SAINT

SAINT is a weakly-supervised learning method where the embedding function is learned automatically from the easily-acquired data.Compared to existing deep learning-based alignment-free method, SAINT doesn’t require tedious labors to collect accurate alignment distances to train.SAINT is more computationally fast and memory efficient because sequence data are operated in a compressed embedding space which is much faster to retrieval and succinct to store.

Compared to existing alignment-free sequence comparison methods,SAINT offers following advantages:

1. SAINTis more computationally fast and memory efficient because sequence data are operated in a compressed embedding space which is much faster to retrieval and succinct to store.

2. SAINTis a weakly-supervised learning method where the embedding function is learned automatically from the easily-acquired data. Compared to existing deep learning-based alignment-free method, SAINT doesn’t require tedious labors to collect accurate alignment distances to train.

Version Release Notes

• Version 1.0

1. This is the first version of SAINT pipeline.

2. An demo of SAINT running is given here.

Package installation and configuration

• Pre-install running environment

1. Unix or Linux operating system.

2. CPU is enough for calculation.

3. Python 3 or above.

4. Packages like sys, optparse, os, random, numpy, pandas, collections, keras and sklearn need to be prepared.

• Detailed steps

1. Download the source code to your directory, e.g: ’/home/user/SAINT’.

2. Enter your specified directory:

     $ cd /home/user/SAINT 
   

3. Extract the zip file:

     $ unzip ./resource/kmer.zip
   

4. If your operating system has multiple Python version, please be sure your Python version at least 3 or above.

The demo of SAINT

The dataset was download from NCBI. For the 232 bacteria genomes, Saint uses KMC tool to convert fasta file into kmer frequency file here.

Run SAINT

1. Usage of SAINT

• The main running command are triplet_model.py and taxonomy_localization.py with following options:

  -h, --help: show this help message and exit

  -i, --inputcsv: the taxomony of the input data

  -d, --kmer_frequency_dir: the dir of kmer frequency.

  -t, --test_name: the list of test name.

  -k, --kofKTuple: the value k of KTuple

  -e, --epochNum: the number of epoch.

  -o, --output: output dir.

2. Run SAINT to get model.

   Create a new folder to put model file

     $ mkdir output
   

   Run triplet_model.py

     $ python code/triplet_model.py -i resource/data.csv -d resource/kmer/ -t resource/test_name.txt -k 6 -e 30 -o output/
   

3. Predict taxonomy of unknown species and Calculate the performance of SAINT results.`

   Run taxonomy_localization.py

     $ python code/taxonomy_localization.py -i resource/data.csv -d resource/kmer/ -t resource/test_name.txt  -o output/
   

   The output are ./output/predict_taxonomy.txt and ./output/Accuracy.txt.