SpeciateIT is an algorithm capable of fast, accurate individual sequence taxonomic classification. vSpeciateDB are models built from custom sets of reference sequences for classifying vaginal microbiota. Using a model guide tree and 7th order Markov Chain models to represent bacterial species trained on taxonomy-adjusted amplicon specific regions sequences, speciateIT requires little computational resources, and can quickly process large sequence datasets. SpeciateIT models for the vaginal microbiota include training sets for 16S rRNA gene V1-V3, V3-V4, and V4 regions sequences. "Cat Maps" are provided for each region to indicate which species are indistinguishable at the targeted variable regions.
Holm, Johanna (2024). speciateIT: vSpeciateDB Models. figshare. Dataset. https://doi.org/10.6084/m9.figshare.25254229.v1
This software runs on Linux.
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Clone repository.
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Download vSpeciateDB (https://doi.org/10.6084/m9.figshare.25254229.v1) and place into "vSpeciateDB_models".
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Unzip vSpeciateDB directories.
cd /path/to/speciateIT
unzip vSpeciateDB_models/vSpeciateIT_V1V3
unzip vSpeciateDB_models/vSpeciateIT_V3V4
unzip vSpeciateDB_models/vSpeciateIT_V4V4 -
From parent directory of cloned repository ("speciateIT" or location of "Makefile")
make all
This will compile all classification scripts.
NOTE1: May require "sudo" to access /usr/local/bin sudo make all
NOTE2: All Makefile's are configured for macosx. If you are on linux machine, change CC, CXX and LINK to gcc, g++ and g++, respectively. You may also need to modify LDFLAGS. -
Add classify to PATH:
export PATH="/path/to/speciateIT/bin/:$PATH"
- Classification:
classify -d < vSpeciateDB dir > -i < fasta file > -o < outDir >
Example: classify -d vSpeciateDB_models/vSpeciateIT_V3V4 -i MyProject_ASV.fasta -o MyProject
Output file name is always "MC_order7_results.txt". Format: "Sequence ID" "Classification" "posterior probability" "number of Decisions".
To force species-level annotations (i.e. ignore error thresholds):
classify -d < vSpeciateDB dir > -i < fasta file > -o < outDir > --skip-err-thld
- Make sample x taxon count table using classifications:
count_table.py -s < MC_order7_results.txt > -c < sample x ASV count table >
classify -d vSpeciateDB_models/vSpeciateIT_V3V4 -i test.fasta -o test
more test/MC_order7_results.txt
ASV1 Lactobacillus_iners 0.970448 50 ASV2 Lactobacillus_crispatus 0.974671 50 ASV3 Lactobacillus_mulieris 0.976813 50 ASV4 Ca_Lachnocurva_vaginae 0.969937 187 ASV5 Gardnerella_vaginalis 0.974747 27 ASV6 Lactobacillus_crispatus 0.965138 50 ASV7 Lactobacillus_iners 0.973368 50 ASV8 Fannyhessea_vaginae 0.984829 24 ASV9 Leptotrichia_shahii 0.916945 26 ASV10 Megasphaera_lornae 0.973400 27