micahvista / mamnet

MAMnet uses a deep learning network to call genetic variants from third generation DNA sequencing data.

MAMnet

MAMnet is a long read based structural variant caller uses deep learning network.

MAMnet is able to detect and genotype deletions and insertions with fast running speed.

Installation

#Install from github (requires Python 3.6.* or newer): installs all dependencies except those necessary for read alignment (ngmlr, minimap2, samtools)
git clone https://github.com/micahvista/MAMnet.git
cd MAMnet

Dependencies

• tensorflow>=2.3.0
• pandas
• numpy
• pysam
• numba
• scipy

Input

MAMnet takes sorted and indexed alignment files in BAM format as inputs. And MAMnet has been successfully tested on PacBio CLR, PacBio HiFi (CCS) and Oxford Nanopore data and alignment files produced by the read aligners minimap2 <https://github.com/lh3/minimap2>, pbmm2 <https://github.com/PacificBiosciences/pbmm2/> , NGMLR <https://github.com/philres/ngmlr>_, and BWA-MEM.

Output

MAMnet produces SV calls in the Variant Call Format (VCF).

Usage

python MAMnet.py -bamfilepath ./HG002_PB_70x_RG_HP10XtrioRTG.bam -workdir ./workdir -outputpath ./variants.vcf -threads 16 -step 50 -includecontig [1,2,3,4]

#-bamfilepath the inputs path of sort and index bam file, the bam file should has MD tag which can be compuated by samtools calmd...
#-workdir the work path of MAMnet to store temporary data
#-outputpath the output path of called vcf file
#-threads the number of threads to use. (default: all available thread)
#-step data shift size [1-200]. (default: 50)
#-includecontig the list of contig to preform detection. (default: [], all contig are used)

Changelog

Contact

If you experience any problems or have suggestions please create an issue or a pull request.

Citation

License

The project is licensed under the GNU General Public License.

mamnet's Issues

More specified dependency information

Hi, I am trying to test your tool in my environment, but have encountered several problems regarding the package versions.
I first tried to run MAMnet with python3.8, numpy1.21, tensorflow2.4.1, but get the following error:

Traceback (most recent call last):
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
    self.run()
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "/data/maiziezhou_lab/Yichen/Softwares/MAMnet/MAMnet.py", line 431, in call_sv
    model = main_model(mc)
  File "/data/maiziezhou_lab/Yichen/Softwares/MAMnet/MAMnet.py", line 142, in main_model
    encoded_frames = layers.Bidirectional(tf.keras.layers.LSTM(256, return_sequences= True))(encoded_frames)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/wrappers.py", line 539, in __call__
    return super(Bidirectional, self).__call__(inputs, **kwargs)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 951, in __call__
    return self._functional_construction_call(inputs, args, kwargs,
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 1090, in _functional_construction_call
    outputs = self._keras_tensor_symbolic_call(
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 822, in _keras_tensor_symbolic_call
    return self._infer_output_signature(inputs, args, kwargs, input_masks)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 863, in _infer_output_signature
    outputs = call_fn(inputs, *args, **kwargs)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/wrappers.py", line 652, in call
    y = self.forward_layer(forward_inputs,
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 660, in __call__
    return super(RNN, self).__call__(inputs, **kwargs)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 1012, in __call__
    outputs = call_fn(inputs, *args, **kwargs)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent_v2.py", line 1157, in call
    inputs, initial_state, _ = self._process_inputs(inputs, initial_state, None)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 859, in _process_inputs
    initial_state = self.get_initial_state(inputs)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 642, in get_initial_state
    init_state = get_initial_state_fn(
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 2506, in get_initial_state
    return list(_generate_zero_filled_state_for_cell(
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 2987, in _generate_zero_filled_state_for_cell
    return _generate_zero_filled_state(batch_size, cell.state_size, dtype)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 3003, in _generate_zero_filled_state
    return nest.map_structure(create_zeros, state_size)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/util/nest.py", line 659, in map_structure
    structure[0], [func(*x) for x in entries],
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/util/nest.py", line 659, in <listcomp>
    structure[0], [func(*x) for x in entries],
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 3000, in create_zeros
    return array_ops.zeros(init_state_size, dtype=dtype)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
    return target(*args, **kwargs)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 2819, in wrapped
    tensor = fun(*args, **kwargs)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 2868, in zeros
    output = _constant_if_small(zero, shape, dtype, name)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 2804, in _constant_if_small
    if np.prod(shape) < 1000:
  File "<__array_function__ internals>", line 5, in prod
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 3051, in prod
    return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 86, in _wrapreduction
    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
  File "/home/liuy120/.conda/envs/MAMnet/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 852, in __array__
    raise NotImplementedError(
NotImplementedError: Cannot convert a symbolic Tensor (bidirectional/forward_lstm/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported

