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srisvs33 kalonji08 emeraldyyy ohickl edwardbirdlab

comebin's Issues

Contamination & complexity

Hi

Thanks for the great tool. I am currently looking into plant metagenomic data - so host contamination/complexity - so I wasn wondering if COMEBin been assessed on how it perform with eukaryotic contamination? I noticed that COMEBin predict lot higher number of bins in comparison to tools like MetaBat2 or MaxBin2 (eg 105 vs 26/10 bins from metabat/maxbin) - so was curious as to what might be the cause? any thoughts would be much appreciated.

logits error

Hello,

I'm getting an error that was seen in a closed thread, but the fixes did not address my problem. I'm running v1.0.4 on an assembly that has >56,000 contigs. I've tried using a BAM file created through the "gen_cov_file.sh" script. The error is:

Traceback (most recent call last):
File "main.py", line 369, in
main()
File "main.py", line 289, in main
train_CLmodel(logger,args)
File "/projects/comp_genomics/sahl/tools/miniconda/envs/comebin_env/bin/COMEBin/train_CLmodel.py", line 236, in train_CLmodel
simclr.train_addpretrain(train_loader, dataset, namelist)
File "/projects/comp_genomics/sahl/tools/miniconda/envs/comebin_env/bin/COMEBin/simclr.py", line 300, in train_addpretrain
top1, top5 = accuracy(logits, labels, topk=(1, 5))
UnboundLocalError: local variable 'logits' referenced before assignment
Something went wrong with running training network. Exiting.

I have successfully run the pipeline on the sample data. Any help would be appreciated.

thanks

Single-copy maker genes for clustering

Hi this tool looks great, and am excited to give it a go. I'm just curious what set of marker genes are used in the final clustering step. I am interested in generating eukaryote MAGs, though. Are the markers prokaryote-specific and thus exclude any eukaryote bins from the final output?

control thread usage better

Hi there - great tool - I'm really excited about using this, but what I've noticed is that regardless of the value of -t, the training part of the algorithm always uses all available CPU cores (in the absence of a GPU). Is there a way to control this better? If this can be addressed I can continue with my testing of the tool :)

Error code 12 (Cannot allocate memory)

Hello Ziye:
I tried to run the latest version of COMEBin with CPU/GPU and there is something wrong happened:

  100%|██████████| 1665/1665 [10:54<00:00,  2.54it/s]
  100%|██████████| 1665/1665 [10:55<00:00,  2.54it/s]
  100%|██████████| 1665/1665 [10:45<00:00,  2.58it/s]
  100%|██████████| 1665/1665 [11:01<00:00,  2.52it/s]
   66%|██████▌   | 1096/1665 [07:14<03:45,  2.52it/s]
  Traceback (most recent call last):
    File "main.py", line 369, in <module>
      main()
    File "main.py", line 289, in main
      train_CLmodel(logger,args)
    File "/public/home/hymeta/anaconda3/envs/comebin/bin/COMEBin/train_CLmodel.py", line 236, in train_CLmodel
      simclr.train_addpretrain(train_loader, dataset, namelist)
    File "/public/home/hymeta/anaconda3/envs/comebin/bin/COMEBin/simclr.py", line 283, in train_addpretrain
      logits, labels = self.info_nce_loss(features)
    File "/public/home/hymeta/anaconda3/envs/comebin/bin/COMEBin/simclr.py", line 42, in info_nce_loss
      labels = (labels.unsqueeze(0) == labels.unsqueeze(1)).float()
  RuntimeError: [enforce fail at CPUAllocator.cpp:68] . DefaultCPUAllocator: can't allocate memory: you tried to allocate 150994944 bytes. Error code 12 (Cannot allocate memory)
  Something went wrong with running training network. Exiting.

By the way, the memory in our HPC node is 512GB(CPU) and 64GB(GPU)
I am confused. Thanks in advance

Best wishes
Joey

Compare with metabinner

Hello, thank you very much for providing such an excellent binning tool.
I have noticed that Metabinner also originates from your team. Could you please share your recommendation on which one you would suggest using? Thank you very much!

AttributeError: module 'numpy' has no attribute 'int'.

Hi, any idea what this is? My data is single-read and things worked fine until this step.

Running getting representation.
2024-03-01 20:41:57,205 - train
2024-03-01 20:41:57,231 - Generate features for the data.
Traceback (most recent call last):
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/main.py", line 369, in
main()
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/main.py", line 289, in main
train_CLmodel(logger,args)
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/train_CLmodel.py", line 38, in train_CLmodel
dataset, namelist = get_ContrastiveLearningDataset(args.data, args.n_views,
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/get_augfeature.py", line 155, in get_ContrastiveLearningDataset
dataset, namelist = get_kmer_coverage(data_path, n_views, kmer_model_path, device, nokmer, cov_meannormalize, cov_minmaxnormalize, cov_standardization,addvars,vars_sqrt,kmer_l2_normalize,kmerMetric_notl2normalize)
File "/mnt/scratch/robinm/software/COMEBin/COMEBin/get_augfeature.py", line 41, in get_kmer_coverage
covIdxArr = np.empty(len(mapObj), dtype=np.int)
File "/mnt/scratch/robinm/software/mambaforge/lib/python3.10/site-packages/numpy/init.py", line 324, in getattr
raise AttributeError(former_attrs[attr])
AttributeError: module 'numpy' has no attribute 'int'.
np.int was a deprecated alias for the builtin int. To avoid this error in existing code, use int by itself. Doing this will not modify any behavior and is safe. When replacing np.int, you may wish to use e.g. np.int64 or np.int32 to specify the precision. If you wish to review your current use, check the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations. Did you mean: 'inf'?
Something went wrong with running training network. Exiting.

