BioPhi is an open-source antibody design platform. It features methods for automated antibody humanization (Sapiens), humanness evaluation (OASis) and an interface for computer-assisted antibody sequence design.
Home Page: https://biophi.dichlab.org/
License: MIT License
BioPhi/biophi/common/utils/io.py
Line 374 in 58e6e6f
The detailed is as follows
Traceback (most recent call last):
File "/data/christianxu/miniconda3/envs/biophi-dev/lib/python3.8/site-packages/flask/app.py", line 2548, in __call__
return self.wsgi_app(environ, start_response)
File "/data/christianxu/miniconda3/envs/biophi-dev/lib/python3.8/site-packages/flask/app.py", line 2528, in wsgi_app
response = self.handle_exception(e)
File "/data/christianxu/miniconda3/envs/biophi-dev/lib/python3.8/site-packages/flask/app.py", line 2525, in wsgi_app
response = self.full_dispatch_request()
File "/data/christianxu/miniconda3/envs/biophi-dev/lib/python3.8/site-packages/flask/app.py", line 1822, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/data/christianxu/miniconda3/envs/biophi-dev/lib/python3.8/site-packages/flask/app.py", line 1820, in full_dispatch_request
rv = self.dispatch_request()
File "/data/christianxu/miniconda3/envs/biophi-dev/lib/python3.8/site-packages/flask/app.py", line 1796, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
File "/data/christianxu/AntiBody/algorithm/BioPhi/biophi/humanization/web/views.py", line 217, in humanize_detail_export_humanized_fasta
return send_fasta(
File "/data/christianxu/AntiBody/algorithm/BioPhi/biophi/common/utils/io.py", line 386, in send_fasta
return send_text(stringio.getvalue(), name=name, extension='fa', timestamp=timestamp)
File "/data/christianxu/AntiBody/algorithm/BioPhi/biophi/common/utils/io.py", line 374, in send_text
return send_file(
TypeError: send_file() got an unexpected keyword argument 'attachment_filename'
Refer to the flask issue 4753,
Old names for some send_file parameters have been removed. download_name replaces attachment_filename, max_age replaces cache_timeout, and etag replaces add_etags. Additionally, path replaces filename in send_from_directory.
I am running a simplified biophi server on my machine. Once I start a humanization project I get the following error on the localhost page:
Error: (sqlite3.OperationalError) unable to open database file (Background on this error at: https://sqlalche.me/e/14/e3q8)
Could somebody help me out how to get rid of this issue?
Hi,
i'v used BioPhi web server to humanized antibody sequence, but Task is waiting in queue... appears for about 5 hours. I wonder if the web server is still working?
thanks!!
Currently the container images are quite big. There are a few things that could be done, such as using conda pack and two stage build that would hopefully decrease the image size. Is this something that would be helpful?
Task is waiting in queue. This seems to be a very recurrent problem over the last few days. I don't seem to be able to get the site to work!!
Just for information and to help other users of Biophi:
I installed Biophi using miniforge by following instructions provided on the project's github page.
However there was a runtime error:
AttributeError: 'Engine' object has no attribute 'execute'
I could track the issue down to a change in Sqlalchemy 2.0. Downgrading to Sqlalchemy via conda did the trick:
https://docs.sqlalchemy.org/en/14/changelog/migration_20.html
It is about engine.execute() which was deprecated in 1.4 and removed in 2.0.
$ conda install "sqlalchemy<2.0"
That installed sqlalchemy-1.4.46.
Kind regards and many thanks for providing Biophi, a great application!
Hi, I see that the CDR grafting option in the server performs straight grafting. Is there any way to automatically perform "Vernier" CDR grafting while retaining key "back-mutations" in the Vernier zones (as you describe in the paper)?
Thank you!!
