absplice's Issues
Can't find cause of the error
AbSplice was launched with the command from tutorial python -m snakemake -j 1 --use-conda from the AbSplice container, installed strictly by instructions in READ.md
End of the output:
[Fri Feb 9 17:37:54 2024]
Error in rule mmsplice_splicemap:
jobid: 8
input: ../data/resources/analysis_files/vcf_files/DIV_train_all_annotated.vcf.gz, ../data/resources/downloaded_files/GRCh38.primary_assembly.genome.fa, ../data/resources/downloaded_files/splicemap_hg38/Adipose_Subcutaneous_splicemap_psi5_method=kn_event_filter=median_cutoff.csv.gz, ../data/resources/downloaded_files/splicemap_hg38/Adipose_Subcutaneous_splicemap_psi3_method=kn_event_filter=median_cutoff.csv.gz
output: ../data/results/hg38/model_scores_from_absplice_features/DIV_train_all_annotated.vcf.gz_MMSplice_SpliceMap.csv
Shutting down, this might take some time.
Exiting because a job execution failed. Look above for error message
Complete
I think that the VCF file can be the cause of the problem(despite successful annotation by SpliceAI and SPiP). It's attached below (modified extension to txt to upload it on github).
DIV_train_all_annotated.txt
Can you help me to find the cause of the error? Thank you!
can't install, stop at "Solving environment: |"
conda env create -f environment.yaml
stop at
"Solving environment: |"
Numpy Error appears in Example-Workflow-Execution
Hey guys, thx very much for your tool!
I just wanted to try it out, because it seems really interesting.
However, I stumbled over the following error when I tried to execute the example workflow:
AttributeError: module 'numpy' has no attribute 'object'.
Here the whole output:
Building DAG of jobs...
Creating conda environment ../envs/environment_spliceai_rocksdb.yaml...
Downloading and installing remote packages.
Environment for /home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/splicing_pred/DNA/../../../envs/environment_spliceai_rocksdb.yaml created (location: .snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_)
Using shell: /usr/bin/bash
Provided cores: 1 (use --cores to define parallelism)
Rules claiming more threads will be scaled down.
Job stats:
job count min threads max threads
-------------------- ------- ------------- -------------
absplice_dna 1 1 1
all 1 1 1
download_human_fasta 1 1 1
download_splicemaps 1 1 1
mmsplice_splicemap 1 1 1
spliceai 1 1 1
spliceai_vcf_to_csv 1 1 1
total 7 1 1
Select jobs to execute...
[Fri May 24 12:42:46 2024]
rule download_splicemaps:
output: ../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi3_method=kn_event_filter=median_cutoff.csv.gz, ../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi5_method=kn_event_filter=median_cutoff.csv.gz
jobid: 2
reason: Missing output files: ../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi3_method=kn_event_filter=median_cutoff.csv.gz, ../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi5_method=kn_event_filter=median_cutoff.csv.gz
wildcards: genome=hg19, tissue=Brain_Cortex
resources: tmpdir=/tmp
Downloading SpliceMaps...
0%| | 0/98 [00:00<?, ?it/s]../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi5_method=kn_event_filter=median_cutoff.csv.gz
12%|██████████████▎ | 12/98 [00:02<00:20, 4.26it/s]../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi3_method=kn_event_filter=median_cutoff.csv.gz
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 98/98 [00:06<00:00, 16.23it/s]
[Fri May 24 12:42:54 2024]
Finished job 2.
1 of 7 steps (14%) done
Select jobs to execute...
[Fri May 24 12:42:54 2024]
rule download_human_fasta:
output: ../data/resources/downloaded_files/GRCh37.primary_assembly.genome.fa
jobid: 1
reason: Missing output files: ../data/resources/downloaded_files/GRCh37.primary_assembly.genome.fa
resources: tmpdir=/tmp
--2024-05-24 12:42:54-- https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_39/GRCh37_mapping/GRCh37.primary_assembly.genome.fa.gz
Auflösen des Hostnamens proxy.charite.de (proxy.charite.de)… 141.42.5.215
Verbindungsaufbau zu proxy.charite.de (proxy.charite.de)|141.42.5.215|:8080 … verbunden.
Proxy-Anforderung gesendet, auf Antwort wird gewartet … 200 OK
Länge: 869924494 (830M) [application/x-gzip]
Wird in »STDOUT« gespeichert.
- 34%[========================> ] 286,27M 1,99MB/s ETA- 34%[=======================> ] 286,33M 1,9- - 100%[===============================================================>] 829,62M 1,99MB/s in 7m 3s
2024-05-24 12:49:57 (1,96 MB/s) - auf die Standardausgabe geschrieben [869924494/869924494]
[Fri May 24 12:49:57 2024]
Finished job 1.
2 of 7 steps (29%) done
Select jobs to execute...