Use checkm2 instead of checkm

Hi,

First of all, thanks for developing ComeBin.
I was wondering if you had considered switching to checkm2 instead of checkm to determine the best settings for graph clustering?
Checkm2 is much faster than checkm (especially for low-completeness bins) and provide betters estimates.

Best,
Florian

markerCmd failed!

Hi again! This time I have a new error ;)

I copy here the comebin.log. I dont understand why it says the files are not there, because they are there?

cat comebin.log
2024-02-07 14:39:07,115 - train
2024-02-07 14:39:07,287 - Generate features for the data.
2024-02-07 14:43:17,827 - Finish training.
2024-02-07 14:43:21,169 - bin
2024-02-07 14:43:21,170 - exec cmd: run_FragGeneScan.pl -genome=/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta -out=/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.frag -complete=0 -train=complete -thread=5 1>/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.frag.out 2>/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.frag.err
2024-02-07 14:43:24,136 - exec cmd: hmmsearch --domtblout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout --cut_tc --cpu 5 /ttmgr/COMEBin/COMEBin/../auxiliary/bacar_marker.hmm /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.frag.faa 1>/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout.out 2>/ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout.err
2024-02-07 14:43:24,613 - exec cmd: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 14:43:24,648 - markerCmd failed! Not exist: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 14:43:24,648 - Start clustering.
2024-02-07 14:43:24,700 - N50: 7243
2024-02-07 14:43:27,335 - get_result
2024-02-07 14:43:28,662 - exec cmd: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 14:43:28,675 - markerCmd failed! Not exist: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 14:43:28,675 - Seed_num: 0
2024-02-07 14:43:28,675 - Run unitem profile: 0
2024-02-07 15:00:46,408 - bin
2024-02-07 15:00:46,418 - exec cmd: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 15:00:47,444 - markerCmd failed! Not exist: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 15:00:47,445 - Start clustering.
2024-02-07 15:00:48,023 - N50: 7243
2024-02-07 15:00:58,941 - get_result
2024-02-07 15:00:59,415 - exec cmd: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 15:00:59,430 - markerCmd failed! Not exist: /ttmgr/COMEBin/COMEBin/../auxiliary/test_getmarker_2quarter.pl /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.hmmout /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta 1001 /ttmgr/COMEBin/COMEBin/scripts/air_sv_barcode01/sv_reference.fasta.bacar_marker.2quarter_lencutoff_1001.seed
2024-02-07 15:00:59,431 - Seed_num: 0
2024-02-07 15:00:59,431 - Run unitem profile: 0

thanks in advance!

Error with gen_cov.py

Hi,

I tried to run comebin like this:
run_comebin.sh -a /projects/0/prjs0956/assembly/megahit/binned/SB_DM_F5B_0-2_II_assembly/final.contigs.fasta \ -p /projects/0/prjs0956/mapping/ \ -o /projects/0/prjs0956/binning/comebin_out/ \ -t 64

but I got this error

`Traceback (most recent call last):
File "/gpfs/work4/0/prjs0956/tools/miniconda3/envs/comebin_env/bin/COMEBin/data_aug/gen_cov.py", line 24, in call
result = self.__callable(*args, **kwargs)
File "/gpfs/work4/0/prjs0956/tools/miniconda3/envs/comebin_env/bin/COMEBin/data_aug/gen_cov.py", line 129, in calculate_coverage_samplebyindex
start = aug_seq_info_dict[contig_name][0]
KeyError: 'NODE_1_length_355648_cov_13.388902'

Traceback (most recent call last):
File "main.py", line 369, in
main()
File "main.py", line 353, in main
run_gen_cov(logger, args)
File "/gpfs/work4/0/prjs0956/tools/miniconda3/envs/comebin_env/bin/COMEBin/data_aug/gen_cov.py", line 302, in run_gen_cov
gen_cov_from_bedout(logger, args.out_augdata_path, out, num_aug=args.n_views-1, contig_len=args.contig_len,num_process=args.num_threads)
File "/gpfs/work4/0/prjs0956/tools/miniconda3/envs/comebin_env/bin/COMEBin/data_aug/gen_cov.py", line 270, in gen_cov_from_bedout
res_mat = pd.read_csv(cov_file, sep='\t', header=0, index_col=0)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 586, in read_csv
return _read(filepath_or_buffer, kwds)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 482, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 811, in init
self._engine = self._make_engine(self.engine)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/readers.py", line 1040, in _make_engine
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 51, in init
self._open_handles(src, kwds)
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/parsers/base_parser.py", line 229, in _open_handles
errors=kwds.get("encoding_errors", "strict"),
File "/projects/0/prjs0956/tools/miniconda3/envs/comebin_env/lib/python3.7/site-packages/pandas/io/common.py", line 707, in get_handle
newline="",
FileNotFoundError: [Errno 2] No such file or directory: '/gpfs/work4/0/prjs0956/binning/comebin_out/data_augmentation/depth/SB_DM_F5B_0-2_II.bam_0_depth.txt_aug1_data_cov.csv'
`

Any ideas why?

thanks!
Menia

Problems with augmented data

Hello!
I wanted to use your tool on my sequencing data, but when I run run_comebin.sh I get this error message:

N50: 3523
Tau(temperature): 0.15
./processing/combined/data_augmentation does not exist.
Running data augmentation.
Something went wrong with running generating augmentation data. Exiting.

