pylshvec 0.1

LSHVec pre-trained models and its Python bindings

LSHVec pre-trained models and its Python bindings

Summary

This repository presents a few of pre-tained models with JLSHVec (which is a rewritten java version of LSHVec). See Remark for technical details.

Python codes and examples to uses these models are also provided.

Requirements

1. Python 3.6
2. cython>=0.28.5
3. Jnius >=1.1.0
4. java >=1.8

Install

build from source

git clone https://github.com/Lizhen0909/PyLSHvec.git && cd PyLSHvec && python setup.py install

or use pip

pip install pylshvec

or use docker

docker pull lizhen0909/pylshvec

How to use

Put things simply, just

from pylshvec import *

#here needs jlshvec jar file, download it first
set_lshvec_jar_path("/mnt/jlshvec-assembly-0.1.jar")

#since vector model is usually large, set a big java memory limit is preferred. 
add_java_options("-Xmx32G")

#here need model file and lsh function file, download them first
#use help(model) to see all the methods and constructor options 
model= LSHVec(model_file="/mnt/refdb_viruses_model_gs_k23_l3000_rand_model_299", 
              hash_file="/mnt/lsh_nt_NonEukaryota_k23_h25.crp")

reads = ['ACGTACGT.....', 'ACGTACGT.....', 'ACGTACGT.....', 'ACGTACGT.....', ....]

predicts = model.predict(reads)

For more complete examples please see the notebooks (see Download for minimum memory requirement):

example_use_virus_classfication_model.ipynb

example_use_bacteria_classfication_model.ipynb

example_use_vectors_in_bacteria_classfication_model.ipynb

example_use_Illumina_bacteria_classfication_model.ipynb

example_use_Pacbio_bacteria_classfication_model.ipynb

Download

JLSHVec jar file

The pre-trained models were trained with a rewritten LSHVec in java. The assembly jar file is needed to load the models.

Download jlshvec-assembly-0.1.jar

md5sum: aeb207b983b3adc27e14fd9c431e2130

Pre-trained models

Be Warned that like all the machine learning models, the model cannot preform better beyond the data. If your data is significant other than the pre-trained model data, training your own model is preferred.

Here are issues I can think of:

• Some NCBI taxonomy id may never be predicted since not all ids have train data.
• Data is not balanced. Some ids (e.g. a specified species) have much more data than others, which makes prediction may prefer to the rich-data ids.
• Strain (even some species) prediction is terrible. Don't expect it.

RefDB viruses classfication model

Trainned with 9.3k viruses assemblies of RefDB. Minimum Java memory: 16G.

• model file: refdb_viruses_model_gs_k23_l3000_rand_model_299 [size: 5.3G]

  md5sum 2502b284b336734300c2297d23d1d349

• hash function file: lsh_nt_NonEukaryota_k23_h25.crp

  md5sum 5eea8a98d224b7ff505091bd483ca75c

RefDB bacteria classfication model

Trainned with 42k bacteria assemblies of RefDB. Minimum Java memory: 32G.

• model file: refdb_bacteria_model_gs_k23_l3000_rand_model_214 [size: 11G]

  md5sum 402e9a286b71068999caa9766b2dbf8c

• hash function file: lsh_nt_NonEukaryota_k23_h25.crp

  md5sum 5eea8a98d224b7ff505091bd483ca75c

GenBank bacteria and viruses classfication model (Illumina Simulation)

Trainned with 560k assemblies from GenBank. Only one assembly was sampled for each species. Because viruses data is too samll compared to bateria, it rarely predicts any viruses. Just take it as a bateria model.

art_illumina was used to simulate the paired-end reads with length of 150, mean size of 270 and stddev of 27.

Minimum Java memory: 48G.

• model file: genbank_model_ill_k23_model_299 [size: 12G]

  md5sum d6b117a4c7ffe4f25e6c532a88bb3a47

• hash function file: lsh_CAMI2_illumina_k23_h25.crp

  md5sum 706633919e347f920ce6ab3277091efb

GenBank bacteria and viruses classfication model (Pacbio Simulation)

Trainned with 560k assemblies from GenBank. Only one assembly was sampled for each species. Because viruses data is too samll compared to bateria, it rarely predicts any viruses. Just take it as a bateria model.

pbsim was used to simulate the pacbio reads with Continuous Long Read (CLR) profile, mean size of 3000 and stddev of 1000.

Minimum Java memory: 16G.

• model file: genbank_model_pb_k9_model_299 [size: 121M]

  md5sum 351275531493a4866be4afcd9df3932c

• hash function file: lsh_CAMI2_pacbio_k9_h25.crp

  md5sum df7ee38cf8b58d5f8034bb9b266e3334

Sample data

• ActinoMock Nanopore Sample [size: 500M].

  The data is used in example notebook example_use_vectors_in_bacteria_classfication_model.ipynb.

  Download from FSU    Download from Amazon Drive

  md5sum: b7f3e55438fdc05920aee693a98ded2e

Remark

What is JLSHVec ? Why JLSHVec instead of LSHVec?

JLSHVec is a rewritten version of LSHVec in Java language.

When we use LSHVec with big dataset (e.g. GenBank, RefDB), we found that LSHVec is hard to process such a big data size.

The reason is that LSHVec which inherits from FastText requires the input is text format separated by white space and then loads all the text in memory. This is acceptable for natural languages since the data size is at most tens GBs.

However in LSHVec k-mers are used instead of words. Suppose we want to train a k-mer embedding of simulated Illumina reads with RefDB bacteria assemblies (about 500G genetic bits). The number of kmers is about D*n, where D is the assembly data size and n is coverage. In our case, assuming n=10 and k=23, the number of kmers is 5T and requires a disk space of 125TB and tens TB of memory, which is unrealistic even for most HPC systems.

How were JLSHVec pre-trained models trained ?

First we prepared a RockDB for the reference sequences (e.g. all RefDB bacteria assemblies).

Then we have several nodes to train the model: one node (train node) trains the vectors and others (hash nodes) generate and hash kmers. The nodes pass protocol-buf message with a Redis server.

Hash node randomly reads reference sequences from RockDB, simulates (e.g. simulations Illumina, Pacbio, Gold Standard) reads, generates kmers and hashes them, then feeds the hashed-kmer-sequences to a Redis queue.

Train node reads from the Redis queue and does jobs of embedding or classification training. Our training code supports hierarchical softmax using NCBI taxonomy tree, which is essential for multi-label(an instance can have a label for each rank) and multi-class(an instance can only have one label for a rank) mixture classification model.

Citation

Please cite:

A Vector Representation of DNA Sequences Using Locality Sensitive Hashing

License