git clone https://github.com/anuradhawick/LRBinner.git

cd LRBinner
python setup.py build

sh build.sh

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

_                                                           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

cd LRBinner
./LRBinner -h

usage: LRBinner [-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 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 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