[Fri May 24 12:49:57 2024]
rule mmsplice_splicemap:
input: ../data/resources/analysis_files/vcf_files/example_hg19.vcf.gz, ../data/resources/downloaded_files/GRCh37.primary_assembly.genome.fa, ../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi5_method=kn_event_filter=median_cutoff.csv.gz, ../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi3_method=kn_event_filter=median_cutoff.csv.gz
output: ../data/results/hg19/model_scores_from_absplice_features/example_hg19.vcf.gz_MMSplice_SpliceMap.csv
jobid: 4
reason: Missing output files: ../data/results/hg19/model_scores_from_absplice_features/example_hg19.vcf.gz_MMSplice_SpliceMap.csv; Input files updated by another job: ../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi3_method=kn_event_filter=median_cutoff.csv.gz, ../data/resources/downloaded_files/GRCh37.primary_assembly.genome.fa, ../data/resources/downloaded_files/splicemap_hg19/Brain_Cortex_splicemap_psi5_method=kn_event_filter=median_cutoff.csv.gz
wildcards: genome=hg19, vcf_id=example_hg19.vcf.gz
resources: tmpdir=/tmp, mem_mb=32000, mem_mib=30518, threads=4
285750it [00:00, 353311.71it/s]
284409it [00:00, 452003.67it/s]
2024-05-24 12:50:25.336222: 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.
0it [00:00, ?it/s]2024-05-24 12:50:25.684759: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2024-05-24 12:50:25.697128: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2400000000 Hz
WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f2ad8583310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.
1it [00:00, 1.62it/s]
[Fri May 24 12:50:27 2024]
Finished job 4.
3 of 7 steps (43%) done
Select jobs to execute...
[Fri May 24 12:50:27 2024]
rule spliceai:
input: ../data/resources/analysis_files/vcf_files/example_hg19.vcf.gz, ../data/resources/downloaded_files/GRCh37.primary_assembly.genome.fa
output: ../data/results/hg19/model_scores_from_absplice_features/example_hg19.vcf.gz_SpliceAI.vcf
jobid: 6
reason: Missing output files: ../data/results/hg19/model_scores_from_absplice_features/example_hg19.vcf.gz_SpliceAI.vcf; Input files updated by another job: ../data/resources/downloaded_files/GRCh37.primary_assembly.genome.fa
wildcards: genome=hg19, vcf_id=example_hg19.vcf.gz
resources: tmpdir=/tmp, mem_mb=16000, mem_mib=15259, threads=1, gpu=1
Activating conda environment: .snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_
/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/framework/dtypes.py:585: FutureWarning: In the future `np.object` will be defined as the corresponding NumPy scalar.
np.object,
Traceback (most recent call last):
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/bin/spliceai", line 7, in <module>
from spliceai.__main__ import main
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/spliceai/__main__.py", line 5, in <module>
from spliceai.utils import Annotator, get_delta_scores
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/spliceai/utils.py", line 6, in <module>
from keras.models import load_model
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/keras/__init__.py", line 21, in <module>
from tensorflow.python import tf2
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/__init__.py", line 41, in <module>
from tensorflow.python.tools import module_util as _module_util
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/__init__.py", line 46, in <module>
from tensorflow.python import data
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/data/__init__.py", line 25, in <module>
from tensorflow.python.data import experimental
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/data/experimental/__init__.py", line 97, in <module>
from tensorflow.python.data.experimental import service
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/data/experimental/service/__init__.py", line 353, in <module>
from tensorflow.python.data.experimental.ops.data_service_ops import distribute
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/data/experimental/ops/data_service_ops.py", line 26, in <module>
from tensorflow.python.data.experimental.ops import compression_ops
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/data/experimental/ops/compression_ops.py", line 20, in <module>
from tensorflow.python.data.util import structure
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/data/util/structure.py", line 26, in <module>
from tensorflow.python.data.util import nest
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/data/util/nest.py", line 40, in <module>
from tensorflow.python.framework import sparse_tensor as _sparse_tensor
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/framework/sparse_tensor.py", line 28, in <module>
from tensorflow.python.framework import constant_op
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/framework/constant_op.py", line 29, in <module>
from tensorflow.python.eager import execute
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/eager/execute.py", line 27, in <module>
from tensorflow.python.framework import dtypes
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/tensorflow/python/framework/dtypes.py", line 585, in <module>
np.object,
File "/home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_/lib/python3.9/site-packages/numpy/__init__.py", line 324, in __getattr__
raise AttributeError(__former_attrs__[attr])
AttributeError: module 'numpy' has no attribute 'object'.
`np.object` was a deprecated alias for the builtin `object`. To avoid this error in existing code, use `object` by itself. Doing this will not modify any behavior and is safe.
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
[Fri May 24 12:50:27 2024]
Error in rule spliceai:
jobid: 6
input: ../data/resources/analysis_files/vcf_files/example_hg19.vcf.gz, ../data/resources/downloaded_files/GRCh37.primary_assembly.genome.fa
output: ../data/results/hg19/model_scores_from_absplice_features/example_hg19.vcf.gz_SpliceAI.vcf
conda-env: /home/kuechleo/splice-prediction-tools/absplice/absplice/example/workflow/.snakemake/conda/7b529f67ba9587a5b21fa83be7c2e50e_
shell:
spliceai -I ../data/resources/analysis_files/vcf_files/example_hg19.vcf.gz -O ../data/results/hg19/model_scores_from_absplice_features/example_hg19.vcf.gz_SpliceAI.vcf -R ../data/resources/downloaded_files/GRCh37.primary_assembly.genome.fa -A grch37
(one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)
Shutting down, this might take some time.
