THAPBI Phytophthora ITS1 Classifier Tool (PICT).
THAPBI Phytophthora ITS1 Classifier Tool (PICT)
THAPBI PICT is a sequence based diagnostic/profiling tool from the UK funded Tree Health and Plant Biosecurity Initiative (THAPBI) Phyto-Threats project, initially focused on identifying Phytophthora species present in Illumina sequenced environmental samples.
Phytophthora (from Greek meaning plant-destroyer) species are economically important plant pathogens, in both agriculture and forestry. ITS1 is short for Internal Transcribed Spacer one, which is a region of eukaryotes genomes between the 18S and 5.8S rRNA genes. This is commonly used for molecular barcoding, where sequencing this short region can identify species.
With appropriate primer settings and a custom database of full length markers, THAPBI PICT can be applied to other organisms and/or barcode marker sequences - not just Phytophthora ITS1. It requires overlapping paired-end Illumina reads which can be merged to cover the full amplicon marker. Longer markers or fragmented amplicons are not supported. Internally it works by tracking unique amplicon sequence variants (ASVs), using MD5 checksums as identifiers.
The worked examples include oomycetes, fungi, fish, bats, and plants, and cover markers in ITS1, ITS2, 12S, 16S, COI, and more. The main criteria has been mock communities with known species composition.
$ conda install thapbi-pict
Alternatively or on Windows, since the software is on the Python Package Index (PyPI), the following command will install it along with its Python dependencies:
$ pip install thapbi-pict
However, in this case you will still need to install various external command line tools. See INSTALL.rst for more details (especially for Windows), and if you want to modify the software read CONTRIBUTING.rst as well.
Once installed, you should be able to run the tool at the command line using:
This should automatically find the installed copy of the Python code. Use thapbi_pict -v to report the version, or thapbi_pict -h for help.
The documentation includes more detailed discussion of the sample datasets in the examples/ folder (which are based on published datasets).
If you use THAPBI PICT in your work, please cite our PeerJ paper, and give details of the version and any non-default settings used in your methods:
Cock et al. (2023) “THAPBI PICT - a fast, cautious, and accurate metabarcoding analysis pipeline” PeerJ 11:e15648 https://doi.org/10.7717/peerj.15648
You can also cite the software specifically via Zenodo which offers version specific DOIs as well as https://doi.org/10.5281/zenodo.4529395 which is for the latest version.
The initial work was supported from 2016 to 2019 under the Tree Health and Plant Biosecurity Initiative (THAPBI) Phyto-Threats project:
This research was supported by a grant funded jointly by the Biotechnology and Biological Sciences Research Council (BBSRC), Department for Environment, Food and Rural affairs (DEFRA), Economic and Social Research Council (ESRC), Forestry Commission, Natural Environment Research Council (NERC) and Scottish Government, under the Tree Health and Plant Biosecurity Initiative, grant number BB/N023463/1.
Work from 2020 to 2021 was supported in part under the Early detection of Phytophthora in EU and third country nurseries and traded plants (ID-PHYT) Euphresco project:
Funded by DEFRA as part of the Future Proofing Plant Health project in support of Euphresco ID-PHYT.
Work from 2022 to 2027 was partly funded by the Rural & Environment Science & Analytical Services (RESAS) Division of the Scottish Government.
THAPBI PICT continues earlier work including:
See the CHANGELOG.rst file.
See file CONTRIBUTING.rst for details of the development setup including Python style conventions, git pre-commit hook, continuous integration and test coverage, and release process.
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