slurm_taiyaki.sh

#!/bin/bash

# Copyright (C) 2020 Tobias Jakobi
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either self.version 3 of the License, or
# (at your option) any later self.version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# @Author: Tobias Jakobi <tjakobi>
# @Email:  tobias.jakobi@med.uni-heidelberg.de
# @Project: University Hospital Heidelberg, Section of Bioinformatics and Systems Cardiology

#SBATCH -n 1
#SBATCH -N 1
#SBATCH -c 40
#SBATCH --mem=50G
#SBATCH -J "Taiyaki"
#SBATCH --mail-type=END,FAIL,TIME_LIMIT_80
#SBATCH --mail-user=tobias.jakobi@med.uni-heidelberg.de

module unload cuda
module load taiyaki

# check if we have 4 arguments
if [ ! $# == 4 ]; then
  echo "Usage: $0 [Read directory] [target dir e.g. /tmp/] [pretrained model] [CUDA device number]"
  exit
fi

$reads = $1
$out = $2
$pretrained_model = $3
$fasta_file = $4
$cuda_dev = $5


# create the target directory
mkdir $out -pv


# Create Per-read Scaling Parameters
generate_per_read_params.py --jobs 40 $reads > $out/modbase.tsv

# Modify input fasta with changed bases

# Create Mapped Read File
# We use A -> Y replacements
prepare_mapped_reads.py  --jobs 40 --mod Y A 6mA reads $out/modbase.tsv $out/modbase.hdf5 $pretrained_model $fast_file 

# Train modified base model

# Having prepared the mapped read file, the train_flipflop.py script trains a flip-flop model
# Progress is displayed on the screen and written to a log file in output directory.
# Checkpoints are regularly saved and training can be restarted from a checkpoint by replacing the model description file with the checkpoint file on the command line.

# First we use the flipflop model from Taiyaki
train_flipflop.py --device 0 --mod_factor 0.01 --outdir $out/training taiyaki/models/mGru_cat_mod_flipflop.py modbase.hdf5

# Second round: starting from model we just trained
train_flipflop.py --device 0 --mod_factor 0.1 --outdir $out/training2 $out/training/model_final.checkpoint modbase.hdf5

# Basecalling
basecall.py --device $cuda_dev --modified_base_output $out/basecalls.hdf5 $reads $out/training2/model_final.checkpoint  > $out/basecalls.fa

# compress base calls
pigz -p 40 $out/basecalls.fa