Exiting because a job execution failed. Look above for error message
Complete log: .snakemake/log/2024-05-24T123919.336725.snakemake.log
My set-up: I used the set-up optoin where one uses a Conda-Environment.
My Conda-Env:
conda list
# packages in environment at /home/kuechleo/miniforge3/envs/absplice_env:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
abseil-cpp 20210324.0 h9c3ff4c_0 conda-forge
absl-py 0.15.0 pypi_0 pypi
absplice 0.0.1 dev_0 <develop>
aioeasywebdav 2.4.0 py38h578d9bd_1001 conda-forge
aiohttp 3.9.5 py38h01eb140_0 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
amply 0.1.6 pyhd8ed1ab_0 conda-forge
appdirs 1.4.4 pyh9f0ad1d_0 conda-forge
argcomplete 3.3.0 pyhd8ed1ab_0 conda-forge
argh 0.31.2 pyhd8ed1ab_0 conda-forge
arrow 1.3.0 pyhd8ed1ab_0 conda-forge
arrow-cpp 4.0.0 py38ha12db41_1_cpu conda-forge
astor 0.8.1 pyh9f0ad1d_0 conda-forge
asttokens 2.4.1 pypi_0 pypi
astunparse 1.6.3 pyhd8ed1ab_0 conda-forge
async-timeout 4.0.3 pyhd8ed1ab_0 conda-forge
attmap 0.13.2 pyhd8ed1ab_0 conda-forge
attrs 23.2.0 pyh71513ae_0 conda-forge
aws-c-cal 0.5.11 h95a6274_0 conda-forge
aws-c-common 0.6.2 h7f98852_0 conda-forge
aws-c-event-stream 0.2.7 h3541f99_13 conda-forge
aws-c-io 0.10.5 hfb6a706_0 conda-forge
aws-checksums 0.1.11 ha31a3da_7 conda-forge
aws-sdk-cpp 1.8.186 hecaee15_4 conda-forge
backcall 0.2.0 pypi_0 pypi
bcrypt 4.1.3 py38h31a4407_0 conda-forge
binaryornot 0.4.4 py_1 conda-forge
biopython 1.83 py38h01eb140_0 conda-forge
blinker 1.8.2 pyhd8ed1ab_0 conda-forge
boto3 1.34.111 pyhd8ed1ab_0 conda-forge
botocore 1.34.112 pyge38_1234567_0 conda-forge
brotli 1.1.0 hd590300_1 conda-forge
brotli-bin 1.1.0 hd590300_1 conda-forge
brotli-python 1.1.0 py38h17151c0_1 conda-forge
bzip2 1.0.8 hd590300_5 conda-forge
c-ares 1.28.1 hd590300_0 conda-forge
ca-certificates 2024.2.2 hbcca054_0 conda-forge
cachetools 4.2.4 pyhd8ed1ab_0 conda-forge
certifi 2024.2.2 pyhd8ed1ab_0 conda-forge
cffi 1.14.4 py38ha312104_0 conda-forge
chardet 5.2.0 py38h578d9bd_1 conda-forge
charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge
click 8.1.7 unix_pyh707e725_0 conda-forge
cloudpickle 3.0.0 pypi_0 pypi
coin-or-cbc 2.10.10 h9002f0b_0 conda-forge
coin-or-cgl 0.60.7 h516709c_0 conda-forge
coin-or-clp 1.17.8 h1ee7a9c_0 conda-forge
coin-or-osi 0.108.10 haf5fa05_0 conda-forge
coin-or-utils 2.11.11 hee58242_0 conda-forge
coincbc 2.10.10 0_metapackage conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
coloredlogs 15.0.1 pyhd8ed1ab_3 conda-forge
colorlog 6.8.2 py38h578d9bd_0 conda-forge
comm 0.2.2 pypi_0 pypi
commonmark 0.9.1 py_0 conda-forge
conda 4.12.0 py38h578d9bd_0 conda-forge
conda-package-handling 2.2.0 pyh38be061_0 conda-forge
conda-package-streaming 0.9.0 pyhd8ed1ab_0 conda-forge
configargparse 1.7 pyhd8ed1ab_0 conda-forge
connection_pool 0.0.3 pyhd3deb0d_0 conda-forge
contourpy 1.1.1 py38h7f3f72f_1 conda-forge
cookiecutter 2.6.0 pyhca7485f_0 conda-forge
cryptography 39.0.0 py38h1724139_0 conda-forge
cycler 0.12.1 pyhd8ed1ab_0 conda-forge
cython 3.0.10 py38h17151c0_0 conda-forge
cyvcf2 0.30.15 pypi_0 pypi
dash 2.10.2 pypi_0 pypi
dash-core-components 2.0.0 pypi_0 pypi
dash-cytoscape 1.0.1 pypi_0 pypi