SG
following conda install instructions the system throws an error when computing humanness
AttributeError: 'Engine' object has no attribute 'cursor'
[2024-02-06 13:58:11,833: ERROR/ForkPoolWorker-14] Task biophi.humanization.web.tasks.humanness_task[9fe7dc93-8651-49ab-baec-f3a0386376f5] raised unexpected: HumannessTaskError("'Engine' object has no attribute 'cursor'")
Traceback (most recent call last):
File "/home/mclark/BioPhi/biophi/humanization/web/tasks.py", line 305, in humanness_task
humanness=get_antibody_humanness(
File "/home/mclark/BioPhi/biophi/humanization/methods/humanness.py", line 275, in get_antibody_humanness
vh=get_chain_humanness(vh, params=params) if vh else None,
File "/home/mclark/BioPhi/biophi/humanization/methods/humanness.py", line 321, in get_chain_humanness
peptides = get_chain_oasis_peptides(chain, params=params)
File "/home/mclark/BioPhi/biophi/humanization/methods/humanness.py", line 303, in get_chain_oasis_peptides
oas_hits = get_oas_hits(
File "/home/mclark/BioPhi/biophi/humanization/methods/humanness.py", line 367, in get_oas_hits
return pd.read_sql(statement, params=peptides, con=engine)
File "/home/mclark/.conda/envs/biophi-dev/lib/python3.9/site-packages/pandas/io/sql.py", line 706, in read_sql
return pandas_sql.read_query(
File "/home/mclark/.conda/envs/biophi-dev/lib/python3.9/site-packages/pandas/io/sql.py", line 2739, in read_query
cursor = self.execute(sql, params)
File "/home/mclark/.conda/envs/biophi-dev/lib/python3.9/site-packages/pandas/io/sql.py", line 2673, in execute
cur = self.con.cursor()
AttributeError: 'Engine' object has no attribute 'cursor'
At
http://localhost:5000/humanization/designer/
I see
Error: [Errno 2] No such file or directory: 'hmmscan'
Can I get any advice?
Is there any documentation showing how to use python code to do analysis locally instead of using the locally webserver, like using API to request or importing some packages?
Result tables should be sortable, now it's implemented in javascript, it only works if we have <= 10 results. We should sort on the backend using a query argument like ?sort=my_column
I freshly cloned this repo on a new Ubuntu 20.04 VM, installed miniconda3, and followed the instructions at Run BioPhi dev server through Docker Compose.
Running make docker-build throws an error:
$ make docker-build
docker-compose build
redis uses an image, skipping
mongo uses an image, skipping
Building worker
Sending build context to Docker daemon 29.63MB
Step 1/12 : FROM continuumio/miniconda
latest: Pulling from continuumio/miniconda
Digest: sha256:fee1354ae2435522b9a8a79c5f1c406facc07ec5c44d730d8053600b37c924f0
Status: Downloaded newer image for continuumio/miniconda:latest
---> b8ea69b5c41c
Step 2/12 : RUN apt-get update -y && apt-get install -y --no-install-recommends build-essential
---> Using cache
---> 9a6c9d2094d4
Step 3/12 : RUN conda config --add channels bioconda && conda config --add channels conda-forge
---> Using cache
---> c3292b2e0f58
Step 4/12 : WORKDIR /opt/biophi
---> Using cache
---> 188452c8dc8c
Step 5/12 : COPY environment.yml .
---> 40000c92604e
Step 6/12 : COPY Makefile .
---> 35a9f24f85cf
Step 7/12 : RUN make env-update ENV_NAME=base
---> Running in 74fb699b3d87
conda env update -n base -f environment.yml
Collecting package metadata (repodata.json): ...working... make: *** [Makefile:12: env-upda
te] Killed
The command '/bin/sh -c make env-update ENV_NAME=base' returned a non-zero code: 2
ERROR: Service 'worker' failed to build : Build failed
make: *** [Makefile:18: docker-build] Error 1So I ran make env-create:
base $ make env-create
conda env create -n biophi-dev -f environment.yml
Collecting package metadata (repodata.json): done
Solving environment: failed
ResolvePackageNotFound:
- abnumber==0.2.7
make: *** [Makefile:9: env-create] Error 1So I added the env name and channels to environment.yml:
name: biophi-test
channels:
- defaults
- bioconda
- anaconda
- conda-forge
dependencies:
- python = 3.8
- pip
- abnumber == 0.2.7
- pip:
- click >= 7
- pandas
- sqlalchemy
- flask
- redis
- celery
- biopython
- pytest
- requests
- tqdm
- xlsxwriter
- humanize
- fairseq == 0.10.2
and re-ran make env-create, which now gives a new error:
base $ make env-create
conda env create -n biophi-dev -f environment.yml
Collecting package metadata (repodata.json): done
Solving environment: done
Downloading and Extracting Packages
pip-21.2.2 | 1.8 MB | ################################################ | 100%