some answers said that this could be solved by downgrading numpy to 1.19, however, when running with numpy 1.19, another error information popped out:

Traceback (most recent call last):
  File "/data/maiziezhou_lab/Yichen/Softwares/MAMnet/MAMnet.py", line 240, in <module>
    import mamnet
ImportError: numpy.core.multiarray failed to import

I've followed the dependencies instruction provided in your readme, but it seems that the version information is not sufficient for users to easily install MAMnet. Would you please make it more specific?

Best,
Yichen

pyalign.py file

Hi,

I have a question about pyalign.py file. what is the application or usage of pyalign.py file in MAMnet?

Thanks,

Is there any way to output REF and ALT sequence?

Hi, after running MAMnet, I got a vcf with the following content:

...
#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO	FORMAT	.
chr1	32028	.	.	.	6	PASS	SVLEN=64;SVTYPE=INS;END=32029;RE=6	GT	1/1
chr1	90367	.	.	.	2	PASS	SVLEN=59;SVTYPE=INS;END=90368;RE=2	GT	1/1
chr1	384542	.	.	.	2	PASS	SVLEN=307;SVTYPE=INS;END=384543;RE=2	GT	1/1
chr1	385987	.	.	.	2	PASS	SVLEN=308;SVTYPE=INS;END=385988;RE=2	GT	1/1
chr1	546019	.	.	.	20	PASS	SVLEN=204;SVTYPE=INS;END=546020;RE=20	GT	1/1
chr1	605052	.	.	.	20	PASS	SVLEN=230;SVTYPE=INS;END=605053;RE=20	GT	1/1
chr1	649756	.	.	.	2	PASS	SVLEN=-50;SVTYPE=DEL;END=649806;RE=2	GT	1/1
...

Is there any parameters for MAMnet to output REF and ALT sequence?

Deadlock in multiproccessing

Hi, I am currently testing MAMnet and have got it working. Unfortunatelly, it gets stuck in the while loop ( line 643 ), and waits for childprocesses that dont seem to exist or never finish. Therefore, the calculated result is never written to a vcf file.

I encountered this while using -threads 1 and - threads >=2 and it seems to be a problem of the multiprocessing code.

Another thing that I noticed is when killing the process with ctr-C it terminates, but there is still another process running and I have to kill it by addressing it with kill pid and passing the exact process id.

Thats the error that appears after terminating.

I am using a small test bam file containing only chr9 data with calculated MD-tags with the default parameter of MAMnet on a linux -Ubuntu x86_64 server and MAMnet in a conda environment.

The only thing I altered without success regarding the multiprocessing documentation is adding the if __name__ == '__main__': line in MAMnet.py

Failed to find any matching files for ./type

Hi,
Thank you very much for adding the version information! However, a new error popped out saying:

RuntimeError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for ./type

Strangely, MAMnet kept running after this error popped out. Do you have any idea about this error? By the way, MAMnet has been running for about 4 days (not finished yet) on a 56x HiFi bam file, which is unusually long (other SV calling tools typically finish in 12h). Is it because the error mentioned above stuck the pipeline?

Any test data?

Hi!

It would be great to have some test data corresponding to the example code in the repository so to test if the tool runs correctly and to be able to thoroughly check the format of input data.

Thanks! :)

Valentina Peona

MD tags with Nanopore data

Hi Hongyu Ding,
I was succesgfully working with MAMnet on small training data. I moved on to use the GIAB OxfordNanopore data. You stated in your README that u tested with that data and the minimap2 aligner. Unfotunately, I ran inbto the following Problem:

I calculated the missing MD tags for the nanopore data with samtools calmd but the program was not able to calculate the MD tags for secondary alignments (flag 256 in Bam-file), because it was missing the SEQ, QUAL columns for those reads. Hence, MAMnet was throwing an error and the program aborted.

