library("rRDP")
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set.seed(1234)rRDP requires the Bioconductor package Biostrings and R to be configured with Java.
if (!require("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("Biostrings")Install rRDP and the database used by the default RDP classifier.
RDP uses Java and you need a working installation of the
rJava package. You need to have a Java JDK installed. On
Linux, you can install Open JDK and run in your shell
R CMD javareconf to configure R for using Jave. On Windows,
you can install the latest version of the JDK from https://www.oracle.com/java/technologies/downloads/ and
set the JAVA_HOME environment variable in R using (make
sure to use the correct location). An example would look like this:
Note the double backslashes (i.e. escaped slashes) used in the path.
Details can be found at https://www.rforge.net/rJava/index.html. To configure R for Java,
citation("rRDP")
To cite package 'rRDP' in publications use:
Hahsler M, Nagar A (2020). "rRDP: Interface to the RDP Classifier."
Bioconductor version: Release (3.19). doi:10.18129/B9.bioc.rRDP
<https://doi.org/10.18129/B9.bioc.rRDP>, R package version 1.23.3.
A BibTeX entry for LaTeX users is
@Misc{,
title = {{rRDP:} Interface to the {RDP} Classifier},
author = {Michael Hahsler and Annurag Nagar},
year = {2020},
doi = {10.18129/B9.bioc.rRDP},
note = {R package version 1.23.3},
howpublished = {Bioconductor version: Release (3.19)},
}The RDP classifier was developed by the Ribosomal Database Project (Cole et al. 2003) which provides various tools and services to the scientific community for data related to 16S rRNA sequences. The classifier uses a Naive Bayesian approach to quickly and accurately classify sequences. The classifier uses 8-mer counts as features (Wang et al. 2007).
RDP is trained with a 16S rRNA training set. The companion data
package rRDPData currently ships with trained models for
the RDP Classifier 2.14 released in August 2023 which contains the
bacterial and archaeal taxonomy training set No. 19 (Wang and Cole 2024).
For the following example we load some test sequences shipped with the package.
seq <- readRNAStringSet(system.file("examples/RNA_example.fasta",
package = "rRDP"
))
seq
#> RNAStringSet object of length 5:
#> width seq names
#> [1] 1481 AGAGUUUGAUCCUGGCUCAGAAC...GGUGAAGUCGUAACAAGGUAACC 1675 AB015560.1 d...
#> [2] 1404 GCUGGCGGCAGGCCUAACACAUG...CACGGUAAGGUCAGCGACUGGGG 4399 D14432.1 Rho...
#> [3] 1426 GGAAUGCUNAACACAUGCAAGUC...AACAAGGUAGCCGUAGGGGAACC 4403 X72908.1 Ros...
#> [4] 1362 GCUGGCGGAAUGCUUAACACAUG...UACCUUAGGUGUCUAGGCUAACC 4404 AF173825.1 A...
#> [5] 1458 AGAGUUUGAUUAUGGCUCAGAGC...UGAAGUCGUAACAAGGUAACCGU 4411 Y07647.2 Dre...Note that the name contains the annotation from the FASTA file. In this case the annotation contains the actual classification information and is encoded in Greengenes format. For convenience, we replace the annotation with just the sequence id.
annotation <- names(seq)
names(seq) <- sapply(strsplit(names(seq), " "), "[", 1)
seq
#> RNAStringSet object of length 5:
#> width seq names
#> [1] 1481 AGAGUUUGAUCCUGGCUCAGAAC...GGUGAAGUCGUAACAAGGUAACC 1675
#> [2] 1404 GCUGGCGGCAGGCCUAACACAUG...CACGGUAAGGUCAGCGACUGGGG 4399
#> [3] 1426 GGAAUGCUNAACACAUGCAAGUC...AACAAGGUAGCCGUAGGGGAACC 4403
#> [4] 1362 GCUGGCGGAAUGCUUAACACAUG...UACCUUAGGUGUCUAGGCUAACC 4404
#> [5] 1458 AGAGUUUGAUUAUGGCUCAGAGC...UGAAGUCGUAACAAGGUAACCGU 4411Next, we apply RDP with the default training set. Note that the data
package rRDPDate needs to be installed!