python-dateutil-2.8. | 233 KB | ################################################ | 100%
pytz-2021.1 | 181 KB | ################################################ | 100%
mkl_fft-1.3.0 | 180 KB | ################################################ | 100%
bottleneck-1.3.2 | 125 KB | ################################################ | 100%
pandas-1.3.1 | 9.6 MB | ################################################ | 100%
python-3.8.11 | 18.2 MB | ################################################ | 100%
biopython-1.78 | 2.1 MB | ################################################ | 100%
mkl-2021.3.0 | 141.2 MB | ################################################ | 100%
anarci-2021.02.04 | 1.1 MB | ################################################ | 100%
blas-1.0 | 6 KB | ################################################ | 100%
numpy-base-1.20.3 | 4.5 MB | ################################################ | 100%
mkl-service-2.4.0 | 59 KB | ################################################ | 100%
hmmer-3.3.2 | 9.6 MB | ################################################ | 100%
numpy-1.20.3 | 23 KB | ################################################ | 100%
numexpr-2.7.3 | 188 KB | ################################################ | 100%
mkl_random-1.2.2 | 308 KB | ################################################ | 100%
abnumber-0.2.7 | 33 KB | ################################################ | 100%
intel-openmp-2021.3. | 1.4 MB | ################################################ | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Installing pip dependencies: - Ran pip subprocess with arguments:
['/home/ubuntu/miniconda3/envs/biophi-dev/bin/python', '-m', 'pip', 'install', '-U', '-r', '/home/ubuntu/BioPhi/condaenv.4ik288eg.requirements.txt']
Pip subprocess output:
Collecting click>=7
Using cached click-8.0.1-py3-none-any.whl (97 kB)
Requirement already satisfied: pandas in /home/ubuntu/miniconda3/envs/biophi-dev/lib/python3.8/site-packages (from -r /home/ubuntu/BioPhi/condaenv.4ik288eg.requirements.txt (line 2)) (1.3.1)
Collecting sqlalchemy
Using cached SQLAlchemy-1.4.22-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)
Collecting flask
Using cached Flask-2.0.1-py3-none-any.whl (94 kB)
Collecting redis
Using cached redis-3.5.3-py2.py3-none-any.whl (72 kB)
Collecting celery
Using cached celery-5.1.2-py3-none-any.whl (401 kB)
Requirement already satisfied: biopython in /home/ubuntu/miniconda3/envs/biophi-dev/lib/python3.8/site-packages (from -r /home/ubuntu/BioPhi/condaenv.4ik288eg.requirements.txt (line 7)) (1.78)
Collecting biopython
Using cached biopython-1.79-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (2.3 MB)
Collecting pytest
Using cached pytest-6.2.4-py3-none-any.whl (280 kB)
Collecting requests
Using cached requests-2.26.0-py2.py3-none-any.whl (62 kB)
Collecting tqdm
Using cached tqdm-4.62.0-py2.py3-none-any.whl (76 kB)
Collecting xlsxwriter
Using cached XlsxWriter-3.0.1-py3-none-any.whl (148 kB)
Collecting humanize
Using cached humanize-3.11.0-py3-none-any.whl (90 kB)
Collecting fairseq==0.10.2
Using cached fairseq-0.10.2-cp38-cp38-manylinux1_x86_64.whl (1.7 MB)
Collecting dataclasses
Using cached dataclasses-0.6-py3-none-any.whl (14 kB)
Collecting cython
Using cached Cython-0.29.24-cp38-cp38-manylinux1_x86_64.whl (1.9 MB)
Collecting regex
Using cached regex-2021.8.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB)
Collecting sacrebleu>=1.4.12
Using cached sacrebleu-2.0.0-py3-none-any.whl (90 kB)
Collecting hydra-core
Using cached hydra_core-1.1.0-py3-none-any.whl (144 kB)
Collecting torch
Pip subprocess error:
/home/ubuntu/miniconda3/envs/biophi-dev/.tmp4b21tyj7: line 3: 32406 Killed /home/ubuntu/miniconda3/envs/biophi-dev/bin/python -m pip install -U -r /home/ubuntu/BioPhi/condaenv.4ik288eg.requirements.txt
failed
CondaEnvException: Pip failed
make: *** [Makefile:9: env-create] Error 1make env-create does not throw an error. Strangely this worked last night but the errors began around 10 pm EST.
Hello,
Is there any ability to create an API for producing embeddings from input sequences? Users could implement this themselves if #19 turns out to be true. If not, perhaps an embedding API could be exposed without having to make weights publicly available?
Thanks!
E.g. Sapiens*1-*5 + Vernier CDR graft + Vernier CDR graft with 1 Sapiens iteration
I'm getting the following error when running BioPhi from the command line.
I'm not sure if what I'm going to showcase here is 100% correct, but here it goes.
