Robust comparative analysis and contamination removal for metagenomics
Project description
Robust comparative analysis and contamination removal for metagenomics
With Recentrifuge, researchers can interactively explore what organisms are in their samples and at which level of confidence, thus enabling a robust comparative analysis of multiple samples in any metagenomic study.
- Removes diverse contaminants, including crossovers, using a novel robust contamination removal algorithm.
- Provides a confidence level for every result, since the calculated score propagates to all the downstream analysis and comparisons.
- Unveils the generalities and specificities in the metagenomic samples, thanks to a new comparative analysis engine.
Recentrifuge's novel approach combines robust statistics, arithmetic of scored taxonomic trees, and parallel computational algorithms.
Recentrifuge is especially useful in the case of low microbial biomass metagenomic studies and when a more reliable detection of minority organisms is needed, like in clinical, environmental and forensic analysis. Among the different confidence levels available in Recentrifuge, it implements several very useful for variable length reads such as those generated by nanopore sequencers.
To play with an example of webpage generated by Recentrifuge, click on the screenshot:
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for recentrifuge-0.28.3-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | fed27c6b3fd3e5f6497a5a88137f6cf21b6102d18bca9acc1c04040124409e64 |
|
| MD5 | f089894984116374f05673a6aef79b15 |
|
| BLAKE2b-256 | eccacf5b810aa631e0629309608b3b0e4a0063d63291fc185110756624a0c1e3 |