dash-html-components 2.0.0 pypi_0 pypi
dash-table 5.0.0 pypi_0 pypi
dataclasses 0.8 pyhc8e2a94_3 conda-forge
datrie 0.8.2 py38h01eb140_7 conda-forge
debugpy 1.8.1 pypi_0 pypi
decorator 5.1.1 pypi_0 pypi
deepdiff 7.0.1 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
deprecation 2.1.0 pyh9f0ad1d_0 conda-forge
dill 0.3.8 pypi_0 pypi
docutils 0.20.1 py38h578d9bd_3 conda-forge
dpath 2.1.6 pyha770c72_0 conda-forge
dropbox 11.36.2 pyhd8ed1ab_0 conda-forge
enum34 1.1.10 py38h32f6830_2 conda-forge
exceptiongroup 1.2.0 pyhd8ed1ab_2 conda-forge
executing 2.0.1 pypi_0 pypi
fastbetabino3 0.0.1 pypi_0 pypi
filechunkio 1.8 py_2 conda-forge
filelock 3.14.0 pyhd8ed1ab_0 conda-forge
flask 2.2.5 pypi_0 pypi
fonttools 4.51.0 py38h01eb140_0 conda-forge
freetype 2.12.1 h267a509_2 conda-forge
frozenlist 1.4.1 py38h01eb140_0 conda-forge
ftputil 5.1.0 pyhd8ed1ab_0 conda-forge
future 1.0.0 pyhd8ed1ab_0 conda-forge
gast 0.3.3 py_0 conda-forge
gevent 24.2.1 pypi_0 pypi
gffutils 0.13 pyh7cba7a3_0 bioconda
gflags 2.2.2 he1b5a44_1004 conda-forge
giflib 5.2.2 hd590300_0 conda-forge
gitdb 4.0.11 pyhd8ed1ab_0 conda-forge
gitpython 3.1.43 pyhd8ed1ab_0 conda-forge
glog 0.4.0 h49b9bf7_3 conda-forge
google-api-core 2.10.0 pyhd8ed1ab_0 conda-forge
google-api-python-client 2.130.0 pyhd8ed1ab_0 conda-forge
google-auth 1.35.0 pyh6c4a22f_0 conda-forge
google-auth-httplib2 0.2.0 pyhd8ed1ab_0 conda-forge
google-auth-oauthlib 0.4.6 pyhd8ed1ab_0 conda-forge
google-cloud-core 2.3.1 pyhd8ed1ab_0 conda-forge
google-cloud-storage 2.11.0 pyh1a96a4e_0 conda-forge
google-crc32c 1.1.2 py38hf9d55a7_5 conda-forge
google-pasta 0.2.0 pyh8c360ce_0 conda-forge
google-resumable-media 2.7.0 pyhd8ed1ab_0 conda-forge
googleapis-common-protos 1.57.0 py38h578d9bd_0 conda-forge
greenlet 3.0.3 pypi_0 pypi
grpc-cpp 1.37.1 h36de60a_0 conda-forge
grpcio 1.37.1 py38hdd6454d_0 conda-forge
h5py 2.10.0 nompi_py38h9915d05_106 conda-forge
hdf5 1.10.6 nompi_h6a2412b_1114 conda-forge
htslib 1.17 h6bc39ce_1 bioconda
httplib2 0.22.0 pyhd8ed1ab_0 conda-forge
humanfriendly 10.0 pyhd8ed1ab_6 conda-forge
icu 68.2 h9c3ff4c_0 conda-forge
idna 3.7 pyhd8ed1ab_0 conda-forge
imageio 2.34.1 pypi_0 pypi
importlib-metadata 7.1.0 pyha770c72_0 conda-forge
importlib-resources 6.4.0 pyhd8ed1ab_0 conda-forge
importlib_resources 6.4.0 pyhd8ed1ab_0 conda-forge
iniconfig 2.0.0 pyhd8ed1ab_0 conda-forge
interpret 0.2.7 pypi_0 pypi
interpret-core 0.2.7 pypi_0 pypi
ipykernel 6.29.4 pypi_0 pypi
ipython 8.12.3 pypi_0 pypi
itsdangerous 2.2.0 pypi_0 pypi
jedi 0.19.1 pypi_0 pypi
jinja2 3.1.4 pyhd8ed1ab_0 conda-forge
jmespath 1.0.1 pyhd8ed1ab_0 conda-forge
joblib 1.4.2 pyhd8ed1ab_0 conda-forge
jpeg 9e h0b41bf4_3 conda-forge
jsonschema 4.22.0 pyhd8ed1ab_0 conda-forge
jsonschema-specifications 2023.12.1 pyhd8ed1ab_0 conda-forge
jupyter-client 8.6.2 pypi_0 pypi
jupyter_core 5.7.2 py38h578d9bd_0 conda-forge
keras 2.4.3 pyhd8ed1ab_0 conda-forge
keras-preprocessing 1.1.2 pyhd8ed1ab_0 conda-forge
keyutils 1.6.1 h166bdaf_0 conda-forge
kipoi 0.8.5 pyh5e36f6f_0 bioconda
kipoi-conda 0.1.6 py_0 bioconda
kipoi-utils 0.7.7 pyh7cba7a3_0 bioconda
kipoiseq 0.7.1 pyhdfd78af_0 bioconda