pred <- predict(rdp(), seq)
pred
#> domain phylum class order
#> 1675 Bacteria Nitrospinota Nitrospinia Nitrospinales
#> 4399 Bacteria Pseudomonadota Alphaproteobacteria Rhodospirillales
#> 4403 Bacteria Pseudomonadota Alphaproteobacteria Rhodospirillales
#> 4404 Bacteria Pseudomonadota Alphaproteobacteria Rhodospirillales
#> 4411 Bacteria Pseudomonadota Alphaproteobacteria Rhodospirillales
#> family genus
#> 1675 Nitrospinaceae Nitrospina
#> 4399 Rhodovibrionaceae Rhodovibrio
#> 4403 Acetobacteraceae Roseococcus
#> 4404 Acetobacteraceae Sediminicoccus
#> 4411 Acetobacteraceae <NA>The prediction confidence is supplied as the attribute
"confidence".
attr(pred, "confidence")
#> domain phylum class order family genus
#> 1675 1 1 1 1 1 1.00
#> 4399 1 1 1 1 1 1.00
#> 4403 1 1 1 1 1 1.00
#> 4404 1 1 1 1 1 1.00
#> 4411 1 1 1 1 1 0.29To evaluate the classification accuracy we can compare the known
classification with the predictions. The known classification was stored
in the FASTA file and encoded in Greengenes format. We can decode the
annotation using decode_Greengenes().
actual <- decode_Greengenes(annotation)
actual
#> Kingdom Phylum Class Order
#> 1 Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales
#> 2 Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales
#> 3 Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales
#> 4 Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales
#> 5 Bacteria Proteobacteria Alphaproteobacteria Rhodospirillales
#> Family Genus Species Otu
#> 1 Nitrospinaceae Nitrospina unknown 3187
#> 2 Rhodospirillaceae Rhodovibrio Rhodovibrio salinarum 2816
#> 3 Acetobacteraceae Roseococcus unknown 2785
#> 4 Acetobacteraceae Roseococcus unknown 2785
#> 5 Acetobacteraceae; Unclassified unknown unknown 2752
#> Org_name Id
#> 1 AB015560.1_deep-sea_sediment_clone_BD4-10 1675
#> 2 D14432.1_Rhodovibrio_salinarum_str._NCIMB2243 4399
#> 3 X72908.1_Roseococcus_thiosulfatophilus_str._RB-3_Yurkov_strain_Drews 4403
#> 4 AF173825.1_Antarctic_clone_LB3-94 4404
#> 5 Y07647.2_Drentse_grassland_soil_clone_vii 4411Now we can compare the prediction with the actual classification by creating a confusion table and calculating the classification accuracy. Here we do this at the Genus level.
confusionTable(actual, pred, rank = "genus")
#> predicted
#> actual Nitrospina Rhodovibrio Roseococcus Sediminicoccus unknown <NA>
#> Nitrospina 1 0 0 0 0 0
#> Rhodovibrio 0 1 0 0 0 0
#> Roseococcus 0 0 1 1 0 0
#> Sediminicoccus 0 0 0 0 0 0
#> unknown 0 0 0 0 0 1
#> <NA> 0 0 0 0 0 0
accuracy(actual, pred, rank = "genus")
#> [1] 0.6RDP can be trained using trainRDP(). We use an example
of training data that is shipped with the package.
trainingSequences <- readDNAStringSet(
system.file("examples/trainingSequences.fasta", package = "rRDP")
)
trainingSequences
#> DNAStringSet object of length 20:
#> width seq names
#> [1] 1384 TAGTGGCGGACGGGTGAGTAACG...GAAGTTCGAATTTGGGTCAAGT 13652 Root;Bacter...
#> [2] 1386 ATCTCACCTCTCAATAGCGGCGG...GCCGTCGAAGGTGGGGTTGGTG 13655 Root;Bacter...
#> [3] 1440 ATCTCACCTCTCAATAGCGGCGG...GTGCGGCTGGATCACCTCCTTA 13661 Root;Bacter...
#> [4] 1421 AATAGCGGCGGACGGGTGAGTAA...GCCGTATCGGAAGGTGCGGCTG 13671 Root;Bacter...
