Summary:

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.

vSpeciateDB:

Holm, Johanna (2024). speciateIT: vSpeciateDB Models. figshare. Dataset. https://doi.org/10.6084/m9.figshare.25254229.v1

To install:

This software runs on Unix.

1. Clone repository.

2. Download vSpeciateDB (https://doi.org/10.6084/m9.figshare.25254229.v1) and place into "vSpeciateDB_models".

3. Unzip vSpeciateDB directories.

   cd /path/to/speciateIT
   unzip vSpeciateDB_models/vSpeciateIT_V1V3
   unzip vSpeciateDB_models/vSpeciateIT_V3V4
   unzip vSpeciateDB_models/vSpeciateIT_V4V4

4. 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.

5. Add classify to PATH:

   export PATH="/path/to/speciateIT/bin/:$PATH"

To use:

1. 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

2. Make sample x taxon count table using classifications:
       count_table.py -s < MC_order7_results.txt > -c < sample x ASV count table >

To test:

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