While attempting to build and run my local version of the BioPhi backend, I run into some weird errors, including sqlalchemy.exc.ArgumentError: List argument must consist only of tuples or dictionaries. In particular, the error directed me to the get_oas_hits function (bellow).
def get_oas_hits(peptides: Union[str, List[str]], engine: Engine, filter_chain=None):
if not isinstance(peptides, tuple):
peptides = tuple(peptides)
filter_chain_statement = ""
if filter_chain:
assert filter_chain in ['Heavy', 'Light']
filter_chain_statement = f"AND Complete{filter_chain}Seqs >= 10000"
statement = "SELECT peptides.* FROM peptides " \
"LEFT JOIN subjects ON peptides.subject=subjects.id " \
"WHERE peptide IN (" + ",".join("?" * len(peptides)) + ") AND subjects.StudyPath <> 'Corcoran_2016' " \
+ filter_chain_statement
return pd.read_sql(statement, params=peptides, con=engine)According to this Stack Overflow post the problem seems to derive from changes in the source code of SQLAlchemy itself. In the environment.yml I see sqlalchemy < 2, I seem to be running SQLAlchemy 1.4.48 on my docker container. Nonetheless, I was having this error and I managed to fix it by changing the get_oas_hits function to:
def get_oas_hits(peptides: Union[str, List[str]], engine: Engine, filter_chain=None):
if not isinstance(peptides, list):
peptides = list(peptides)
filter_chain_statement = ""
if filter_chain:
assert filter_chain in ['Heavy', 'Light']
filter_chain_statement = f"AND Complete{filter_chain}Seqs >= 10000"
params = {f'param_{i}': peptide for i, peptide in enumerate(peptides)}
statement = "SELECT peptides.* FROM peptides " \
"LEFT JOIN subjects ON peptides.subject=subjects.id " \
f"WHERE peptide IN ({','.join(':' + key for key in params.keys())}) AND subjects.StudyPath <> 'Corcoran_2016' " \
+ filter_chain_statement
return pd.read_sql(statement, params=params, con=engine)Not sure if this is something you'd like to take a look at.
Black arrows in Vernier zones and CDRs, black bold residues that are mutated
Upgrading to pandas 2.0 makes the program raise a HumannessTaskError:
Traced the error back to pandas 2.0 and the following AttributeError in SQLAlchemy:
AttributeError: 'Connection' object has no attribute 'exec_driver_sql'
Reverting to pandas 1.5.2 solved the issue
I wasn't sure how to get the OASis and Germline scores from within a Python script. It works with this code, but I'm wondering if there is a simpler/more preferred way?
from biophi.common.utils.io import parse_antibody_files
from biophi.humanization.methods.humanness import OASisParams
from biophi.humanization.web.tasks import humanness_task, HumannessTaskResult
(
antibody_inputs,
invalid_names,
duplicate_names,
unrecognized_files,
) = parse_antibody_files(files=["./input.fasta"], verbose=False)
min_percent_subjects = 50
oasis_params = OASisParams(
oasis_db_path="/general/biophi/OASis_9mers_v1.db",
min_fraction_subjects=min_percent_subjects / 100,
)
file_index = 0
out = humanness_task(
input=antibody_inputs[file_index],
oasis_params=oasis_params,
scheme="kabat",
cdr_definition="kabat",
)
df = HumannessTaskResult.to_overview_dataframe([out])
where input.fasta is:
>Test1 H
QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPSRGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSS
Hello, I tried multiple options, but regardless of number of iterations or limit parameter I am getting always the same number of mutations introduced. Humanness score indeed increases, but I would like to know if there is an option to say how many mutations I want to be introduced?
Hi. I'm attempting to install biophi using conda on an environment using Python 3.9. I know that in the environment from the instructions it specifically says to use Python 3.8. I was wondering if there's any plans to assure compatibility with Python 3.9 any time soon. Thank you.
Found conflicts! Looking for incompatible packages.
This can take several minutes. Press CTRL-C to abort.
failed
UnsatisfiableError: The following specifications were found to be incompatible with each other:
Output in format: Requested package -> Available versionsThe following specifications were found to be incompatible with your system:
- feature:/linux-64::__glibc==2.31=0
- feature:|@/linux-64::__glibc==2.31=0
Your installed version is: 2.31
We should display the number of mutations, and separately number of Vernier mutations.
We should also report the % preservation values.
I used the server sometime earlier this year and it went find and will get results back in couple of minutes.
