BlobToolKit is described in our BlobToolKit paper:
BlobToolKit – Interactive quality assessment of genome assemblies Richard Challis, Edward Richards, Jeena Rajan, Guy Cochrane, Mark Blaxter G3: GENES, GENOMES, GENETICS April 1, 2020 vol. 10 no. 4 1361-1374; https://doi.org/10.1534/g3.119.400908
Similar to BlobTools v1, BlobTools2 is a command line tool designed to aid genome assembly QC and contaminant/cobiont detection and filtering. In addition to supporting interactive visualisation, a motivation for this reimplementation was to provide greater flexibility to include new types of information, such as BUSCO results and BLAST hit distributions.
BlobTools2 supports command-line filtering of datasets, assembly files and read files based on values or categories assigned to assembly contigs/scaffolds through the blobtools filter command. Interactive filters and selections made using the BlobToolKit Viewer can be reproduced on the command line and used to generate new, filtered datasets which retain all fields from the original dataset.
BlobTools2 is built around a file-based data structure, with data for each field contained in a separate JSON file within a directory (BlobDir) containing a single meta.json file with metadata for each field and the dataset as a whole. Additional fields can be added to an existing BlobDir using the blobtools add command, which parses an input to generate one or more additional JSON files and updates the dataset metadata. Fields are treated as generic datatypes, Variable (e.g. gc content, length and coverage), Category (e.g. taxonomic assignment based on BLAST hits) alongside Array and MultiArray datatypes to store information such as start, end, NCBI taxid and bitscore for a set of blast hits to a single sequence. Support for new analyses can be added to BlobTools2 by creating a new python module with an appropriate parse function.
To learn more about the development of the BlobTools approach, take a look at the papers by Laetsch DR and Blaxter ML, 2017 and Kumar et al., 2013.
As of version 3.0.0, BlobTools2 and a local version of the BlobToolKit Viewer can be installed with:
pip install blobtoolkit
The blobtools viewcommand requires firefox or a chromium-based browser to start the interactive viewer or to generate plots from the command line, these can be installed with:
conda install -c conda-forge firefox geckodriver
On MacOS, Xquartz is required to provide an X-windows environment. This can be installed without root sure privileges using:
brew install xquartz
Start the Viewer with an example dataset by using _ as the dataset name (visit the URL shown in the command output)
blobtools view --local _
Generate a table of contigs in a filtered BlobDir:
blobtools filter --param length--Min=1000000 --table table.tsv /path/to/BlobDir
Generate a snail plot from a hosted BlobDir:
blobtools view --view snail --host https://blobtoolkit.genomehubs.org mSciVul1_1
To use a chromium-based browser (e.g. Google Chrome) in place of firefox, add --driver chromium
blobtools view --view snail --host https://blobtoolkit.genomehubs.org --driver chromium mSciVul1_1
A set of Docker images are available from dockerhub:
genomehubs/blobtoolkit-blobtoolscontains the blobtools executable, which includes theblobtools viewcommand to run the Viewer and APIgenomehubs/blobtoolkitcontains the blobtools executable along with all pipline dependenciesgenomehubs/blobtoolkit-apicontains a standalone version of the BlobToolKit APIgenomehubs/blobtoolkit-viewercontains a standalone version of the BlobToolKit Viewer
The example commands above can all be run using the genomehubs/blobtoolkit-blobtools image. To access the API and Viewer running inside the container, it is necessary to map the default ports, 8000 and 8001:
docker run -it --rm --name blobtools -p 8000:8000 -p 8001:8001 genomehubs/blobtoolkit-blobtools:latest blobtools view --local _
If you find a problem or want to suggest a feature, please submit an issue.