kiwisolver 1.4.5 py38h7f3f72f_1 conda-forge
krb5 1.20.1 hf9c8cef_0 conda-forge
lazy-loader 0.4 pypi_0 pypi
lcms2 2.12 hddcbb42_0 conda-forge
ld_impl_linux-64 2.40 h55db66e_0 conda-forge
libarchive 3.5.1 h3f442fb_1 conda-forge
libblas 3.9.0 20_linux64_openblas conda-forge
libbrotlicommon 1.1.0 hd590300_1 conda-forge
libbrotlidec 1.1.0 hd590300_1 conda-forge
libbrotlienc 1.1.0 hd590300_1 conda-forge
libcblas 3.9.0 20_linux64_openblas conda-forge
libcrc32c 1.1.2 h9c3ff4c_0 conda-forge
libcurl 7.87.0 h6312ad2_0 conda-forge
libdeflate 1.13 h166bdaf_0 conda-forge
libedit 3.1.20191231 he28a2e2_2 conda-forge
libev 4.33 hd590300_2 conda-forge
libevent 2.1.10 h9b69904_4 conda-forge
libffi 3.2.1 he1b5a44_1007 conda-forge
libgcc-ng 13.2.0 h77fa898_7 conda-forge
libgfortran-ng 13.2.0 h69a702a_7 conda-forge
libgfortran5 13.2.0 hca663fb_7 conda-forge
libgomp 13.2.0 h77fa898_7 conda-forge
libiconv 1.17 hd590300_2 conda-forge
liblapack 3.9.0 20_linux64_openblas conda-forge
liblapacke 3.9.0 20_linux64_openblas conda-forge
libnghttp2 1.51.0 hdcd2b5c_0 conda-forge
libopenblas 0.3.25 pthreads_h413a1c8_0 conda-forge
libpng 1.6.43 h2797004_0 conda-forge
libprotobuf 3.15.8 h780b84a_1 conda-forge
libsodium 1.0.18 h36c2ea0_1 conda-forge
libsolv 0.7.29 ha6fb4c9_0 conda-forge
libsqlite 3.45.3 h2797004_0 conda-forge
libssh2 1.10.0 haa6b8db_3 conda-forge
libstdcxx-ng 13.2.0 hc0a3c3a_7 conda-forge
libthrift 0.14.1 he6d91bd_2 conda-forge
libtiff 4.2.0 hbd63e13_2 conda-forge
libutf8proc 2.8.0 h166bdaf_0 conda-forge
libwebp-base 1.4.0 hd590300_0 conda-forge
libxml2 2.9.12 h72842e0_0 conda-forge
libzlib 1.2.13 hd590300_5 conda-forge
lime 0.2.0.1 pypi_0 pypi
llvmlite 0.41.1 pypi_0 pypi
logmuse 0.2.6 pyh8c360ce_0 conda-forge
lz4-c 1.9.3 h9c3ff4c_1 conda-forge
lzo 2.10 hd590300_1001 conda-forge
mamba 0.15.3 py38h2aa5da1_0 conda-forge
markdown 3.6 pyhd8ed1ab_0 conda-forge
markupsafe 2.1.5 py38h01eb140_0 conda-forge
matplotlib-base 3.7.3 py38h58ed7fa_0 conda-forge
matplotlib-inline 0.1.7 pypi_0 pypi
mmsplice 2.4.0 pypi_0 pypi
multidict 6.0.5 py38h01eb140_0 conda-forge
multiprocess 0.70.16 pypi_0 pypi
munkres 1.1.4 pyh9f0ad1d_0 conda-forge
natsort 8.4.0 pyhd8ed1ab_0 conda-forge
nbformat 5.10.4 pyhd8ed1ab_0 conda-forge
ncls 0.0.68 py38he5da3d1_2 bioconda
ncurses 6.5 h59595ed_0 conda-forge
nest-asyncio 1.6.0 pypi_0 pypi
networkx 3.1 pypi_0 pypi
numba 0.58.1 pypi_0 pypi
numpy 1.23.0 py38h3a7f9d9_0 conda-forge
oauth2client 4.1.3 py_0 conda-forge
oauthlib 3.2.2 pyhd8ed1ab_0 conda-forge
olefile 0.47 pyhd8ed1ab_0 conda-forge
openjpeg 2.4.0 hb52868f_1 conda-forge
openssl 1.1.1w hd590300_0 conda-forge
opt_einsum 3.3.0 pyhc1e730c_2 conda-forge
orc 1.6.7 heec2584_1 conda-forge
ordered-set 4.1.0 pyhd8ed1ab_0 conda-forge
orjson 3.10.3 py38h31a4407_0 conda-forge
packaging 24.0 pyhd8ed1ab_0 conda-forge
pandas 2.0.3 py38h01efb38_1 conda-forge
paramiko 3.4.0 pyhd8ed1ab_0 conda-forge
parquet-cpp 1.5.1 2 conda-forge
parso 0.8.4 pypi_0 pypi
patsy 0.5.6 pyhd8ed1ab_0 conda-forge
peppy 0.40.1 pyhd8ed1ab_0 conda-forge
pexpect 4.9.0 pypi_0 pypi
pickleshare 0.7.5 pypi_0 pypi
pillow 10.3.0 pypi_0 pypi
pip 24.0 pyhd8ed1ab_0 conda-forge
pkgutil-resolve-name 1.3.10 pyhd8ed1ab_1 conda-forge