However, lately I submitted a couple of calculations to either Evaluate humanness or Humanize antibody, the page kept refreshing with "Tasks are waiting in queue" for 24hr and did not show any results. When went back to the link a day later, it said "Results are only stored a limited amount of time, please submit the form again".
Just a quick issue - following the conda installation instructions in the README fails with a strange glibc version error for me (I think it's related to this issue).
Looks like the problem is that you actually need the additional channels from the environment.yml file ( conda-forge and/or nodefaults) for the installation to succeed, so the command should be
conda install biophi -c bioconda -c conda-forge -c nodefaults
Is there a way to call alternative language models instead of the current one? With several of them emerging in recent literature, it might be handy to have the choice which one is to be used. Example for alternative language model: https://arxiv.org/abs/2308.14300
Hello,
Thanks for this great work. I was wondering if the actual weights of the Transformer are publicly available or if there are plans to make them available?
-Nick
Recently the server was resetted to resolve this issue. After 3 weeks of use, the issue arose once more.
Could you please reset the server on the website?
how to get sequence datasets?just like RU3, IAVI57,IAVI74 , and C38.
While using humanization module at https://biophi.dichlab.org/, I found the sequence of IGKV2-30*01 was different from the sequence from http://www.imgt.org/download/GENE-DB/IMGTGENEDB-ReferenceSequences.fasta-AA-WithoutGaps-F+ORF+inframeP .The second sequence was copied from the biophi web page.
Maybe there are some errors with your human germline V and J gene sequences.
I am trying to run the dev server using the instructions for development using docker compose. Once I try to build docker it fails at the stage => ERROR [biophi-main-web 6/9] RUN make env-update ENV_NAME=base with following error:
#0 0.237 conda env update -n base -f environment.yml
#0 0.718 Collecting package metadata (repodata.json): ...working... done
#0 46.40 Solving environment: ...working... failed
#0 46.40
#0 46.40 ResolvePackageNotFound:
#0 46.40 - hmmer[version='>=3.1']
#0 46.40
#0 49.63 make: *** [Makefile:12: env-update] Error 1
I am also not able to install this package locally using Conda, as the package cannot be found (I specify bioconda as a source)
I thought that the problem is in hmmer package so I have downloaded its 3.1 version, located it in the BioPhi directory and added the installation to the docker file. This solved the hmmer problem but raised a lot of package incompatibility errors, which eventually leads to the same results -> docker fails to build. Is there a workaround how to make it work? Thanks a lot for any help.
P.S This is just a small part of error messages I receive:
[worker 8/10] RUN make env-update ENV_NAME=base:
0.108 conda env update -n base -f environment.yml
0.528 Collecting package metadata (repodata.json): ...working... done
27.19 Solving environment: ...working...
54.07 Found conflicts! Looking for incompatible packages.
54.07 This can take several minutes. Press CTRL-C to abort.
Examining conflict for pycparser packaging brotlipy charset-normalizer conda-libmamba-solver truststore idna conda cffi conda-content-trust pluggy requests pycosat pysocks tk conda-package-streaming pyopenssl jsonpatch libmambapy wheel cryptography jsonpointer zstandard ruamel.yaml urllib3 anarci pip setuptools certifi abnumber conda-package-handling tqdm pythExamining conflict for pycparser packaging brotlipy charset-normalizer conda-libmamba-solver truststore idna conda cffi conda-content-trust pluggy requests pycosat pysocks conda-package-streaming pyopenssl jsonpatch libmambapy wheel cryptography jsonpointer zstandard ruamel.yaml urllib3 anarci pip setuptools certifi abnumber conda-package-handling tqdm python Examining conflict for pycparser packaging brotlipy charset-normalizer conda-libmamba-solver truststore idna conda cffi conda-content-trust pluggy requests pycosat pysocks conda-package-streaming pyopenssl jsonpatch libmambapy wheel