LRBinner
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Description
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} }