#> [5] 1439 ATCTCACCTCTCAATANCGGCGG...CGGAAGGTGCGGCTGGATCACC 13677 Root;Bacter...
#> ... ... ...
#> [16] 1478 TGGCTCAGGACGAACGCTGGCGG...GTAGCCGTATCGGAAGGTGCGG 13763 Root;Bacter...
#> [17] 1507 CCTGGCTCAGGACGAACGCTGGC...AGCCGTATCGGAAGGTGCGGCT 13781 Root;Bacter...
#> [18] 1481 TGGAGAGTTTGATCCTGGCTCAG...NAACCGCAAGGATATAGCCGTC 13797 Root;Bacter...
#> [19] 1463 CGGCGTGCTTGGACCCACCCAAA...AGGAGGGTCCTAAGGTGGGGGC 13799 Root;Bacter...
#> [20] 1389 CGAGTGGCAAACGGGTGAGTAAC...AAACCGCAAGGATGCAGCCGTC 13800 Root;Bacter...Note that the training data needs to have names in a specific RDP format:
"<ID> <Kingdom>;<Phylum>;<Class>;<Order>;<Family>;<Genus>"
In the following we show the name for the first sequence. We use here
sprintf to display only the first 65~characters so the it
fits into a single line.
sprintf(names(trainingSequences[1]), fmt = "%.65s...")
#> [1] "13652 Root;Bacteria;Firmicutes;Clostridia;Clostridiales;Peptococc..."Now, we can train a the classifier. The model is stored in a
directory specified by the parameter dir.
customRDP <- trainRDP(trainingSequences, dir = "myRDP")
customRDP
#> RDPClassifier
#> Location: /tmp/RtmpqzJc2S/Rbuildcce5477f7a8/rRDP/vignettes/myRDPtestSequences <- readDNAStringSet(
system.file("examples/testSequences.fasta", package = "rRDP")
)
pred <- predict(customRDP, testSequences)
pred
#> domain Phylum Class Order
#> 13811 Firmicutes Firmicutes Clostridia Clostridiales
#> 13813 Firmicutes Firmicutes Clostridia Clostridiales
#> 13678 Firmicutes Firmicutes Clostridia Clostridiales
#> 13755 Firmicutes Firmicutes Clostridia Clostridiales
#> 13661 Firmicutes Firmicutes Clostridia Clostridiales
#> Family Genus
#> 13811 Veillonellaceae Selenomonas
#> 13813 Veillonellaceae Selenomonas
#> 13678 Peptococcaceae Desulfotomaculum
#> 13755 Thermoanaerobacterales Family III. Incertae Sedis Thermoanaerobacterium
#> 13661 Peptococcaceae DesulfotomaculumSince the custom classifier is stored on disc it can be recalled
anytime using rdp().
To permanently remove the classifier use removeRDP().
This will delete the directory containing the classifier files.
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
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#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] rRDP_1.37.3 Biostrings_2.73.2 GenomeInfoDb_1.41.2
#> [4] XVector_0.45.0 IRanges_2.39.2 S4Vectors_0.43.2
#> [7] BiocGenerics_0.51.3 rmarkdown_2.28
#>
#> loaded via a namespace (and not attached):
#> [1] crayon_1.5.3 httr_1.4.7 cli_3.6.3
#> [4] knitr_1.48 rlang_1.1.4 xfun_0.48
#> [7] UCSC.utils_1.1.0 rJava_1.0-11 jsonlite_1.8.9
#> [10] buildtools_1.0.0 htmltools_0.5.8.1 maketools_1.3.1
#> [13] sys_3.4.3 sass_0.4.9 evaluate_1.0.1
#> [16] jquerylib_0.1.4 fastmap_1.2.0 yaml_2.3.10
#> [19] lifecycle_1.0.4 compiler_4.4.1 digest_0.6.37
#> [22] R6_2.5.1 GenomeInfoDbData_1.2.13 bslib_0.8.0
#> [25] tools_4.4.1 zlibbioc_1.51.2 cachem_1.1.0This research is supported by research grant no. R21HG005912 from the National Human Genome Research Institute (NHGRI / NIH).