cryptography jsonpointer zstandard ruamel.yaml urllib3 anarci pip setuptools certifi abnumber conda-package-handling tqdm python Examining conflict for pycparser packaging brotlipy charset-normalizer conda-libmamba-solver pcre2 truststore idna conda cffi conda-content-trust pluggy libarchive requests bzip2 pycosat pysocks conda-package-streaming pyopenssl libmamba jsonpatch libmambapy wheel cryptography jsonpointer zstandard ruamel.yaml libsolv urllib3 anarci pip setuptools certifi abnuExamining conflict for pycparser packaging brotlipy charset-normalizer conda-libmamba-solver pcre2 truststore idna conda cffi conda-content-trust pluggy libarchive requests bzip2 pycosat pysocks conda-package-streaming pyopenssl libmamba jsonpatch libmambapy wheel cryptography jsonpointer zstandard ruamel.yaml libsolv urllib3 anarci pip setuptools certifi abnuExamining conflict for pycparser packaging brotlipy charset-normalizer conda-libmamba-solver readline truststore idna conda cffi conda-content-trust pluggy requests pycosat krb5 pysocks conda-package-streaming sqlite pyopenssl ncurses jsonpatch libmambapy wheel cryptography jsonpointer zstandard ruamel.yaml urllib3 anarci pip setuptools certifi abnumber conda-Examining conflict for pycparser packaging brotlipy charset-normalizer conda-libmamba-solver readline truststore idna conda cffi conda-content-trust pluggy requests pycosat krb5 pysocks conda-package-streaming sqlite pyopenssl ncurses jsonpatch libmambapy wheel cryptography jsonpointer zstandard ruamel.yaml urllib3 anarci pip setuptools certifi abnumber conda-Examining conflict for conda-package-streaming zstandard pycparser brotlipy urllib3 conda anarci charset-normalizer pip setuptools requests certifi jsonpatch abnumber wheel conda-package-handling cryptography jsonpointer: 7%|▋ | 5/76 [00:02<00:28, 2.48it/s]
Currently, the output of the humanization step returns a single fasta file. I would be interested in having the output have multiple sequences that are based on sampling from the output from the model.
I have a POC to do this in the cli, we could discuss what this would look like in the web client if needed.
Provisioned a fresh VM with Ubuntu 20.04., git v2.25.1, and latest clone of BioPhi's main.
2 vCPU and 4 GB RAM throws the following error:
base $ git clone https://github.com/Merck/BioPhi.git
Cloning into 'BioPhi'...
remote: Enumerating objects: 1513, done.
remote: Counting objects: 100% (1513/1513), done.
remote: Compressing objects: 100% (1103/1103), done.
remote: Total 1513 (delta 467), reused 1419 (delta 385), pack-reused 0
Receiving objects: 100% (1513/1513), 14.62 MiB | 43.79 MiB/s, done.
Resolving deltas: 100% (467/467), done.
base $ cd BioPhi
base $ make docker-build
docker-compose build
redis uses an image, skipping
Building web
Sending build context to Docker daemon 29.64MB
Step 1/11 : FROM continuumio/miniconda3
latest: Pulling from continuumio/miniconda3
33847f680f63: Pull complete
f5a80bcd1413: Pull complete
8d0d14d1334a: Pull complete
Digest: sha256:592a60b95b547f31c11dc6593832e962952e3178f1fa11db37f43a2afe8df8d7
Status: Downloaded newer image for continuumio/miniconda3:latest
---> 67414e5844b6
Step 2/11 : RUN apt-get update -y && apt-get install -y --no-install-recommends build-essential
---> Running in 4c3edc93c3d5
Get:1 http://deb.debian.org/debian buster InRelease [122 kB]
Get:2 http://deb.debian.org/debian buster-updates InRelease [51.9 kB]
Get:3 http://security.debian.org/debian-security buster/updates InRelease [65.4 kB]
Get:4 http://deb.debian.org/debian buster/main amd64 Packages [7907 kB]
Get:5 http://deb.debian.org/debian buster-updates/main amd64 Packages [15.2 kB]
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binutils binutils-common binutils-x86-64-linux-gnu cpp cpp-8 dpkg-dev g++
g++-8 gcc gcc-8 libasan5 libatomic1 libbinutils libc-dev-bin libc6-dev
libcc1-0 libdpkg-perl libgcc-8-dev libgomp1 libisl19 libitm1 liblsan0
libmpc3 libmpfr6 libmpx2 libquadmath0 libstdc++-8-dev libtsan0 libubsan1
linux-libc-dev make patch xz-utils
Suggested packages:
binutils-doc cpp-doc gcc-8-locales debian-keyring g++-multilib
g++-8-multilib gcc-8-doc libstdc++6-8-dbg gcc-multilib manpages-dev autoconf