plac 1.4.3 pyhd8ed1ab_0 conda-forge
platformdirs 4.2.2 pyhd8ed1ab_0 conda-forge
plotly 5.22.0 pypi_0 pypi
pluggy 1.5.0 pyhd8ed1ab_0 conda-forge
ply 3.11 pyhd8ed1ab_2 conda-forge
pooch 1.8.1 pyhd8ed1ab_0 conda-forge
prettytable 3.10.0 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.43 pypi_0 pypi
protobuf 3.15.8 py38h709712a_0 conda-forge
psutil 5.9.8 py38h01eb140_0 conda-forge
ptyprocess 0.7.0 pypi_0 pypi
pulp 2.7.0 py38h578d9bd_1 conda-forge
pure-eval 0.2.2 pypi_0 pypi
pyarrow 4.0.0 py38hc9229eb_1_cpu conda-forge
pyasn1 0.6.0 pyhd8ed1ab_0 conda-forge
pyasn1-modules 0.4.0 pyhd8ed1ab_0 conda-forge
pycosat 0.6.6 py38h01eb140_0 conda-forge
pycparser 2.22 pyhd8ed1ab_0 conda-forge
pyfaidx 0.8.1.1 pyhdfd78af_0 bioconda
pygments 2.18.0 pyhd8ed1ab_0 conda-forge
pyjwt 2.8.0 pyhd8ed1ab_1 conda-forge
pynacl 1.5.0 py38h01eb140_3 conda-forge
pyopenssl 23.2.0 pyhd8ed1ab_1 conda-forge
pyparsing 3.1.2 pyhd8ed1ab_0 conda-forge
pyranges 0.0.125 pypi_0 pypi
pyrle 0.0.40 py38he5da3d1_0 bioconda
pysam 0.21.0 py38h1c8baaf_0 bioconda
pysftp 0.2.9 py_1 conda-forge
pysocks 1.7.1 pyha2e5f31_6 conda-forge
pytest 8.2.1 pyhd8ed1ab_0 conda-forge
python 3.8.0 h357f687_5 conda-forge
python-dateutil 2.9.0 pyhd8ed1ab_0 conda-forge
python-fastjsonschema 2.19.1 pyhd8ed1ab_0 conda-forge
python-flatbuffers 1.12 pyhd8ed1ab_1 conda-forge
python-irodsclient 2.0.1 pyhd8ed1ab_0 conda-forge
python-slugify 8.0.4 pyhd8ed1ab_0 conda-forge
python-tzdata 2024.1 pyhd8ed1ab_0 conda-forge
python_abi 3.8 2_cp38 conda-forge
pytz 2024.1 pyhd8ed1ab_0 conda-forge
pyu2f 0.1.5 pyhd8ed1ab_0 conda-forge
pyvcf3 1.0.3 pyhdfd78af_0 bioconda
pywavelets 1.4.1 pypi_0 pypi
pyyaml 6.0.1 py38h01eb140_1 conda-forge
pyzmq 26.0.3 pypi_0 pypi
re2 2021.04.01 h9c3ff4c_0 conda-forge
readline 8.2 h8228510_1 conda-forge
referencing 0.35.1 pyhd8ed1ab_0 conda-forge
related 0.7.3 pyhd8ed1ab_0 conda-forge
reproc 14.2.4.post0 hd590300_1 conda-forge
reproc-cpp 14.2.4.post0 h59595ed_1 conda-forge
requests 2.32.2 pyhd8ed1ab_0 conda-forge
requests-oauthlib 2.0.0 pyhd8ed1ab_0 conda-forge
reretry 0.11.8 pyhd8ed1ab_0 conda-forge
rich 12.0.1 pypi_0 pypi
rpds-py 0.18.1 py38h31a4407_0 conda-forge
rsa 4.9 pyhd8ed1ab_0 conda-forge
ruamel_yaml 0.15.80 py38h01eb140_1009 conda-forge
s2n 1.0.10 h9b69904_0 conda-forge
s3transfer 0.10.1 pyhd8ed1ab_0 conda-forge
salib 1.4.8 pypi_0 pypi
scikit-image 0.21.0 pypi_0 pypi
scikit-learn 1.3.2 py38ha25d942_2 conda-forge
scipy 1.10.1 py38h59b608b_3 conda-forge
seaborn 0.13.2 hd8ed1ab_2 conda-forge
seaborn-base 0.13.2 pyhd8ed1ab_2 conda-forge
setuptools 70.0.0 pyhd8ed1ab_0 conda-forge
shap 0.44.1 pypi_0 pypi
simplejson 3.19.2 py38h01eb140_0 conda-forge
singledispatch 3.6.1 pyh44b312d_0 conda-forge
six 1.15.0 pyh9f0ad1d_0 conda-forge
skope-rules 1.0.1 pypi_0 pypi
slacker 0.14.0 py_0 conda-forge
slicer 0.0.7 pypi_0 pypi
smart_open 7.0.4 pyhd8ed1ab_0 conda-forge
smmap 5.0.0 pyhd8ed1ab_0 conda-forge
snakemake 7.26.0 hdfd78af_0 bioconda
snakemake-minimal 7.26.0 pyhdfd78af_0 bioconda
snappy 1.1.10 hdb0a2a9_1 conda-forge
sorted_nearest 0.0.39 py38he5da3d1_1 bioconda
spliceai 1.3.1 pyh864c0ab_1 bioconda
splicemap 0.0.1 pypi_0 pypi
