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LRBinner

Description

lrbinner website

LRBinner is a long-read binning tool that overcomes several limitations of our previous work MetaBCC-LR (ISMB 2020). The tool uses variational auto-encoders to bin error-prone long reads using coverage and composition.

Environment Modules

Run module spider lrbinner to find out what environment modules are available for this application.

Environment Variables

  • HPC_LRBINNER_DIR - installation directory
  • HPC_LRBINNER_BIN - executable directory

Job Script Examples

Below is a job script used for testing application installation:

#!/bin/bash
#SBATCH --job-name=lrbinner_0.1_test
#SBATCH --mail-type=NONE
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32
#SBATCH --mem-per-cpu=4gb
#SBATCH --gpus=1
#SBATCH --time=24:00:00
#SBATCH --output=lrbinner_0.1_test.log

echo "Setting up test environment..."
TEST_PWD=/data/apps/tests/lrbinner/0.1
TEST_DATADIR=${TEST_PWD}/example_data
TEST_WORKDIR=${TEST_PWD}/test_output

cd ${TEST_PWD}
module load lrbinner/0.1

# Remove any previous test results and re-create a working directory
if [ -d ${TEST_WORKDIR} ]; then rm -rf ${TEST_WORKDIR}/; fi
mkdir ${TEST_WORKDIR}

echo "Starting test run at $(date) on $(hostname)..."

# Based on https://github.com/anuradhawick/LRBinner#test-run-data
###################################
LRBinner \
    reads \
    -r ${TEST_DATADIR}/reads.fasta \
    -bc 10 \
    -bs 32 \
    -o ${TEST_WORKDIR}/lrb \
    --cuda \
    -mbs 5000 \
    --ae-dims 4 \
    --ae-epochs 200 \
    -bit 0 \
    -t ${SLURM_JOB_CPUS_PER_NODE:-4}

# Evaluate results:
eval.py \
    --truth ${TEST_DATADIR}/ids.txt \
    --bins ${TEST_WORKDIR}/lrb/bins.txt \
    --print
###################################

echo "Test complete at $(date)."

Citation

If you publish research that uses LRBinner you have to cite it as follows:

@InProceedings{wickramarachchi_et_al:LIPIcs.WABI.2021.11, author = {Wickramarachchi, Anuradha and Lin, Yu}, title = {{LRBinner: Binning Long Reads in Metagenomics Datasets}}, booktitle = {21st International Workshop on Algorithms in Bioinformatics (WABI 2021)}, pages = {11:1--11:18}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-200-6}, ISSN = {1868-8969}, year = {2021}, volume = {201}, editor = {Carbone, Alessandra and El-Kebir, Mohammed}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/opus/volltexte/2021/14364}, URN = {urn:nbn:de:0030-drops-143644}, doi = {10.4230/LIPIcs.WABI.2021.11}, annote = {Keywords: Metagenomics binning, long reads, machine learning, clustering} }

Categories

biology, genomics