MMSplice/mtsplice

Authors: Jun Cheng

License: MIT

Contributed by: Jun Cheng

Cite as:

Trained on: MPRA (Rosenberg 2015), GENCODE annotation 24, Vex-seq (Adamson et al., 2018). Chromosome 1 to chromosome 8 were provided as training data. The remaining chromosomes 9 to 22 and chromosome X were held out.

Source files

Predict splicing variant effect (delta logit PSI) from VCF

Tissue specific prediction of $\Delta logit(\Psi)$. Returns one prediction per variant-exon pair for each tissue.

Create a new conda environment with all dependencies installed

kipoi env create MMSplice/mtsplice
source activate kipoi-MMSplice__mtsplice

Test the model

kipoi test MMSplice/mtsplice --source=kipoi

Make a prediction

kipoi get-example MMSplice/mtsplice -o example
kipoi predict MMSplice/mtsplice \
  --dataloader_args='{"gtf": "example/gtf", "fasta_file": "example/fasta_file", "vcf_file": "example/vcf_file", "exon_cut_l": 0, "exon_cut_r": 0, "acceptor_intron_cut": 6, "donor_intron_cut": 6, "acceptor_intron_len": 50, "acceptor_exon_len": 3, "donor_exon_len": 5, "donor_intron_len": 13}' \
  -o '/tmp/MMSplice|mtsplice.example_pred.tsv'
# check the results
head '/tmp/MMSplice|mtsplice.example_pred.tsv'

Create a new conda environment with all dependencies installed

kipoi env create MMSplice/mtsplice
source activate kipoi-MMSplice__mtsplice

Get the model

import kipoi
model = kipoi.get_model('MMSplice/mtsplice')

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('MMSplice/mtsplice')

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:mmsplice-mtsplice-slim

Get the full sized docker image

docker pull kipoi/kipoi-docker:mmsplice-mtsplice

Get the activated conda environment inside the container

docker run -it kipoi/kipoi-docker:mmsplice-mtsplice-slim

Test the model

docker run kipoi/kipoi-docker:mmsplice-mtsplice-slim kipoi test MMSplice/mtsplice --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:mmsplice-mtsplice-slim \
kipoi get-example MMSplice/mtsplice -o /app/example 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:mmsplice-mtsplice-slim \
kipoi predict MMSplice/mtsplice \
--dataloader_args='{'gtf': '/app/example/gtf', 'fasta_file': '/app/example/fasta_file', 'vcf_file': '/app/example/vcf_file', 'exon_cut_l': 0, 'exon_cut_r': 0, 'acceptor_intron_cut': 6, 'donor_intron_cut': 6, 'acceptor_intron_len': 50, 'acceptor_exon_len': 3, 'donor_exon_len': 5, 'donor_intron_len': 13}' \
-o '/app/MMSplice_mtsplice.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/MMSplice_mtsplice.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 MMSplice/mtsplice -o example
kipoi predict MMSplice/mtsplice \
--dataloader_args='{"gtf": "example/gtf", "fasta_file": "example/fasta_file", "vcf_file": "example/vcf_file", "exon_cut_l": 0, "exon_cut_r": 0, "acceptor_intron_cut": 6, "donor_intron_cut": 6, "acceptor_intron_len": 50, "acceptor_exon_len": 3, "donor_exon_len": 5, "donor_intron_len": 13}' \
-o 'MMSplice_mtsplice.example_pred.tsv' \
--singularity 
# check the results
head MMSplice_mtsplice.example_pred.tsv

Schema

Inputs

Dictionary of numpy arrays

Name: seq/acceptor_intron

Shape: (None, 4)

Doc: alternative sequence of acceptor intron

Name: seq/acceptor

Shape: (None, 4)

Doc: alternative sequence of acceptor

Name: seq/exon

Shape: (None, 4)

Doc: alternative sequence of exon

Name: seq/donor

Shape: (None, 4)

Doc: alternative sequence of donor

Name: seq/donor_intron

Shape: (None, 4)

Doc: alternative sequence of donor intron

Name: mut_seq/acceptor_intron

Shape: (None, 4)

Doc: alternative sequence of acceptor intron

Name: mut_seq/acceptor

Shape: (None, 4)

Doc: alternative sequence of acceptor

Name: mut_seq/exon

Shape: (None, 4)

Doc: alternative sequence of exon

Name: mut_seq/donor

Shape: (None, 4)

Doc: alternative sequence of donor

Name: mut_seq/donor_intron

Shape: (None, 4)

Doc: alternative sequence of donor intron

Targets

Single numpy array

Name: None

Shape: (56,)

Doc: Predicted delta logit(psi) for each tissue

Dataloader

Defined as: .

Doc: This model first predicts the effect of variants using 5 sub-modules (acceptor intron module, acceptor module, exon module, donor module, donor intron module), and then integrates those predictions using linear regression. The model has been trained to predict delta PSI subject to variants.

Authors: Jun Cheng

Type: SampleIterator

License: MIT

Arguments

gtf : path to the GTF file required by the models (Ensemble)

fasta_file : reference genome fasta file

vcf_file : Path to the input vcf file

split_seq (optional): Whether split the sequence in dataloader

encode (optional): If split the sequence, whether one hot encoding

exon_cut_l (optional): when extract exon feature, how many base pair to cut out at the begining of an exon

exon_cut_r (optional): when extract exon feature, how many base pair to cut out at the end of an exon

acceptor_intron_cut (optional): how many bp to cut out at the end of acceptor intron that consider as acceptor site

donor_intron_cut (optional): how many bp to cut out at the end of donor intron that consider as donor site

acceptor_intron_len (optional): what length in acceptor intron to consider for acceptor site model

acceptor_exon_len (optional): what length in acceptor exon to consider for acceptor site model

donor_exon_len (optional): what length in donor exon to consider for donor site model

donor_intron_len (optional): what length in donor intron to consider for donor site model

Model dependencies

conda:

python=3.7
pip=21.0.1

pip:

h5py==2.10.0
mmsplice==2.0.0
protobuf==3.20

Dataloader dependencies

conda:

bioconda::cyvcf2=0.11.5
bioconda::pyranges=0.0.71
bioconda::pysam=0.15.3
python=3.7

pip:

mmsplice==2.0.0