sqlite 3.45.3 h2c6b66d_0 conda-forge
stack-data 0.6.3 pypi_0 pypi
statsmodels 0.14.1 py38h7f0c24c_0 conda-forge
stone 3.3.6 pyhd8ed1ab_0 conda-forge
stopit 1.1.2 py_0 conda-forge
tabix 1.11 hdfd78af_0 bioconda
tabulate 0.9.0 pyhd8ed1ab_1 conda-forge
tenacity 8.3.0 pypi_0 pypi
tensorboard 2.4.1 pyhd8ed1ab_1 conda-forge
tensorboard-plugin-wit 1.8.1 pyhd8ed1ab_0 conda-forge
tensorflow 2.4.3 py38h578d9bd_0 conda-forge
tensorflow-base 2.4.3 py38h83f5f1d_0 conda-forge
tensorflow-estimator 2.4.0 pyh9656e83_0 conda-forge
termcolor 1.1.0 pypi_0 pypi
text-unidecode 1.3 pyhd8ed1ab_1 conda-forge
threadpoolctl 3.5.0 pyhc1e730c_0 conda-forge
throttler 1.2.2 pyhd8ed1ab_0 conda-forge
tifffile 2023.7.10 pypi_0 pypi
tinydb 4.8.0 pyhd8ed1ab_0 conda-forge
tk 8.6.13 noxft_h4845f30_101 conda-forge
tomli 2.0.1 pyhd8ed1ab_0 conda-forge
toposort 1.10 pyhd8ed1ab_0 conda-forge
tornado 6.4 pypi_0 pypi
tqdm 4.66.4 pyhd8ed1ab_0 conda-forge
traitlets 5.14.3 pyhd8ed1ab_0 conda-forge
treeinterpreter 0.2.3 pypi_0 pypi
types-python-dateutil 2.9.0.20240316 pyhd8ed1ab_0 conda-forge
typing-extensions 3.7.4.3 0 conda-forge
typing_extensions 3.7.4.3 py_0 conda-forge
ubiquerg 0.7.0 pyhd8ed1ab_0 conda-forge
unicodedata2 15.1.0 py38h01eb140_0 conda-forge
uritemplate 4.1.1 pyhd8ed1ab_0 conda-forge
urllib3 1.26.18 pyhd8ed1ab_0 conda-forge
veracitools 0.1.3 py_0 conda-forge
wcwidth 0.2.13 pyhd8ed1ab_0 conda-forge
werkzeug 2.2.3 pypi_0 pypi
wget 3.2 pypi_0 pypi
wheel 0.43.0 pyhd8ed1ab_1 conda-forge
wrapt 1.12.1 pypi_0 pypi
xz 5.2.6 h166bdaf_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
yarl 1.9.4 py38h01eb140_0 conda-forge
yte 1.5.4 pyha770c72_0 conda-forge
zipp 3.17.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hd590300_5 conda-forge
zope-event 5.0 pypi_0 pypi
zope-interface 6.4.post2 pypi_0 pypi
zstandard 0.19.0 py38h0a891b7_0 conda-forge
zstd 1.4.9 ha95c52a_0 conda-forge
How to add specific tissues like the retina into the source code
I noticed that specific tissues like the retina are not included in the existing information. Could you kindly provide guidance on how to incorporate additional tissues, such as the retina, into your source code? Thanks for your help!
Add SpliceMap info to AbSplice output
create a snakemake rule that joins the necessary columns from MMSplice + SpliceMap output to AbSplice
Add AbSplice to environment.yaml
How to get CATs data
I want to get CATs data to validate the absplice performance. How to get CATs data?
SpliceAI running time
Hello,
I an testing absplice on a human WGS sample using default configurations (and tissues).
Currently I ran snakemake workflow inside the docker container using 20 threads.
MMSplice finished quite qwickly, but SpliceAI in running since 6 days and is still at chr2...
Is there a way for speeding SpliceAI up?
Does SpliceAI scale up with the threads given as input to snakemake?
Can't get attribute 'EBMPreprocessor' on <module 'interpret.glassbox.ebm.ebm'
Hey guys, your tool looks really interesting to me! :)
And so I wanted to give it a try.
However, I stumbled over this error when running the example on my Linux-server.
Do you know how to get around it?