Since you were stating you where able to use OxfordNanopore data, what data did you use and how did you use it? Is there a workaround? Can MAMnet handle those errors without crashing?
I tried remapping with minimap2 and the -secondary-seq flag which was not giving the promised results unfortunately.

Bam file data from GIAB

Segmentation fault

Dear professor:
I am currently using MAMnet for my project, but I have encountered an issue that I am unable to resolve. Specifically, the process always fails with a "Segmentation fault" error. Below is the MAMnet command I used for execution:

python ./MAMnet.py -bamfilepath */WZS.sort.bam -threads 1 -step 50 -INTERVAL 1e7 -genotype True -workdir ./workdir -SV_weightspath ./MAMnet/type -genotype_weightspath ./MAMnet/geno -outputpath */02.WZS/WZS_mamnet.vcf

In addition to the segmentation fault, I am also receiving the following error message:

NotImplementedError: Cannot convert a symbolic Tensor (bidirectional/forward_lstm/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported.

I am unsure if these issues are related or if the second error is affecting the execution of my command. I would greatly appreciate your expertise and guidance on how to resolve these errors.
Thank you for your time and assistance.
Best regards,
Flooooooooooooower

Segmentation fault while running MAMnet with slurm

When running MAMnet with slurm job manager, it sometimes fails with the error Segmentation fault, but sometimes works well on other bam file. (All bam files can be called correctly with other SV callers)

The MAMnet command I used is:

python ${MAMnetPath}/MAMnet.py -bamfilepath ${bam} -threads 20 -step 50 -INTERVAL 1e7 -genotype True -workdir ${work_dir} -SV_weightspath ${MAMnetPath}/type -genotype_weightspath ${MAMnetPath}/geno -outputpath ${output}

The bam file is generated from human genome ONT reads, and the coverage is around 10x.

Would you please give me some suggestions about this issue?

The complete error log is:

2023-02-10 14:22:01.737917: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2023-02-10 14:26:18.452917: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2023-02-10 14:26:18.455260: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2023-02-10 14:26:18.491379: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
pciBusID: 0000:3b:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2023-02-10 14:26:18.491468: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2023-02-10 14:26:20.455815: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2023-02-10 14:26:20.455988: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10
2023-02-10 14:26:28.632020: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2023-02-10 14:26:37.928679: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2023-02-10 14:26:49.623876: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2023-02-10 14:26:49.826945: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10
2023-02-10 14:26:57.359726: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7
2023-02-10 14:26:57.373221: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2023-02-10 14:26:57.433696: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-02-10 14:26:57.435454: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2023-02-10 14:26:57.438896: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
pciBusID: 0000:3b:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5
coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s
2023-02-10 14:26:57.439012: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2023-02-10 14:26:57.439100: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2023-02-10 14:26:57.439161: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10
2023-02-10 14:26:57.439220: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2023-02-10 14:26:57.439279: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2023-02-10 14:26:57.439335: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2023-02-10 14:26:57.439393: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10
2023-02-10 14:26:57.439451: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7
2023-02-10 14:26:57.444975: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2023-02-10 14:26:57.454605: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2023-02-10 14:30:41.554191: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-02-10 14:30:41.555194: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 
2023-02-10 14:30:41.555358: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N 
2023-02-10 14:30:47.180432: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10074 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:3b:00.0, compute capability: 7.5)
2023-02-10 14:53:46.612038: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2023-02-10 14:53:48.612236: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2300000000 Hz
2023-02-10 14:53:57.108128: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2023-02-10 14:54:35.100335: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7
/var/spool/slurmd.gpu0038/job49507205/slurm_script: line 43: 54825 Segmentation fault      python ${MAMnetPath}/MAMnet.py -bamfilepath ${bam} -threads 20 -step 50 -INTERVAL 1e7 -genotype True -workdir ${work_dir} -SV_weightspath ${MAMnetPath}/type -genotype_weightspath ${MAMnetPath}/geno -outputpath ${output}

my environment is:

_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       2_gnu    conda-forge
_tflow_select             2.1.0                       gpu  
abseil-cpp                20211102.0           h27087fc_1    conda-forge
absl-py                   1.1.0              pyhd8ed1ab_0    conda-forge
aiohttp                   3.8.1            py38h0a891b7_1    conda-forge
aiosignal                 1.2.0              pyhd8ed1ab_0    conda-forge
alsa-lib                  1.2.6.1              h7f98852_0    conda-forge
astor                     0.8.1              pyh9f0ad1d_0    conda-forge
astunparse                1.6.3              pyhd8ed1ab_0    conda-forge
async-timeout             4.0.2              pyhd8ed1ab_0    conda-forge
attrs                     21.4.0             pyhd8ed1ab_0    conda-forge
blinker                   1.4                        py_1    conda-forge
brotli                    1.0.9                h166bdaf_7    conda-forge
brotli-bin                1.0.9                h166bdaf_7    conda-forge
brotlipy                  0.7.0           py38h0a891b7_1004    conda-forge
bzip2                     1.0.8                h7f98852_4    conda-forge
c-ares                    1.18.1               h7f98852_0    conda-forge
ca-certificates           2022.6.15            ha878542_0    conda-forge
cachetools                4.2.4              pyhd8ed1ab_0    conda-forge
certifi                   2022.6.15        py38h578d9bd_0    conda-forge
cffi                      1.15.0           py38h3931269_0    conda-forge
charset-normalizer        2.0.12             pyhd8ed1ab_0    conda-forge
click                     8.1.3            py38h578d9bd_0    conda-forge
cryptography              37.0.1           py38h9ce1e76_0  
cudatoolkit               10.1.243            h8cb64d8_10    conda-forge
cudatoolkit-dev           10.1.243             h516909a_3    conda-forge
cudnn                     7.6.5.32             hc0a50b0_1    conda-forge
cupti                     10.1.168                      0  
cycler                    0.11.0             pyhd8ed1ab_0    conda-forge
dbus                      1.13.6               h5008d03_3    conda-forge
expat                     2.4.8                h27087fc_0    conda-forge
font-ttf-dejavu-sans-mono 2.37                 hab24e00_0    conda-forge
font-ttf-inconsolata      3.000                h77eed37_0    conda-forge
font-ttf-source-code-pro  2.038                h77eed37_0    conda-forge
font-ttf-ubuntu           0.83                 hab24e00_0    conda-forge
fontconfig                2.14.0               h8e229c2_0    conda-forge
fonts-conda-ecosystem     1                             0    conda-forge
fonts-conda-forge         1                             0    conda-forge
fonttools                 4.33.3           py38h0a891b7_0    conda-forge
freetype                  2.10.4               h0708190_1    conda-forge
frozenlist                1.3.0            py38h0a891b7_1    conda-forge
gast                      0.4.0              pyh9f0ad1d_0    conda-forge
gettext                   0.19.8.1          h73d1719_1008    conda-forge
giflib                    5.2.1                h36c2ea0_2    conda-forge
glib                      2.70.2               h780b84a_4    conda-forge
glib-tools                2.70.2               h780b84a_4    conda-forge
google-auth               1.35.0             pyh6c4a22f_0    conda-forge
google-auth-oauthlib      0.4.6              pyhd8ed1ab_0    conda-forge
google-pasta              0.2.0              pyh8c360ce_0    conda-forge
grpc-cpp                  1.46.3               hc275302_1    conda-forge
grpcio                    1.46.3           py38hb6c94e9_1    conda-forge
gst-plugins-base          1.20.3               hf6a322e_0    conda-forge
gstreamer                 1.20.3               hd4edc92_0    conda-forge
h5py                      2.10.0          nompi_py38h513d04c_102    conda-forge
hdf5                      1.10.5          nompi_h5b725eb_1114    conda-forge
icu                       69.1                 h9c3ff4c_0    conda-forge
idna                      3.3                pyhd8ed1ab_0    conda-forge
importlib-metadata        4.11.4           py38h578d9bd_0    conda-forge
jpeg                      9e                   h166bdaf_1    conda-forge
keras-preprocessing       1.1.2              pyhd8ed1ab_0    conda-forge
