RbcL Diat.barcode Reference Set for The RDP Classifier
This repository contains trained reference sets that can be used with the Ribosomal Database Project classifier (Wang et al., 2007) to taxonomically assign diatom rbcL cpDNA sequences. The latest releases can be downloaded from https://github.com/terrimporter/rbcLdiatomClassifier/releases
Note
This classifier is suitable for classifying diatom rbcL sequences to genus rank. To classify a broader diversity of taxa, there is a eukaryote rbcL reference set available at https://github.com/terrimporter/rbcLClassifier .
How to cite
If you use the RDP-formatted rbcL diatom reference set in a publication, please cite:
Maitland, V. C., Robinson, C. V., Porter, T. M., & Hajibabaei, M. (2020). Freshwater diatom biomonitoring through benthic kick-net metabarcoding. PLoS ONE, 15(11): e0242143.
You can also cite this repository directly:
Teresita M. Porter. (2020, June 5). RbcL Diat.barcode Reference Set For The RDP Classifier (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.4741478
Please also acknowledge the original diatom barcode database here:
Rimet, F., Gusev, E., Kahlert, M. et al. Diat.barcode, an open-access curated barcode library for diatoms. Sci Rep 9, 15116 (2019). https://doi.org/10.1038/s41598-019-51500-6
If you use this reference set with the RDP classifier please also cite:
Wang et al. (2007) Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73: 5261.
Quick Start
############ Install the RDP classifier if you need it
# The easiest way to install the RDP classifier v2.13 is using conda
conda install -c bioconda rdp_classifier
# Alternatively, you can install from SourceForge and run with java if you don't use conda
wget https://sourceforge.net/projects/rdp-classifier/files/rdp-classifier/rdp_classifier_2.13.zip
# decompress it
unzip rdp_classifier_2.13
# record path to classifier.jar ex. /path/to/rdp_classifier_2.13/dist/classifier.jar
############ Get the latest RDP-formatted rbcL diatom training set
wget https://github.com/terrimporter/rbcLdiatomClassifier/releases/download/v1.0/mydata_trained.tar.gz
# decompress it
tar -xvf mydata_trained.tar.gz
# record the path to the rRNAClassifier.properties file ex. /path/to/mydata_trained/rRNAClassifier.properties
############ Run the RDP Classifier
# If it was installed using conda, run it like this:
rdp_classifier -Xmx8g classify -t /path/to/mydata_trained/rRNAClassifier.properties -o rdp.output query.fasta
# Otherwise run it using java like this:
java -Xmx8g -jar /path/to/rdp_classifier_2.13/classifier.jar -t /path/to/mydata_trained/rRNAClassifie
Releases
rbcL Diatom Classifier v1.0
Created from the INRA R-Syst::diatom v7.1 dataset available at https://data.inra.fr/dataset.xhtml?persistentId=doi%3A10.15454%2FTOMBYZ (Rimet et al., 2016). This version contains 3504 reference sequences and 1432 taxa at all ranks.
Taxonomic assignment results should be filtered according to their bootstrap support values to reduce false positive assignments. Cutoffs are based on leave-one-sequence-out testing of non-singleton genera. Here we recommend MINIMUM bootstrap cutoffs according to query length and assignment rank. Assuming your query sequences are represented in the reference set, using the cutoffs presented in the first table below should ensure 99% accuracy. If you wish to cast a wider net, you can use the second table below for 95% accuracy.
Bootstrap support cutoffs, 99% accuracy:
| Rank | Full | 400 bp | 300 bp | 200 bp | 100 bp |
|---|---|---|---|---|---|
| Domain | 0 | 0 | 0 | 0 | 0 |
| Kingdom | 0 | 0 | 0 | 0 | 0 |
| Subkingdom | 0 | 0 | 0 | 0 | 0 |
| Phylum | 0 | 0 | 0 | 0 | 0 |
| Class | 0 | 40 | 40 | 50 | 70 |
| Order | 50 | 60 | 50 | 60 | 90 |
| Family | 70 | 60 | 60 | 60 | NA |
| Genus | NA | 95 | 80 | 90 | NA |
| Species | NA | NA | NA | NA | NA |
NA = No cutoff available will result in 99% correct assignments
Bootstrap support cutoffs, 95% accuracy:
| Rank | Full | 400 bp | 300 bp | 200 bp | 100 bp |
|---|---|---|---|---|---|
| Domain | 0 | 0 | 0 | 0 | 0 |
| Kingdom | 0 | 0 | 0 | 0 | 0 |
| Subkingdom | 0 | 0 | 0 | 0 | 0 |
| Phylum | 0 | 0 | 0 | 0 | 0 |
| Class | 0 | 0 | 0 | 0 | 10 |
| Order | 0 | 0 | 0 | 0 | 50 |
| Family | 0 | 0 | 0 | 20 | 50 |
| Genus | 0 | 30 | 20 | 40 | 60 |
| Species | NA | NA | NA | NA | NA |
NA = No cutoff available will result in 95% correct assignments
Bootstrap support cutoffs, 90% accuracy:
| Rank | Full | 400 bp | 300 bp | 200 bp | 100 bp |
|---|---|---|---|---|---|
| Domain | 0 | 0 | 0 | 0 | 0 |
| Kingdom | 0 | 0 | 0 | 0 | 0 |
| Subkingdom | 0 | 0 | 0 | 0 | 0 |
| Phylum | 0 | 0 | 0 | 0 | 0 |
| Class | 0 | 0 | 0 | 0 | 0 |
| Order | 0 | 0 | 0 | 0 | 20 |
| Family | 0 | 0 | 0 | 0 | 30 |
| Genus | 0 | 0 | 0 | 0 | 30 |
| Species | 95 | 90 | 90 | 90 | NA |
References
Maitland, V. C., Robinson, C. V., Porter, T. M., & Hajibabaei, M. (2020). Freshwater diatom biomonitoring through benthic kick-net metabarcoding. PLoS ONE, 15(11): e0242143.
Rimet, F., Gusev, E., Kahlert, M. et al. Diat.barcode, an open-access curated barcode library for diatoms. Sci Rep 9, 15116 (2019). https://doi.org/10.1038/s41598-019-51500-6
Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. Available from https://sourceforge.net/projects/rdp-classifier/
Acknowledgements
We acknowledge support from the Canadian federal Genomics Research & Development Initiative (GRDI), Metagenomics-Based Ecosystem Biomonitoring (Ecobiomics) project.
Last updated: May 6, 2021