Dockerfile is now available. (If you're familiar deploying a docker file, No Image pushed yet)
LRBinner is coded purely using C++ (v9+) and Python 3.10. To run LRBinner, you will need to install the following python and C++ modules.
A possible conda/mamba environment to work (credits Calum Walsh)
conda create -n lrbinner -y python=3.10 numpy scipy seaborn h5py hdbscan gcc openmp tqdm biopython fraggenescan hmmer tabulate pytorch pytorch-cuda=11.7 -c pytorch -c nvidia -c biocondaEssential libraries
- numpy 1.16.4
- scipy 1.3.0
- seaborn 0.9.0
- h5py 2.9.0
- tabulate 0.8.7
- pytorch 1.4.0
Essential for contig binning
- fraggenescan 1.31
- hmmer 3.3.2
- HDBSCAN
- GCC version 9.1.0 or later
- OpenMP 4.5 for multi processing
To download LRBinner, you have to clone the LRBinner repository to your machine.
git clone https://github.com/anuradhawick/LRBinner.git
- Build the binaries
cd LRBinner
python setup.py build
OR
sh build.sh
For long reads binning coverage of this dataset may be too low for assembly)
Extract Sim-8 data from here;
python LRBinner reads -r reads.fasta -bc 10 -bs 32 -o lrb --resume --cuda -mbs 5000 --ae-dims 4 --ae-epochs 200 -bit 0 -t 32
Note that the results could vary due to slight sampling differences. Evaluations can be done using the eval.py script.
_ Bin-0_(4) Bin-1_(1) Bin-2_(3) Bin-3_(0) Bin-4_(5) Bin-5_(2) Bin-6_(6) Bin-7_(7)
CP002618.1_Lactobacillus_paracasei_strain_BD-II 744 973 92 70169 215 0 0 0
NC_011658.1_Bacillus_cereus_AH187 110 11 277 10 30129 0 1 439
CP002807.1_Chlamydia_psittaci_08DC60_chromosome 0 0 9 0 1 0 0 11014
NC_012883.1_Thermococcus_sibiricus_MM_739 74 0 32441 1 28 2 0 8
NC_011415.1_Escherichia_coli_SE11,_complete_sequence 70474 432 47 220 616 3 0 43
NC_013929.1_Streptomyces_scabiei_87.22_complete_genome 343 0 0 0 0 118979 8 0
FQ312002.1_Haemophilus_parainfluenzae_T3T1_complete_genome 394 83623 32 191 1596 0 0 46
AP011170.1_Acetobacter_pasteurianus_IFO_3283-12_DNA 991 40 15 83 14 0 7382 13
Precision 98.12
Recall 98.12
F1-Score 98.12
Bins 8
Our manuscript presents results with -k 3 i.e. using 3-mers. Use -k 4 for tetramer based binning.
Internal parameters are not yet set for -k 5 choice. We are working on that. 😄
Use the -h argument to list all the available commands.
cd LRBinner
./lrbinner.py -h
usage: lrbinner.py [-h] [--version] {reads,contigs} ...
LRBinner Help. A tool developed for binning of metagenomics long reads
(PacBio/ONT) and long read assemblies. Tool utilizes composition and coverage
profiles of reads based on k-mer frequencies to perform dimension reduction
via a deep variational auto-encoder. Dimension reduced reads are then
clustered. Minimum RAM requirement is 9GB (4GB GPU if cuda used).
optional arguments:
-h, --help show this help message and exit
--version, -v Show version.
LRBinner running Mode:
{reads,contigs}
reads for binning reads
contigs for binning contigs
usage: lrbinner.py reads [-h] --reads-path READS_PATH [--k-size {3,4,5}]
[--bin-size BIN_SIZE] [--bin-count BIN_COUNT]
[--ae-epochs AE_EPOCHS] [--ae-dims AE_DIMS]
[--ae-hidden AE_HIDDEN] [--threads THREADS] [--separate]
[--cuda] [--resume] --output <DEST> [--version]
[--min-bin-size MIN_BIN_SIZE]
[--bin-iterations BIN_ITERATIONS]
optional arguments:
-h, --help show this help message and exit
--reads-path READS_PATH, -r READS_PATH
Reads path for binning
--k-size {3,4,5}, -k {3,4,5}
k value for k-mer frequency vector. Choose between 3
and 5.
--bin-size BIN_SIZE, -bs BIN_SIZE
Bin size for the coverage histogram.
--bin-count BIN_COUNT, -bc BIN_COUNT
Number of bins for the coverage histogram.
--ae-epochs AE_EPOCHS
Epochs for the auto_encoder.
--ae-dims AE_DIMS Size of the latent dimension.
--ae-hidden AE_HIDDEN
Hidden layer sizes eg: 128,128
--threads THREADS, -t THREADS
Thread count for computations
--separate, -sep Flag to separate reads/contigs into bins detected.
Avaialbe in folder named 'binned'.
--cuda Whether to use CUDA if available.
--resume Continue from the last step or the binning step (which
ever comes first). Can save time needed count k-mers.
--output <DEST>, -o <DEST>
Output directory
--version, -v Show version.
--min-bin-size MIN_BIN_SIZE, -mbs MIN_BIN_SIZE
The minimum number of reads a bin should have.
--bin-iterations BIN_ITERATIONS, -bit BIN_ITERATIONS
Number of iterations for cluster search. Use 0 for
exhaustive search.
usage: lrbinner.py contigs [-h] --reads-path READS_PATH [--k-size {3,4,5}]
[--bin-size BIN_SIZE] [--bin-count BIN_COUNT]
[--ae-epochs AE_EPOCHS] [--ae-dims AE_DIMS]
[--ae-hidden AE_HIDDEN] [--threads THREADS]
[--separate] [--cuda] [--resume] --output <DEST>
[--version] --contigs CONTIGS
optional arguments:
-h, --help show this help message and exit
--reads-path READS_PATH, -r READS_PATH
Reads path for binning
--k-size {3,4,5}, -k {3,4,5}
k value for k-mer frequency vector. Choose between 3
and 5.
--bin-size BIN_SIZE, -bs BIN_SIZE
Bin size for the coverage histogram.
--bin-count BIN_COUNT, -bc BIN_COUNT
Number of bins for the coverage histogram.
--ae-epochs AE_EPOCHS
Epochs for the auto_encoder.
--ae-dims AE_DIMS Size of the latent dimension.
--ae-hidden AE_HIDDEN
Hidden layer sizes eg: 128,128
--threads THREADS, -t THREADS
Thread count for computations
--separate, -sep Flag to separate reads/contigs into bins detected.
Avaialbe in folder named 'binned'.
--cuda Whether to use CUDA if available.
--resume Continue from the last step or the binning step (which
ever comes first). Can save time needed count k-mers.
--output <DEST>, -o <DEST>
Output directory
--version, -v Show version.
--contigs CONTIGS, -c CONTIGS
Contigs path
If you use LRBinner please cite using the following bibtex entries.
@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}
}@Article{Wickramarachchi2022,
author={Wickramarachchi, Anuradha and Lin, Yu},
title={Binning long reads in metagenomics datasets using composition and coverage information},
journal={Algorithms for Molecular Biology},
year={2022},
month={Jul},
day={11},
volume={17},
number={1},
pages={14},
issn={1748-7188},
doi={10.1186/s13015-022-00221-z},
url={https://doi.org/10.1186/s13015-022-00221-z}
}
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