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.

System Variables

• HPC_LRBINNER_DIR - installation directory
• HPC_LRBINNER_BIN - executable directory

Job Script Examples

Below is a job script used for testing application installation

Expand to view example.

#!/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 --partition=gpu
#SBATCH --gres=gpu:a100:1
#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}
 }