automake libtool flex bison gdb gcc-doc gcc-8-multilib libgcc1-dbg
libgomp1-dbg libitm1-dbg libatomic1-dbg libasan5-dbg liblsan0-dbg
libtsan0-dbg libubsan1-dbg libmpx2-dbg libquadmath0-dbg glibc-doc gnupg
| gnupg2 bzr libstdc++-8-doc make-doc ed diffutils-doc
Recommended packages:
fakeroot gnupg | gnupg2 libalgorithm-merge-perl manpages manpages-dev
libfile-fcntllock-perl liblocale-gettext-perl
The following NEW packages will be installed:
binutils binutils-common binutils-x86-64-linux-gnu build-essential cpp cpp-8
dpkg-dev g++ g++-8 gcc gcc-8 libasan5 libatomic1 libbinutils libc-dev-bin
libc6-dev libcc1-0 libdpkg-perl libgcc-8-dev libgomp1 libisl19 libitm1
liblsan0 libmpc3 libmpfr6 libmpx2 libquadmath0 libstdc++-8-dev libtsan0
libubsan1 linux-libc-dev make patch xz-utils
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Step 6/11 : RUN make env-update ENV_NAME=base
---> Running in 14b9502a50b7
conda env update -n base -f environment.yml
Collecting package metadata (repodata.json): ...working... done
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Downloading and Extracting Packages
abnumber-0.2.7 | 33 KB | ########## | 100%
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Installing pip dependencies: ...working... Ran pip subprocess with arguments:
['/opt/conda/bin/python', '-m', 'pip', 'install', '-U', '-r', '/opt/biophi/condaenv.tshc824g.requirements.txt']
Pip subprocess output:
Collecting click>=7
Downloading click-8.0.1-py3-none-any.whl (97 kB)
Requirement already satisfied: pandas in /opt/conda/lib/python3.8/site-packages (from -r /opt/biophi/condaenv.tshc824g.requirements.txt (line 2)) (1.3.1)
Collecting pandas
Downloading pandas-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.5 MB)
Collecting sqlalchemy
Downloading SQLAlchemy-1.4.23-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB)
Collecting flask
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Collecting redis
Downloading redis-3.5.3-py2.py3-none-any.whl (72 kB)
Collecting celery
Downloading celery-5.1.2-py3-none-any.whl (401 kB)
Requirement already satisfied: biopython in /opt/conda/lib/python3.8/site-packages (from -r /opt/biophi/condaenv.tshc824g.requirements.txt (line 7)) (1.79)
Collecting pytest
Downloading pytest-6.2.4-py3-none-any.whl (280 kB)
Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from -r /opt/biophi/condaenv.tshc824g.requirements.txt (line 9)) (2.25.1)
Collecting requests
Downloading requests-2.26.0-py2.py3-none-any.whl (62 kB)
Requirement already satisfied: tqdm in /opt/conda/lib/python3.8/site-packages (from -r /opt/biophi/condaenv.tshc824g.requirements.txt (line 10)) (4.61.2)
Collecting tqdm
Downloading tqdm-4.62.2-py2.py3-none-any.whl (76 kB)
Collecting xlsxwriter
Downloading XlsxWriter-3.0.1-py3-none-any.whl (148 kB)
Collecting humanize
Downloading humanize-3.11.0-py3-none-any.whl (90 kB)
Collecting fairseq==0.10.2
Downloading fairseq-0.10.2-cp38-cp38-manylinux1_x86_64.whl (1.7 MB)
Requirement already satisfied: cffi in /opt/conda/lib/python3.8/site-packages (from fairseq==0.10.2->-r /opt/biophi/condaenv.tshc824g.requirements.txt (line 13)) (1.14.6)
Collecting hydra-core
Downloading hydra_core-1.1.1-py3-none-any.whl (145 kB)
Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from fairseq==0.10.2->-r /opt/biophi/condaenv.tshc824g.requirements.txt (line 13)) (1.21.1)
Collecting sacrebleu>=1.4.12
Downloading sacrebleu-2.0.0-py3-none-any.whl (90 kB)
Collecting regex
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Collecting dataclasses
Downloading dataclasses-0.6-py3-none-any.whl (14 kB)
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Downloading torch-1.9.0-cp38-cp38-manylinux1_x86_64.whl (831.4 MB)
failed
Pip subprocess error:
/opt/conda/.tmpg04gop_k: line 3: 461 Killed /opt/conda/bin/python -m pip install -U -r /opt/biophi/condaenv.tshc824g.requirements.txt
CondaEnvException: Pip failed
make: *** [Makefile:12: env-update] Error 1
The command '/bin/sh -c make env-update ENV_NAME=base' returned a non-zero code: 2
ERROR: Service 'web' failed to build : Build failed
make: *** [Makefile:25: docker-build] Error 1The error does not appear if repeating the above steps with 2 vCPU and 8 GB RAM.
