MetaClassifier
Overview
MetaClassifier is an integrated pipeline for identifying the floral composition of honey using DNA metabarcoding to determine the plants that honey bees visit. MetaClassifier utilizes a database of marker sequences and their corresponding taxonomy lineage information to classify high-throughput metabarcoding sample sequencing reads data into taxonomic groups and quantify taxon abundance. MetaClassifier can also be employed in other studies that utilize barcoding, metabarcoding, and metagenomics techniques to characterize richness, abundance, relatedness, and interactions in ecological communities.
In addition to this README file, you can consult the MetaClassifier manual for more detailed information.
Installation
MetaClassifier requires dependencies and external tools that need to be installed and available on the environment the pipeline can be used. Not a requirement if insatalling using Bioconda.
Dependecies
External tools
- PEAR for merging overlapping paired-end (PE) reads
- seqtk for converting FASTQ to FASTA sequence format
- VSEARCH for searching searching high-throughtput sequence read data against marker database
Python package installation
MetaClassifier is available through pypi. To install, type:
pip install metaclassifier
Repository from GitHub
Either "git clone https://github.com/ewafula/MetaClassifier.git" or download and "unzip MetaClassifier-main.zip"
cd MetaClassifier/
python setup.py install
Bioconda package
This requires a working Conda installation.
conda install -c bioconda metaclassifier
We recommend installing MetaClassifier in a new separate environment from the base for all dependencies to be properly resolved by conda. To install, type:
conda create -n "metaclassifier" -c bioconda metaclassifier=1.0.1
Marker reference databases
MetaCurator reference databases with taxonomy lineage information reformated to work with MetaClassifier. Detailed step by step tutorial workflow for creating reference marker database is descrribe on the GitHub MetaCurator database repository
Basic usage
metaclassifier [options] <SAMPLE_FILE> <DB_DIR> <CONFIG_FILE>
Please consult the MetaClassifier manual for a detailed description and usage of all options.
Citation
If you use MetaClassifier please cite the following paper the describes the methodology:
Characterizing the floral resources of a North American metropolis using a honey bee foraging assay.
Douglas B. Sponsler, Don Shump, Rodney T. Richardson, Christina M. Grozinger.
Ecosphere 11, no. 4 (2020): e03102.
DOI: https://doi.org/10.1002/ecs2.3102
License
MetaClassifier is distributed under the GNU GPL v3.0 For more information, see license.
Metadata
Release files for metaclassifier 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| metaclassifier-1.0.1.tar.gz | 9.9 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| metaclassifier-1.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 34.0 kB
Release files / metaclassifier-1.0.1.tar.gz
| Download URL | metaclassifier-1.0.1.tar.gz |
|---|---|
| Size | 9.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
7b2ee5437bb4e234b83d568d4f671ab83b635370be155df4d3e916cdd5b473ef
|
|
BLAKE2b-256 checksum How to use checksums |
039b2a30a5f4837237bb6693eb56cb10f42b581e9aaea679d017e27efd1fd1e5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.58.0 CPython/3.8.5
|
Release files / metaclassifier-1.0.1-py3-none-any.whl
| Download URL | metaclassifier-1.0.1-py3-none-any.whl |
|---|---|
| Size | 24.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c70d70266d1c97d9e8d0055e3e84456ade999e5ae8d31b27ba8844c631784f01
|
|
BLAKE2b-256 checksum How to use checksums |
ef3a8218a09c9d4cbbdb689ca2711e71e7b74a08ab1071709f43843b0473bd3f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.58.0 CPython/3.8.5
|
Security for metaclassifier 1.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.