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