[Sat Dec 24 01:35:02 2022]
rule absplice_dna:
input: mmsplice_splicemap.csv, spliceai.vcf
output: absplice_dna.csv
jobid: 1
reason: Missing output files: absplice_dna.csv; Input files updated by another job: mmsplice_splicemap.csv
resources: tmpdir=/tmp
2022-12-24 01:35:06.874355: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
File "/home/mi/olivek95/git_projects/absplice/example/.snakemake/scripts/tmp5a1c9dig.absplice_dna.py", line 11, in <module>
splicing_result.predict_absplice_dna()
File "/home/mi/olivek95/git_projects/absplice/absplice/result.py", line 568, in predict_absplice_dna
model = pickle.load(open(pickle_file, 'rb'))
AttributeError: Can't get attribute 'EBMPreprocessor' on <module 'interpret.glassbox.ebm.ebm' from '/buffer/ag_bsc/PS_SEQAN_STUDENTS/olivek95/miniconda3/envs/absplice/lib/python3.9/site-packages/interpret/glassbox/ebm/ebm.py'>
[Sat Dec 24 01:35:17 2022]
Error in rule absplice_dna:
jobid: 1
input: mmsplice_splicemap.csv, spliceai.vcf
output: absplice_dna.csv
RuleException:
CalledProcessError in file /home/mi/olivek95/git_projects/absplice/example/Snakefile, line 88:
Command 'set -euo pipefail; /buffer/ag_bsc/PS_SEQAN_STUDENTS/olivek95/miniconda3/envs/absplice/bin/python /home/mi/olivek95/git_projects/absplice/example/.snakemake/scripts/tmp5a1c9dig.absplice_dna.py' returned non-zero exit status 1.
File "/home/mi/olivek95/git_projects/absplice/example/Snakefile", line 88, in __rule_absplice_dna
File "/buffer/ag_bsc/PS_SEQAN_STUDENTS/olivek95/miniconda3/envs/absplice/lib/python3.9/concurrent/futures/thread.py", line 58, in run
Shutting down, this might take some time.
Exiting because a job execution failed. Look above for error message
Complete log: .snakemake/log/2022-12-24T013405.664404.snakemake.log
Initial command:
cd example; python -m snakemake -j 1
Container Environment problem
Hello,
We are trying to make the container work on our slurm based HPCC. We don't have docker available to us on the server and images before allowing them to be used but .oci isn't accepted. They asked for a tar.gz version of the image to convert to a sif before we could use it.
I installed docker locally, loaded the oci following the commands on git:
docker load -i absplice.oci docker run -it --name absplice_container localhost/absplice:latest /bin/bash
Then saved to a tar.gz
docker save localhost/absplice:latest | gzip > absplice_latest.tar.gz)
The IT team created a sif for us using this and gave us the following command to load it.
singularity shell -B /n /n/app/singularity/containers/absplice.sif
However, I run into an error trying to load the conda environment and conda init bash has no effect. Is this a problem with the system that we have to run it, the process of converting to tar.gz, or the image itself? And are there any recommendations for how to get around this problem?
`[user@compute-node ~]$ singularity shell -B /n /path/to/absplice.sif
Apptainer> conda activate absplice_dock
CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.
To initialize your shell, run
$ conda init <SHELL_NAME>
Currently supported shells are:
- bash
- fish
- tcsh
- xonsh
- zsh
- powershell
See 'conda init --help' for more information and options.
IMPORTANT: You may need to close and restart your shell after running 'conda init'.`
Thank you so much for your help!!!
Shayna
Bugs in Absplice implementation
Dear Dr. Gagneur.
We are also working on the implementation of the Absplice reported in this issue.
However, I have been working on it based on the readme at the following URL, but an error occurred in the test sample.
I would like to know what to do in this case.
The command that caused the error is as follows
python -m snakemake -j 1 --use-conda
The following is the ERROR message.
Error in rule download_splicemaps:
jobid: 4
output: splicemap_gtex_v8
shell:
splicemap_download --version gtex_v8 --splicemap_dir splicemap_gtex_v8 --tissues Testis --tissues Cells_Cultured_fibroblasts
(one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)
Shutting down, this might take some time.
Exiting because a job execution failed. Look above for error message
Complete log: .snakemake/log/2023-05-17T141553.827340.snakemake.log
Absplice-RNA
Hi,
I am trying to implement Absplice-RNA with a VCF file and RNAseq data from the same individual. I see the example code in README is made for Absplice-DNA. Could you point out how to use Absplice-RNA?
Thanks!
Yan
Convert EBM model to ONNX to get rid of interpret dependency
pip-installing interpret pulls a myriad of other awful pip dependencies like e.g. seaborn.
=> https://github.com/interpretml/ebm2onnx
T2T CHM13v2.0 (hs1) support
Do you have any plans to officially support the T2T CHM13v2.0 (hs1) reference genome?
Splicemap_tissues
Dear Developer
Thank you for your making docker container.
I want to know about "Splicemap_tissues"
If I have a blood DNA sample but want to know the splicing mutations in the liver, can I select Splicemap_tissues for the liver?
License
Hello developers of absplice,
First of all many thanks for such a nice open-source project to improve splicing variant prediction and annotation.
I'm opening this issue because I struggle to find a license for using the code or precomputed scores. Do you happen to have a license for your wonderful project?
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