The Microbiome Characterisation Utilities (MCU) is based on our novel methods for metagenome analysis. These methods are published in leading peer-reviewed journals and conference proceedings, and more methods are being added.
The MCU currently contains the following tools:
graphMap: Combining variation graph representation of gene sets with a locality-sensitive hashing indexing scheme, graphMap can perform accurate gene profiling of microbiome samples in minutes using just a laptop.
histoSketch: Using similarity-preserving sketches to represent streaming k-mer spectra, histoSketch can compress large microbiome samples to small sketches. These sketches can be indexed, searched or classified in real-time.
sketchPredict: Applying machine learning classifiers to sketches, sketchPredict is able to classify microbiome samples according to phenotype or other metadata.
geneFlow: Combining MCU components with the powerful Nextflow framework, geneFlow is able to identify microbiome samples of interest, perform gene and taxonomic profiling, and predict gene context.
We have designed the MCU to be translatable and customisable. We are able to offer bespoke MCU editions to users with specific requirements. In addition to command-line versions, we are able to offer graphical user interfaces.
These tools are available to companies under a commercial license. We can develop bespoke analytics pipelines for specific applications via collaborative R&D projects.
Analysis of metagenomic data including:
- Skin and mouth microbiome
- Gut / faecal microbiome
- Soil microbiome
- Gene profiling
- Reduced costs by using more efficient algorithms
- Data compression
- New applications enabled through real-time analysis
- Rowe WPM, Winn MD. Indexed variation graphs for efficient and accurate resistome profiling. Bioinformatics. 2018;34:3601–8. doi: 10.1093/bioinformatics/bty387
- Rowe WPM, Carrieri AP, Alcon-Giner C, Caim S, Shaw A, Sim K, et al. Streaming histogram sketching for rapid microbiome analytics. Microbiome. 2019;7:40. doi: 10.1186/s40168-019-0653-2
- Carrieri AP, Rowe WPM, Winn MD, Pyzer-Knapp EO. A Fast Machine Learning Workflow for Rapid Phenotype Prediction from Whole Shotgun Metagenomes. Innov Appl Artif Intell. 2019
To use these tools as part of an omics workflow – contact us.