keyutils                  1.6.1                h166bdaf_0    conda-forge
kiwisolver                1.4.3            py38h43d8883_0    conda-forge
krb5                      1.19.3               h3790be6_0    conda-forge
lcms2                     2.12                 hddcbb42_0    conda-forge
ld_impl_linux-64          2.36.1               hea4e1c9_2    conda-forge
lerc                      3.0                  h9c3ff4c_0    conda-forge
libblas                   3.9.0           15_linux64_openblas    conda-forge
libbrotlicommon           1.0.9                h166bdaf_7    conda-forge
libbrotlidec              1.0.9                h166bdaf_7    conda-forge
libbrotlienc              1.0.9                h166bdaf_7    conda-forge
libcblas                  3.9.0           15_linux64_openblas    conda-forge
libclang                  13.0.1          default_hc23dcda_0    conda-forge
libcurl                   7.83.1               h7bff187_0    conda-forge
libdeflate                1.10                 h7f98852_0    conda-forge
libedit                   3.1.20191231         he28a2e2_2    conda-forge
libev                     4.33                 h516909a_1    conda-forge
libevent                  2.1.10               h9b69904_4    conda-forge
libffi                    3.4.2                h7f98852_5    conda-forge
libgcc-ng                 12.1.0              h8d9b700_16    conda-forge
libgfortran-ng            12.1.0              h69a702a_16    conda-forge
libgfortran5              12.1.0              hdcd56e2_16    conda-forge
libglib                   2.70.2               h174f98d_4    conda-forge
libgomp                   12.1.0              h8d9b700_16    conda-forge
libiconv                  1.16                 h516909a_0    conda-forge
liblapack                 3.9.0           15_linux64_openblas    conda-forge
libllvm13                 13.0.1               hf817b99_2    conda-forge
libnghttp2                1.47.0               h727a467_0    conda-forge
libnsl                    2.0.0                h7f98852_0    conda-forge
libogg                    1.3.4                h7f98852_1    conda-forge
libopenblas               0.3.20          pthreads_h78a6416_0    conda-forge
libopus                   1.3.1                h7f98852_1    conda-forge
libpng                    1.6.37               h21135ba_2    conda-forge
libpq                     14.4                 hd77ab85_0    conda-forge
libprotobuf               3.20.1               h6239696_0    conda-forge
libssh2                   1.10.0               ha56f1ee_2    conda-forge
libstdcxx-ng              12.1.0              ha89aaad_16    conda-forge
libtiff                   4.4.0                h0fcbabc_0    conda-forge
libuuid                   2.32.1            h7f98852_1000    conda-forge
libvorbis                 1.3.7                h9c3ff4c_0    conda-forge
libwebp                   1.2.2                h3452ae3_0    conda-forge
libwebp-base              1.2.2                h7f98852_1    conda-forge
libxcb                    1.13              h7f98852_1004    conda-forge
libxkbcommon              1.0.3                he3ba5ed_0    conda-forge
libxml2                   2.9.12               h885dcf4_1    conda-forge
libzlib                   1.2.12               h166bdaf_1    conda-forge
llvmlite                  0.38.1                    <pip>
lz4-c                     1.9.3                h9c3ff4c_1    conda-forge
markdown                  3.3.7              pyhd8ed1ab_0    conda-forge
matplotlib                3.5.1            py38h578d9bd_0    conda-forge
matplotlib-base           3.5.1            py38hf4fb855_0    conda-forge
multidict                 6.0.2            py38h0a891b7_1    conda-forge
munkres                   1.1.4              pyh9f0ad1d_0    conda-forge
mysql-common              8.0.29               haf5c9bc_1    conda-forge
mysql-libs                8.0.29               h28c427c_1    conda-forge
ncurses                   6.3                  h27087fc_1    conda-forge
nspr                      4.32                 h9c3ff4c_1    conda-forge
nss                       3.78                 h2350873_0    conda-forge
numba                     0.55.2                    <pip>
numpy                     1.19.5           py38h8246c76_3    conda-forge
oauthlib                  3.2.0              pyhd8ed1ab_0    conda-forge
