QMRA on ARG-carrying pathogens with mobility potential. Teams integrating long-read metagenomics into public health surveillance pipelines.

Verdict

Worth a spin for nanopore QMRA niches despite 18 stars and 1.0% credibility score—docs are clear with examples, but expect tweaks for production as it's early-stage with external tool dependencies. Solid starter if your data fits.

(178 words)

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