The first step, gen_cov_file.sh, gave me the following output:
assembly.fa
assembly.fa.bwt
filtered_barcode01_passed.bam
filtered_barcode11_passed.bam
filtered_barcode14_passed.bam
assembly.fa.amb
assembly.fa.pac
filtered_barcode06_passed.bam
filtered_barcode12_passed.bam
assembly.fa.ann assembly.fa.sa
filtered_barcode07_passed.bam
filtered_barcode13_passed.bam

I am a bit lost! Thanks in advance :)

UnboundLocalError: local variable 'joined' referenced before assignment

Hi, I'm trying to run COMEBin but I'm encountering the following issue:

Traceback (most recent call last):
  File "main.py", line 368, in <module>
    main()
  File "main.py", line 352, in main
    run_gen_cov(logger, args)
  File "/work/ese-alexp/software/python/anaconda3/2021.11/envs/comebin_v103/bin/COMEBin/data_aug/gen_cov.py", line 302, in run_gen_cov
    gen_cov_from_bedout(logger, args.out_augdata_path, out, num_aug=args.n_views-1, contig_len=args.contig_len,num_process=args.num_threads)
  File "/work/ese-alexp/software/python/anaconda3/2021.11/envs/comebin_v103/bin/COMEBin/data_aug/gen_cov.py", line 244, in gen_cov_from_bedout
    joined.to_csv(outfile, sep='\t', header=True)
UnboundLocalError: local variable 'joined' referenced before assignment

This is the command I'm running:

run_comebin.sh -a contigs_1000.fasta -o comebin_bins -p /data/sort_bam_files -t 40

This is the log file:

2023-11-01 15:02:38,764 - generate_aug_data: fastafile
2023-11-01 15:05:53,818 - aug:	1
2023-11-01 15:06:55,554 - aug:	2
2023-11-01 15:08:01,244 - aug:	3
2023-11-01 15:09:04,357 - aug:	4
2023-11-01 15:10:05,453 - aug:	5
2023-11-01 15:11:10,134 - Generate coverage files from bam files.
Traceback (most recent call last):
  File "main.py", line 368, in <module>
    main()
  File "main.py", line 352, in main
    run_gen_cov(logger, args)
  File "/work/ese-alexp/software/python/anaconda3/2021.11/envs/comebin_v103/bin/COMEBin/data_aug/gen_cov.py", line 302, in run_gen_cov
    gen_cov_from_bedout(logger, args.out_augdata_path, out, num_aug=args.n_views-1, contig_len=args.contig_len,num_process=args.num_threads)
  File "/work/ese-alexp/software/python/anaconda3/2021.11/envs/comebin_v103/bin/COMEBin/data_aug/gen_cov.py", line 244, in gen_cov_from_bedout
    joined.to_csv(outfile, sep='\t', header=True)
UnboundLocalError: local variable 'joined' referenced before assignment

"timeout or by a memory leak.", UserWarning

Hi,

I have used Comebin several times without any issues, but I encountered a problem when I tried to run it on a large dataset. I ran Comebin in CPU mode on a dataset containing more than 2.5 million contigs. I received the following message:

2024-06-12 14:45:48,897 - bin
2024-06-12 14:45:48,900 - Start clustering.
2024-06-12 14:47:19,684 - N50: 4304
2024-06-12 14:47:27,225 - Run weighted seed k-means for obtaining the SCG information of the contigs within a manageable time during the final step.
2024-06-12 14:47:27,225 - Bin_numbers: [2539]
2024-06-12 14:47:27,225 - 2539
/home/user/.conda/envs/comebin_env/lib/python3.7/site-packages/joblib/externals/loky/process_executor.py:705: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
  "timeout or by a memory leak.", UserWarning

It has been stuck at this step for 2 days now. I checked Comebin's status in htop and saw that it is still running:

/home/user/.conda/envs/comebin_env/bin/python -m joblib.externals.loky.backend.popen_loky_posix --process-name LokyProcess-1 --pipe 14
/home/user/.conda/envs/comebin_env/bin/python -m joblib.externals.loky.backend.popen_loky_posix --process-name LokyProcess-2 --pipe 15
/home/user/.conda/envs/comebin_env/bin/python -m joblib.externals.loky.backend.popen_loky_posix --process-name LokyProcess-3 --pipe 16
...

Is this normal? Has anyone else encountered this message before? I googled the message and found that it does not necessarily affect the results. Should I continue to wait for the results? The program has been running for about 20 days now.

I have 192 GB RAM and 47 threads. Command used: Comebin: -t 46
Conda version: 23.3.1

Best,
Roland

Error in get_final_results.py : ValueError: max() arg is an empty sequence

Hi! Sorry to bug you again.
I've tested COMEBin with the test data provided and a reduced dataset and everything ran fine.
Now I'm trying to bin contigs generated by the co-assembly of five samples, but I'm consistently getting this error after the Run unitem profile step:

Traceback (most recent call last):
  File "main.py", line 369, in <module>
    main()
  File "main.py", line 365, in main
    run_get_final_result(logger, args, seed_num, num_threads, ignore_kmeans_res=True)
  File "/stor/progetti/p1037/p1037u03/miniconda3/envs/comebin/bin/COMEBin/get_final_result.py", line 232, in run_get_final_result
    best_method = estimate_bins_quality_nobins(bac_mg_table, ar_mg_table, args.output_path + '/cluster_res/',ignore_kmeans_res=ignore_kmeans_res)
  File "/stor/progetti/p1037/p1037u03/miniconda3/envs/comebin/bin/COMEBin/get_final_result.py", line 190, in estimate_bins_quality_nobins
    bin_quality_dict, best_method = get_bin_quality(orig_bins, methods_sorted, markers)
  File "/stor/progetti/p1037/p1037u03/miniconda3/envs/comebin/bin/COMEBin/get_final_result.py", line 90, in get_bin_quality
    sum_max = max(sum_list)
ValueError: max() arg is an empty sequence

Any idea on what might be causing it?

Thank you!
Riccardo

Here's the full log:
comebin_error_15Mar24.txt

Parsing final output - is there any value in preclustered bins?

Hello folks,

Great tool so far, I appreciate the ease of use/install compared to the complexity of metaWRAP and DAS_tool dependencies.
I had a question about how to best parse the output of bins. Running the tool through a single sample, it looks like two folders of bins are created - comebin_res_bins and cluster_res/$(best model name)_bins

From what I can tell, the comebin_res_bins is the initial split of every contig such that all contigs are placed into some bin, while the cluster_res result only has some contigs placed into bins, based on your Leiden clustering. When I run the comebin_res_bins fastas through CheckM1 - I get 94 bins meeting 50% completeness, 10% contamination. But, I can see the CheckM1 output provided by combin for the cluster_res bins given in cluster_res/unitem_profile, which has only 30 clustered bins that reach > 50% complete, < 10% contaminated. The number of higher quality (90% complete, 5% contamination) bins is also higher noticably before clustering (33 vs. 16).

Is the clustering performing a dereplication function similar to dRep, where only the best bin from a kmer-coverage-similarity group is kept? I often use % nucleotides binned in medium-quality MAGs as a metric for evaluating binning software, so this difference (26% nucleotides binned after clustering vs. 56% nucleotides binned before clustering) jumped out at me. I can appreciate the value in having fewer, consolidated bins per predicted taxa, but I just wanted to confirm what this tool is doing before I move forward with any post processing of the MAGs.

Thanks for your help!

Apple silicon MPS support

COMEBin looks very interesting!
I would really like to use this with Apple silicon metal performance shaders ('mps') support instead of cuda.

I could be able to help add some of the changes but it looks like mps does not support autocast which is currently being worked on in this PR

conda install warnings

Errors when installing 
$ conda create -y -n comebin-1.0.4 -c pytorch -c nvidia -c conda-forge -c bioconda comebin=1.0.4 pytorch pytorch-cuda=11.8
Channels:
 - pytorch
 - nvidia
 - conda-forge
 - bioconda
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /home/apps/conda/miniconda3/envs/comebin-1.0.4

  added / updated specs:
    - comebin=1.0.4
    - pytorch
    - pytorch-cuda=11.8


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    _openmp_mutex-4.5          |       2_kmp_llvm           6 KB  conda-forge
    blas-2.121                 |              mkl          14 KB  conda-forge
    blas-devel-3.9.0           |   21_linux64_mkl          14 KB  conda-forge
    cached-property-1.5.2      |       hd8ed1ab_1           4 KB  conda-forge
    comebin-1.0.4              |       hdfd78af_0         2.6 MB  bioconda
    cuda-libraries-11.8.0      |                0           1 KB  nvidia
    cuda-runtime-11.8.0        |                0           1 KB  nvidia
    importlib_metadata-4.11.4  |       hd8ed1ab_0           4 KB  conda-forge
    libcblas-3.9.0             |   21_linux64_mkl          14 KB  conda-forge
    liblapack-3.9.0            |   21_linux64_mkl          14 KB  conda-forge
    liblapacke-3.9.0           |   21_linux64_mkl          14 KB  conda-forge
    mkl-2024.0.0               |   ha957f24_49657       120.2 MB  conda-forge
    mkl-devel-2024.0.0         |   ha770c72_49657          31 KB  conda-forge
    mkl-include-2024.0.0       |   ha957f24_49657         750 KB  conda-forge
    python_abi-3.7             |          4_cp37m           6 KB  conda-forge
    pytorch-cuda-11.8          |       h7e8668a_5           3 KB  pytorch
    pytorch-mutex-1.0          |             cuda           3 KB  pytorch
    seaborn-0.12.1             |       hd8ed1ab_0           5 KB  conda-forge
    tbb-2021.11.0              |       h00ab1b0_1         191 KB  conda-forge
    typing-extensions-4.7.1    |       hd8ed1ab_0          10 KB  conda-forge
    ------------------------------------------------------------
                                           Total:       123.9 MB

The following NEW packages will be INSTALLED:

  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge
  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_kmp_llvm
  absl-py            conda-forge/noarch::absl-py-2.1.0-pyhd8ed1ab_0
  anndata            conda-forge/noarch::anndata-0.8.0-pyhd8ed1ab_1
  arpack             conda-forge/linux-64::arpack-3.7.0-hdefa2d7_2
  atomicwrites       conda-forge/noarch::atomicwrites-1.4.0-pyh9f0ad1d_0
  bedtools           bioconda/linux-64::bedtools-2.30.0-h468198e_3
  biolib             bioconda/noarch::biolib-0.1.6-py_0
  biopython          conda-forge/linux-64::biopython-1.76-py37h516909a_0
  blas               conda-forge/linux-64::blas-2.121-mkl
  blas-devel         conda-forge/linux-64::blas-devel-3.9.0-21_linux64_mkl
  brotli             conda-forge/linux-64::brotli-1.1.0-hd590300_1
  brotli-bin         conda-forge/linux-64::brotli-bin-1.1.0-hd590300_1
  bwa                bioconda/linux-64::bwa-0.7.17-he4a0461_11
  bzip2              conda-forge/linux-64::bzip2-1.0.8-hd590300_5
  c-ares             conda-forge/linux-64::c-ares-1.26.0-hd590300_0
  ca-certificates    conda-forge/linux-64::ca-certificates-2024.2.2-hbcca054_0
  cached-property    conda-forge/noarch::cached-property-1.5.2-hd8ed1ab_1
  cached_property    conda-forge/noarch::cached_property-1.5.2-pyha770c72_1
  certifi            conda-forge/noarch::certifi-2023.11.17-pyhd8ed1ab_0
  checkm-genome      bioconda/noarch::checkm-genome-1.1.3-py_1
  click              conda-forge/linux-64::click-8.0.4-py37h89c1867_0
  colorama           conda-forge/noarch::colorama-0.4.6-pyhd8ed1ab_0
  comebin            bioconda/noarch::comebin-1.0.4-hdfd78af_0
  cuda-cudart        nvidia/linux-64::cuda-cudart-11.8.89-0
  cuda-cupti         nvidia/linux-64::cuda-cupti-11.8.87-0
  cuda-libraries     nvidia/linux-64::cuda-libraries-11.8.0-0
  cuda-nvrtc         nvidia/linux-64::cuda-nvrtc-11.8.89-0
  cuda-nvtx          nvidia/linux-64::cuda-nvtx-11.8.86-0
  cuda-runtime       nvidia/linux-64::cuda-runtime-11.8.0-0
  cudatoolkit        nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  cycler             conda-forge/noarch::cycler-0.11.0-pyhd8ed1ab_0
  dendropy           bioconda/noarch::dendropy-4.6.1-pyhdfd78af_0
  fonttools          conda-forge/linux-64::fonttools-4.38.0-py37h540881e_0
  fraggenescan       bioconda/linux-64::fraggenescan-1.31-h031d066_6
  freetype           conda-forge/linux-64::freetype-2.12.1-h267a509_2
  future             conda-forge/linux-64::future-0.18.2-py37h89c1867_5
  gettext            conda-forge/linux-64::gettext-0.21.1-h27087fc_0
  glpk               conda-forge/linux-64::glpk-4.65-h9202a9a_1004
  gmp                conda-forge/linux-64::gmp-6.3.0-h59595ed_0
  grpc-cpp           conda-forge/linux-64::grpc-cpp-1.48.1-h05bd8bd_1
  grpcio             conda-forge/linux-64::grpcio-1.48.1-py37h3fae8ff_1
  h5py               conda-forge/linux-64::h5py-3.6.0-nompi_py37hd308b1e_100
  hdf5               conda-forge/linux-64::hdf5-1.12.1-nompi_h2386368_104
  hmmer              bioconda/linux-64::hmmer-3.1b2-3
  hnswlib            conda-forge/linux-64::hnswlib-0.6.2-py37h48bf904_3
  htslib             bioconda/linux-64::htslib-1.17-h6bc39ce_1
  icu                conda-forge/linux-64::icu-73.2-h59595ed_0
  igraph             conda-forge/linux-64::igraph-0.9.9-h026ac8f_0
  importlib-metadata conda-forge/linux-64::importlib-metadata-4.11.4-py37h89c1867_0
  importlib_metadata conda-forge/noarch::importlib_metadata-4.11.4-hd8ed1ab_0
  joblib             conda-forge/noarch::joblib-1.1.0-pyhd8ed1ab_0
  jpeg               conda-forge/linux-64::jpeg-9e-h0b41bf4_3
  keyutils           conda-forge/linux-64::keyutils-1.6.1-h166bdaf_0
  kiwisolver         conda-forge/linux-64::kiwisolver-1.4.4-py37h7cecad7_0
  krb5               conda-forge/linux-64::krb5-1.20.1-hf9c8cef_0
  lcms2              conda-forge/linux-64::lcms2-2.14-h6ed2654_0
  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.40-h41732ed_0
  leidenalg          conda-forge/linux-64::leidenalg-0.8.10-py37hd23a5d3_0
  lerc               conda-forge/linux-64::lerc-4.0.0-h27087fc_0
  libabseil          conda-forge/linux-64::libabseil-20220623.0-cxx17_h05df665_6
  libblas            conda-forge/linux-64::libblas-3.9.0-21_linux64_mkl
  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.1.0-hd590300_1
  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.1.0-hd590300_1
  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.1.0-hd590300_1
  libcblas           conda-forge/linux-64::libcblas-3.9.0-21_linux64_mkl
  libcublas          nvidia/linux-64::libcublas-11.11.3.6-0
  libcufft           nvidia/linux-64::libcufft-10.9.0.58-0
  libcufile          nvidia/linux-64::libcufile-1.8.1.2-0
  libcurand          nvidia/linux-64::libcurand-10.3.4.107-0
  libcurl            conda-forge/linux-64::libcurl-7.87.0-h6312ad2_0
  libcusolver        nvidia/linux-64::libcusolver-11.4.1.48-0
  libcusparse        nvidia/linux-64::libcusparse-11.7.5.86-0
  libdeflate         conda-forge/linux-64::libdeflate-1.13-h166bdaf_0
  libedit            conda-forge/linux-64::libedit-3.1.20191231-he28a2e2_2
  libev              conda-forge/linux-64::libev-4.33-hd590300_2
  libffi             conda-forge/linux-64::libffi-3.4.2-h7f98852_5
  libgcc-ng          conda-forge/linux-64::libgcc-ng-13.2.0-h807b86a_5
  libgfortran-ng     conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_5
  libgfortran5       conda-forge/linux-64::libgfortran5-13.2.0-ha4646dd_5
  libhwloc           conda-forge/linux-64::libhwloc-2.9.3-default_h554bfaf_1009
  libiconv           conda-forge/linux-64::libiconv-1.17-hd590300_2
  libidn2            conda-forge/linux-64::libidn2-2.3.7-hd590300_0
  liblapack          conda-forge/linux-64::liblapack-3.9.0-21_linux64_mkl
  liblapacke         conda-forge/linux-64::liblapacke-3.9.0-21_linux64_mkl
  libllvm11          conda-forge/linux-64::libllvm11-11.1.0-he0ac6c6_5
  libnghttp2         conda-forge/linux-64::libnghttp2-1.51.0-hdcd2b5c_0
  libnpp             nvidia/linux-64::libnpp-11.8.0.86-0
  libnsl             conda-forge/linux-64::libnsl-2.0.1-hd590300_0
  libnvjpeg          nvidia/linux-64::libnvjpeg-11.9.0.86-0
  libpng             conda-forge/linux-64::libpng-1.6.42-h2797004_0
  libprotobuf        conda-forge/linux-64::libprotobuf-3.21.8-h6239696_0