When trying:
biophi cdr_grafting mabs.fa --fasta-only --output humanized.fa
errors appears as:
Error: No such command 'cdr_grafting'
I checked scripts and find methond "cdr_grafting" is in class CDRGraftingHumanizationParams of script biophi/humanization/methods/humanization.py.
How should cdr_grafting function should be used in command line?
Hi,
Thanks for open sourcing this work- it's awesome! Not as much of an issue as a request. Could you open source the humanization benchmark that you use in the paper. It would be great for reproducibility and development of future humanization tools!
Thanks!
Following step 2 of the install (https://github.com/Merck/BioPhi#2-install-biophi-using-conda) results in the following:
Alternatively using docker, the provided code is asking for additional arguments. Admittedly I have very limited experience with docker. I'm hoping to get this set up on a physical Windows box. Any assistance would be appreciated!
It's a minor thing, but it gave me a headache for a little while and I needed to dig through the code to see how the path specified via --oasis-db is passed to SQLite.
The CLI --help page suggests to pass paths as sqlite:////Absolute/path/to/oas_human_subject_9mers_2019_11.db, but as can be seen below, the sqlite:/// is already prepended. This results in Error: .. The OASis DB path does not exist ....
Error: Invalid sequence for "Antibody1": "QVQLVQSGGGVVQPGRSLRLSCKASGYTETRYTMHWVRQAPGKGLEWIGYINPSRGYTNYNOKVKDRFTISTDKSKSTAFLQMDSLRPEDTAVYYCARYYDDHYCSSASCFCLDYWGQGTPVTVSS"
I dont know how to deal with this issue , It would be much appreciated if this solves the problem,thanks guys!
I got this message while trying to Export a Full Table after humanization.
I am trying to run biophi in anaconda. I am very new to this so already after trying to get biophi running I encounter the following error.
UnsatisfiableError: The following specifications were found to be incompatible with each other:
Output in format: Requested package -> Available versionsThe following specifications were found to be incompatible with your system:
feature:/win-64::__win==0=0
feature:|@/win-64::__win==0=0
biophi -> click[version='>=7'] -> __unix
biophi -> click[version='>=7'] -> __win
I tried installing click using pip install click, which seems to work, but doesnt resolve the error message. I am running this in windows and not in unix/linux.
Greetings, and thank you for your work!
I have been searching for a transformer model that has been trained on antibody sequences so I can extract learned representations of my dataset, and this repo has been suggested to me.
After tokenizing a sequence using the dictionary.encode_line method, I call the sentence_encoder to get the encoding of my sequence. In your opinion, for downstream tasks, should I ideally be working with the 'encoder_embedding' values of the output dictionary, or with the 'encoder_out' values?
More importantly, is there a built-in method that I can use to get reconstructed sequences back? I wrote a linear decoder that tries to reconstruct the 'encoder_out' values back to the original tokenized sequences, and although it does a somewhat decent job, it is not good enough for my downstream tasks.
Thank you for your time!
Once again, the server stays at "in queue" after submitting the job. No results return.
Encountered this error in the CLI while setting up: https://stackoverflow.com/questions/73337927/typeerror-init-got-an-unexpected-keyword-argument-unbound-message
Was solved by downgrading to flask == 2.1.3
The paper says: We observed that most mutations achieved the first or second highest Sapiens score (Supplementary Figure 19).
Which makes me think higher is more immunogenic, but I'm getting more accurate results with lower is better.
Enable collecting multiple designed sequences, adding names to them and exporting them in a single FASTA file.
I would like to run the tool using the cli instead of the web browser. Specifically, I saw that running the humanization using cdr grafting is not an option currently. Is this a feature you would be interested in having?
I'm happy to work on implementing this, I would propose a two step process:
After 8eb45f1 merged, now getting error doing fresh install on new VM and repo clone:
$ make docker-build
docker-compose build
ERROR: Service 'web' depends on service 'mongo' which is undefined.
make: *** [Makefile:25: docker-build] Error 1
Hi there.
Thank you very much for contributing to the project.
I noticed that the code has some type checking, but not all of it does. I'm happy to try and improve this by adding it everywhere, as well as adding a check in the ci that ensures proper type checking. Let me know if this is something you would approve of.
We should provide instructions on providing the OASIS_DB_PATH and mounting the directory so that the database can be accessed when running through docker run or docker-compose.
It would be nice to be able to navigate between all results without having to go back to the result table page
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