openjpeg                  2.4.0                hb52868f_1    conda-forge
openssl                   1.1.1o               h166bdaf_0    conda-forge
opt_einsum                3.3.0              pyhd8ed1ab_1    conda-forge
packaging                 21.3               pyhd8ed1ab_0    conda-forge
pandas                    1.4.2            py38h47df419_2    conda-forge
pcre                      8.45                 h9c3ff4c_0    conda-forge
pillow                    9.1.1            py38h0ee0e06_1    conda-forge
pip                       22.1.2             pyhd8ed1ab_0    conda-forge
protobuf                  3.20.1           py38hfa26641_0    conda-forge
pthread-stubs             0.4               h36c2ea0_1001    conda-forge
pyasn1                    0.4.8                      py_0    conda-forge
pyasn1-modules            0.2.7                      py_0    conda-forge
pycparser                 2.21               pyhd8ed1ab_0    conda-forge
pyjwt                     2.4.0              pyhd8ed1ab_0    conda-forge
pyopenssl                 22.0.0             pyhd8ed1ab_0    conda-forge
pyparsing                 3.0.9              pyhd8ed1ab_0    conda-forge
pyqt                      5.12.3           py38ha8c2ead_4    conda-forge
pysam                     0.19.0           py38h8bf8b8d_0    bioconda
pysocks                   1.7.1            py38h578d9bd_5    conda-forge
python                    3.8.13          h582c2e5_0_cpython    conda-forge
python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge
python-flatbuffers        2.0                pyhd8ed1ab_0    conda-forge
python_abi                3.8                      2_cp38    conda-forge
pytz                      2022.1             pyhd8ed1ab_0    conda-forge
pyu2f                     0.1.5              pyhd8ed1ab_0    conda-forge
qt                        5.12.9               h1304e3e_6    conda-forge
re2                       2022.04.01           h27087fc_0    conda-forge
readline                  8.1.2                h0f457ee_0    conda-forge
requests                  2.28.0             pyhd8ed1ab_0    conda-forge
requests-oauthlib         1.3.1              pyhd8ed1ab_0    conda-forge
rsa                       4.8                pyhd8ed1ab_0    conda-forge
scipy                     1.8.1            py38h1ee437e_0    conda-forge
setuptools                62.6.0           py38h578d9bd_0    conda-forge
six                       1.16.0             pyh6c4a22f_0    conda-forge
sqlite                    3.38.5               h4ff8645_0    conda-forge
tensorboard               2.4.1              pyhd8ed1ab_1    conda-forge
tensorboard-plugin-wit    1.8.1              pyhd8ed1ab_0    conda-forge
tensorflow                2.4.1           gpu_py38h8a7d6ce_0  
tensorflow-base           2.4.1           gpu_py38h29c2da4_0  
tensorflow-estimator      2.6.0            py38h709712a_0    conda-forge
tensorflow-gpu            2.4.1                h30adc30_0  
termcolor                 1.1.0                      py_2    conda-forge
tk                        8.6.12               h27826a3_0    conda-forge
tornado                   6.1              py38h0a891b7_3    conda-forge
typing-extensions         4.2.0                hd8ed1ab_1    conda-forge
typing_extensions         4.2.0              pyha770c72_1    conda-forge
unicodedata2              14.0.0           py38h0a891b7_1    conda-forge
urllib3                   1.26.9             pyhd8ed1ab_0    conda-forge
werkzeug                  2.1.2              pyhd8ed1ab_1    conda-forge
wheel                     0.37.1             pyhd8ed1ab_0    conda-forge
wrapt                     1.14.1           py38h0a891b7_0    conda-forge
xorg-libxau               1.0.9                h7f98852_0    conda-forge
xorg-libxdmcp             1.1.3                h7f98852_0    conda-forge
xz                        5.2.5                h516909a_1    conda-forge
yarl                      1.7.2            py38h0a891b7_2    conda-forge
zipp                      3.8.0              pyhd8ed1ab_0    conda-forge
zlib                      1.2.12               h166bdaf_1    conda-forge
zstd                      1.5.2                h8a70e8d_1    conda-forge

argument: -outputpath does not work as expected

MAMnet use this to generate the output vcf name:

outputpath = bamfilepath.split('/')[-1][:-4] + '.vcf'

the argument -outputpath does not change the name of the output vcf name