  libsqlite          conda-forge/linux-64::libsqlite-3.44.2-h2797004_0
  libssh2            conda-forge/linux-64::libssh2-1.10.0-haa6b8db_3
  libstdcxx-ng       conda-forge/linux-64::libstdcxx-ng-13.2.0-h7e041cc_5
libtiff            conda-forge/linux-64::libtiff-4.4.0-h0e0dad5_3
  libunistring       conda-forge/linux-64::libunistring-0.9.10-h7f98852_0
  libuv              conda-forge/linux-64::libuv-1.46.0-hd590300_0
  libwebp-base       conda-forge/linux-64::libwebp-base-1.3.2-hd590300_0
  libxcb             conda-forge/linux-64::libxcb-1.13-h7f98852_1004
  libxcrypt          conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1
  libxml2            conda-forge/linux-64::libxml2-2.12.4-h232c23b_1
  libzlib            conda-forge/linux-64::libzlib-1.2.13-hd590300_5
  llvm-openmp        conda-forge/linux-64::llvm-openmp-17.0.6-h4dfa4b3_0
  llvmlite           conda-forge/linux-64::llvmlite-0.38.1-py37h0761922_0
  markdown           conda-forge/noarch::markdown-3.5.2-pyhd8ed1ab_0
  markupsafe         conda-forge/linux-64::markupsafe-2.1.1-py37h540881e_1
  matplotlib-base    conda-forge/linux-64::matplotlib-base-3.5.1-py37h1058ff1_0
  matplotlib-inline  conda-forge/noarch::matplotlib-inline-0.1.3-pyhd8ed1ab_0
  metis              conda-forge/linux-64::metis-5.1.0-h59595ed_1007
  mkl                conda-forge/linux-64::mkl-2024.0.0-ha957f24_49657
  mkl-devel          conda-forge/linux-64::mkl-devel-2024.0.0-ha770c72_49657
  mkl-include        conda-forge/linux-64::mkl-include-2024.0.0-ha957f24_49657
  mpfr               conda-forge/linux-64::mpfr-4.2.1-h9458935_0
  munkres            conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0
  natsort            conda-forge/noarch::natsort-8.4.0-pyhd8ed1ab_0
  ncurses            conda-forge/linux-64::ncurses-6.4-h59595ed_2
  networkx           conda-forge/noarch::networkx-2.6.3-pyhd8ed1ab_1
  numba              conda-forge/linux-64::numba-0.55.1-py37h2d894fd_0
  numpy              conda-forge/linux-64::numpy-1.19.0-py37h8960a57_0
  openjpeg           conda-forge/linux-64::openjpeg-2.5.0-h7d73246_1
  openssl            conda-forge/linux-64::openssl-1.1.1w-hd590300_0
  packaging          conda-forge/noarch::packaging-23.2-pyhd8ed1ab_0
  pandas             conda-forge/linux-64::pandas-1.3.5-py37he8f5f7f_0
  patsy              conda-forge/noarch::patsy-0.5.6-pyhd8ed1ab_0
  perl               conda-forge/linux-64::perl-5.32.1-7_hd590300_perl5
  pillow             conda-forge/linux-64::pillow-9.2.0-py37h850a105_2
  pip                conda-forge/noarch::pip-22.0.4-pyhd8ed1ab_0
  pplacer            bioconda/linux-64::pplacer-1.1.alpha19-h9ee0642_2
  prodigal           bioconda/linux-64::prodigal-2.6.3-h031d066_7
  protobuf           conda-forge/linux-64::protobuf-4.21.8-py37hd23a5d3_0
  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001
  pynndescent        conda-forge/noarch::pynndescent-0.5.11-pyhca7485f_0
  pyparsing          conda-forge/noarch::pyparsing-3.1.1-pyhd8ed1ab_0
  pysam              bioconda/linux-64::pysam-0.21.0-py37hee149a5_0
  python             conda-forge/linux-64::python-3.7.12-hb7a2778_100_cpython
  python-dateutil    conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0
  python-fastjsonsc~ conda-forge/noarch::python-fastjsonschema-2.15.3-pyhd8ed1ab_0
  python-igraph      conda-forge/linux-64::python-igraph-0.9.11-py37h87e8262_0
  python_abi         conda-forge/linux-64::python_abi-3.7-4_cp37m
  pytorch            pytorch/linux-64::pytorch-1.10.2-py3.7_cuda11.3_cudnn8.2.0_0
  pytorch-cuda       pytorch/linux-64::pytorch-cuda-11.8-h7e8668a_5
  pytorch-mutex      pytorch/noarch::pytorch-mutex-1.0-cuda
  pytz               conda-forge/noarch::pytz-2023.4-pyhd8ed1ab_0
  pyyaml             conda-forge/linux-64::pyyaml-6.0-py37h540881e_4
  re2                conda-forge/linux-64::re2-2022.06.01-h27087fc_1
  readline           conda-forge/linux-64::readline-8.2-h8228510_1
  samtools           bioconda/linux-64::samtools-1.15.1-h6899075_1
  scanpy             conda-forge/noarch::scanpy-1.9.1-pyhd8ed1ab_0
  scikit-learn       conda-forge/linux-64::scikit-learn-0.22.1-py37hcdab131_1
  scipy              conda-forge/linux-64::scipy-1.7.3-py37hf2a6cf1_0
  seaborn            conda-forge/noarch::seaborn-0.12.1-hd8ed1ab_0
  seaborn-base       conda-forge/noarch::seaborn-base-0.12.1-pyhd8ed1ab_0
  session-info       conda-forge/noarch::session-info-1.0.0-pyhd8ed1ab_0
  setuptools         conda-forge/linux-64::setuptools-59.5.0-py37h89c1867_0
  six                conda-forge/noarch::six-1.16.0-pyh6c4a22f_0
  sqlite             conda-forge/linux-64::sqlite-3.44.2-h2c6b66d_0
  statsmodels        conda-forge/linux-64::statsmodels-0.13.2-py37hb1e94ed_0
  stdlib-list        conda-forge/noarch::stdlib-list-0.10.0-pyhd8ed1ab_0
  suitesparse        conda-forge/linux-64::suitesparse-5.10.1-h5a4f163_3
  tbb                conda-forge/linux-64::tbb-2021.11.0-h00ab1b0_1
  tensorboard        conda-forge/linux-64::tensorboard-1.15.0-py37_0
  texttable          conda-forge/noarch::texttable-1.7.0-pyhd8ed1ab_0
  tk                 conda-forge/linux-64::tk-8.6.13-noxft_h4845f30_101
  tqdm               conda-forge/noarch::tqdm-4.66.1-pyhd8ed1ab_0
  traitlets          conda-forge/noarch::traitlets-5.9.0-pyhd8ed1ab_0
  typing-extensions  conda-forge/noarch::typing-extensions-4.7.1-hd8ed1ab_0
  typing_extensions  conda-forge/noarch::typing_extensions-4.7.1-pyha770c72_0
  umap-learn         conda-forge/linux-64::umap-learn-0.5.3-py37h89c1867_0
  unicodedata2       conda-forge/linux-64::unicodedata2-14.0.0-py37h540881e_1
  werkzeug           conda-forge/noarch::werkzeug-2.2.3-pyhd8ed1ab_0
  wget               conda-forge/linux-64::wget-1.20.3-ha56f1ee_1
  wheel              conda-forge/noarch::wheel-0.42.0-pyhd8ed1ab_0
  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.11-hd590300_0
  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0
  xz                 conda-forge/linux-64::xz-5.2.6-h166bdaf_0
  yaml               conda-forge/linux-64::yaml-0.2.5-h7f98852_2
  zipp               conda-forge/noarch::zipp-3.15.0-pyhd8ed1ab_0
  zlib               conda-forge/linux-64::zlib-1.2.13-hd590300_5
  zstd               conda-forge/linux-64::zstd-1.5.5-hfc55251_0



Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: /
SafetyError: The package for checkm-genome located at /home/apps/conda/miniconda3/pkgs/checkm-genome-1.1.3-py_1
appears to be corrupted. The path 'site-packages/checkm/DATA_CONFIG'
has an incorrect size.
  reported size: 215 bytes
  actual size: 247 bytes

ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::cuda-cudart-11.8.89-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcudart.so.11.0'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::cuda-cupti-11.8.87-0, nvidia/linux-64::cuda-nvtx-11.8.86-0
  path: 'LICENSE'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::cuda-cupti-11.8.87-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcupti.so'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::cuda-nvrtc-11.8.89-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnvrtc.so.11.2'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::cuda-nvtx-11.8.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnvToolsExt.so'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::cuda-nvtx-11.8.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnvToolsExt.so.1'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::cuda-nvtx-11.8.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnvToolsExt.so.1.0.0'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libcublas-11.11.3.6-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcublas.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libcublas-11.11.3.6-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcublasLt.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libcublas-11.11.3.6-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnvblas.so.11'
ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libcufft-10.9.0.58-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcufft.so.10'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libcufft-10.9.0.58-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcufftw.so.10'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libcurand-10.3.4.107-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcurand.so.10'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libcusolver-11.4.1.48-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcusolver.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libcusolver-11.4.1.48-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcusolverMg.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libcusparse-11.7.5.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libcusparse.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppc.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppial.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppicc.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppidei.so.11'

ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppif.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppig.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppim.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppist.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppisu.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnppitc.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnpp-11.8.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnpps.so.11'


ClobberError: This transaction has incompatible packages due to a shared path.
  packages: nvidia/linux-64::libnvjpeg-11.9.0.86-0, nvidia/linux-64::cudatoolkit-11.3.1-ha36c431_9
  path: 'lib/libnvjpeg.so.11'


done
Executing transaction: - By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.
html

| checkm data setRoot /home/apps/conda/miniconda3/envs/comebin-1.0.4/checkm_data

done
#
# To activate this environment, use
#
#     $ conda activate comebin-1.0.4
#
# To deactivate an active environment, use
#
#     $ conda deactivate

Checking the last mentioned file as a test:

$ conda activate comebin-1.0.4
$ ls -l $CONDA_PREFIX/lib/libnvjpeg.so*
lrwxrwxrwx 1 appadmin admin      23 Feb  2 11:28 /home/apps/conda/miniconda3/envs/comebin-1.0.4/lib/libnvjpeg.so -> libnvjpeg.so.11.5.0.109
lrwxrwxrwx 1 appadmin admin      23 Feb  2 11:28 /home/apps/conda/miniconda3/envs/comebin-1.0.4/lib/libnvjpeg.so.11 -> libnvjpeg.so.11.5.0.109
-rwxr-xr-x 3 appadmin admin 5170312 Jul  8  2021 /home/apps/conda/miniconda3/envs/comebin-1.0.4/lib/libnvjpeg.so.11.5.0.109
-rwxr-xr-x 5 appadmin admin 5699080 Aug 26  2022 /home/apps/conda/miniconda3/envs/comebin-1.0.4/lib/libnvjpeg.so.11.9.0.86

It seems that these shared paths are links, but I was wondering if the error/warning messages could be circumvented or ignored.
Thanks for your input on this!

Error Feature Generation

Hi again,

after fixing the path issue I am getting a different error by now:

Traceback (most recent call last):
File "main.py", line 368, in
main()
File "main.py", line 288, in main
train_CLmodel(logger,args)
File "/COMEBin/COMEBin/train_CLmodel.py", line 235, in train_CLmodel
simclr.train_addpretrain(train_loader, dataset, namelist)
File "/COMEBin/COMEBin/simclr.py", line 299, in train_addpretrain
top1, top5 = accuracy(logits, labels, topk=(1, 5))
UnboundLocalError: local variable 'logits' referenced before assignment
Something went wrong with running training network. Exiting.

Is there a way to fix this issue?

Best,
tim

gen_cov_file.sh How to change the thread

(comebin_env) lizhihua@lizhihua-T640:/media/lizhihua/software/binning_method/COMEBin/COMEBin/scripts$ bash gen_cov_file.sh -a /media/lizhihua/data/LZLJ/GJ/GJ_all/final.contigs.fa -o /media/lizhihua/data/LZLJ/GJ/GJ_all/bamfiles /media/lizhihua/data/LZLJ/GJ/GJ_all/*.fastq -t 20

The thread is 20 but the result is -t 1 as below

[main] CMD: bwa mem -v 1 -t 1 /media/lizhihua/data/LZLJ/GJ/GJ_all/bamfiles/work_files/assembly.fa /media/lizhihua/data/LZLJ/GJ/GJ_all/GJall_1.fastq /media/lizhihua/data/LZLJ/GJ/GJ_all/GJall_2.fastq
[main] Real time: 108706.147 sec; CPU: 109901.352 sec

----- Sorting the GJall alignment file -----

[bam_sort_core] merging from 263 files and 1 in-memory blocks...

########################################################################################################################

The process of generating bam files finished!!!

########################################################################################################################

ComeBin stuck in the clustering step

Hi,

I am using ComeBin to recover MAGs from cow rumen metagenomic data.
In a few samples ComeBin is stuck in the clustering step for more than 5 days now.
It generated quickly a few Leiden_*.tsv files but nothing else since then.

Could you investigate this? Your help would be very appreciated.

I can send you all data.

Best Florian.

Is it for the individual-or co- assembly?

Hi Ziye,

I am going to try the COMEBin v1.0.3 to bin my contigs, but is it for the individual-or co- assembly?

Looking forward your reply.

Thank you very much.

Best,

Bing

Augmentation threads

Regardless of the threads specified, the augmentation step only runs on a single thread. Is their any way to alter this? Since, it can take multiple days when dealing with sample sets with large .bam files.

Thread usage in cluster.py

Hello! I'm having an issue in the clustering step of version v1.0.4:
Regardless of the value of -t, cluster.py always uses all available CPUs.

We may have found the problem:

In cluster.py:

line 66:
int_labels = p.add_items(features, labels_index)

and line 398:
ann_neighbor_indices, ann_distances = p.knn_query(norm_embeddings, max_edges+1)

both functions lack the argument num_threads, causing them to default to -1 (all available cores)

Hope this helps and thank you for the tool, I'm getting promising results!
Riccardo

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