SeqVec/embedding

Authors: Michael Heinzinger, Ahmed Elnaggar

License: MIT

Contributed by: Michael Heinzinger, Ahmed Elnaggar

Type: None

Postprocessing: None

Trained on: UniRef50

Embeddings from Language Models (ELMo) trained on protein sequences. Allows to convert protein sequence to a vector representation.

Create a new conda environment with all dependencies installed

kipoi env create SeqVec/embedding
source activate kipoi-SeqVec__embedding

Test the model

kipoi test SeqVec/embedding --source=kipoi

Make a prediction

kipoi get-example SeqVec/embedding -o example
kipoi predict SeqVec/embedding \
  --dataloader_args='{"fasta_file": "example/fasta_file"}' \
  -o '/tmp/SeqVec|embedding.example_pred.tsv'
# check the results
head '/tmp/SeqVec|embedding.example_pred.tsv'

Create a new conda environment with all dependencies installed

kipoi env create SeqVec/embedding
source activate kipoi-SeqVec__embedding

Get the model

import kipoi
model = kipoi.get_model('SeqVec/embedding')

Make a prediction for example files

pred = model.pipeline.predict_example(batch_size=4)

Use dataloader and model separately

# Download example dataloader kwargs
dl_kwargs = model.default_dataloader.download_example('example')
# Get the dataloader and instantiate it
dl = model.default_dataloader(**dl_kwargs)
# get a batch iterator
batch_iterator = dl.batch_iter(batch_size=4)
for batch in batch_iterator:
    # predict for a batch
    batch_pred = model.predict_on_batch(batch['inputs'])

Make predictions for custom files directly

pred = model.pipeline.predict(dl_kwargs, batch_size=4)

Get the model

library(reticulate)
kipoi <- import('kipoi')
model <- kipoi$get_model('SeqVec/embedding')

Make a prediction for example files

predictions <- model$pipeline$predict_example()

Use dataloader and model separately

# Download example dataloader kwargs
dl_kwargs <- model$default_dataloader$download_example('example')
# Get the dataloader
dl <- model$default_dataloader(dl_kwargs)
# get a batch iterator
it <- dl$batch_iter(batch_size=4)
# predict for a batch
batch <- iter_next(it)
model$predict_on_batch(batch$inputs)

Make predictions for custom files directly

pred <- model$pipeline$predict(dl_kwargs, batch_size=4)

Get the docker image

docker pull kipoi/kipoi-docker:seqvec-slim

Get the full sized docker image

docker pull kipoi/kipoi-docker:seqvec

Get the activated conda environment inside the container

docker run -it kipoi/kipoi-docker:seqvec-slim

Test the model

docker run kipoi/kipoi-docker:seqvec-slim kipoi test SeqVec/embedding --source=kipoi

Make prediction for custom files directly

# Create an example directory containing the data
mkdir -p $PWD/kipoi-example 
# You can replace $PWD/kipoi-example with a different absolute path containing the data 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:seqvec-slim \
kipoi get-example SeqVec/embedding -o /app/example 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:seqvec-slim \
kipoi predict SeqVec/embedding \
--dataloader_args='{'fasta_file': '/app/example/fasta_file'}' \
-o '/app/SeqVec_embedding.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/SeqVec_embedding.example_pred.tsv

Install apptainer

https://apptainer.org/docs/user/main/quick_start.html#quick-installation-steps

Make prediction for custom files directly

kipoi get-example SeqVec/embedding -o example
kipoi predict SeqVec/embedding \
--dataloader_args='{"fasta_file": "example/fasta_file"}' \
-o 'SeqVec_embedding.example_pred.tsv' \
--singularity 
# check the results
head SeqVec_embedding.example_pred.tsv

Schema

Inputs

Single numpy array

Name: None

Shape: (1,)

Doc: Path to file containing protein sequences in fasta format. Sequences can have different length.

Targets

List of numpy arrays

Name: seq

Shape: (1024, None)

Doc: Embedding for a protein sequence. Each amino acid in your protein of length L is represented by a vector of length 1024.

Dataloader

Defined as: .

Doc: Data-loader returning protein sequence as required by ELMo

Authors: Michael Heinzinger

Type: Dataset

License: MIT

Arguments

fasta_file : fasta file containing multiple protein sequence(s)

split_char (optional): charcter used for separating header of fasta files (together with id_field used to extract protein identifier)

id_field (optional): index for extracting protein identifier from fasta header after splitting after split_char

Model dependencies

conda:

python=3.6
conda-forge::allennlp=0.7.2
pip=9.0.3
scikit-learn==0.22.2.post1
overrides=3.1.0

pip:

Dataloader dependencies

conda:

python=3.6
conda-forge::allennlp=0.7.2

pip: