clami66/AF_server

None

Overview

Latest release: None, Last update: 2023-01-02

Linting: linting: failed, Formatting: formatting: failed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/clami66/AF_server . --tag None

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

config.yaml

# E-mail settings
server_address: "user@server.com" ### server mailbox address
mail_server: "server.com"  ### server mailbox domain
whitelist: "config/whitelist" ### The whitelist contains one email per line, only senders from the list will be allowed
# AF settings
AF_install_dir: "/proj/apps/alphafold" ### AlphaFold installation directory
monomer_flagfile: "/proj/apps/alphafold/flagfiles/monomer_full_dbs.flag" # monomer and multimer flag files paths
multimer_flagfile: "/proj/apps/alphafold/flagfiles/multimer_full_dbs.flag"
# resources settings, these override the slurm profile settings
mem_mb: 240000
walltime: 4320
max_gpus: 8
gpus_per_node: 8
n_cores_per_gpu: 16
# CASP settings, can be ignored if it is not a CASP server. Will show in PDB headers
CASP_groupn: "xxxx-yyyy-zzzz-wwww" # 
CASP_groupn_multi: "wwww-xxxx-yyyy-zzzz"
CASP_groupname: "af2-standard"
CASP_groupname_multi: "af2-multimer"
CASP_sender: "casp-meta@predictioncenter.org"
# for uploading results somewhere (passwordless).
# Results in invoking `rsync -av results/AF_models/{target}/[models,msas].tar.gz user@server.com:/path`
data_server_user: "user"
data_server_address: "server.com"
data_server_folder: "/path"

envmodules.yaml

envmodules:
  run_alphafold: # specifies envmodules to load when executing rule `run_alphafold`
    - Anaconda/2021.05-nsc1
    - AlphaFold/2.2.1
    - other envmodules...

whitelist

allowed_sender@mail.com # one email per line
another_allowed_sender@server.net

Linting and formatting

Linting results

  1Lints for snakefile /tmp/tmpzw509uh7/workflow/rules/common.smk:
  2    * Path composition with '+' in line 144:
  3      This becomes quickly unreadable. Usually, it is better to endure some
  4      redundancy against having a more readable workflow. Hence, just repeat
  5      common prefixes. If path composition is unavoidable, use pathlib or
  6      (python >= 3.6) string formatting with f"...".
  7      Also see:
  8
  9
 10Lints for rule add_headers (line 82, /tmp/tmpzw509uh7/workflow/rules/alphafold.smk):
 11    * No log directive defined:
 12      Without a log directive, all output will be printed to the terminal. In
 13      distributed environments, this means that errors are harder to discover.
 14      In local environments, output of concurrent jobs will be mixed and become
 15      unreadable.
 16      Also see:
 17      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
 18    * Specify a conda environment or container for each rule.:
 19      This way, the used software for each specific step is documented, and the
 20      workflow can be executed on any machine without prerequisites.
 21      Also see:
 22      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
 23      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 24    * Param model_dir is a prefix of input or output file but hardcoded:
 25      If this is meant to represent a file path prefix, it will fail when
 26      running workflow in environments without a shared filesystem. Instead,
 27      provide a function that infers the appropriate prefix from the input or
 28      output file, e.g.: lambda w, input: os.path.splitext(input[0])[0]
 29      Also see:
 30      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
 31      https://snakemake.readthedocs.io/en/stable/tutorial/advanced.html#tutorial-input-functions
 32
 33Lints for rule send_acknowledgement (line 7, /tmp/tmpzw509uh7/workflow/rules/submit.smk):
 34    * No log directive defined:
 35      Without a log directive, all output will be printed to the terminal. In
 36      distributed environments, this means that errors are harder to discover.
 37      In local environments, output of concurrent jobs will be mixed and become
 38      unreadable.
 39      Also see:
 40      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
 41    * Migrate long run directives into scripts or notebooks:
 42      Long run directives hamper workflow readability. Use the script or
 43      notebook direcive instead. Note that the script or notebook directive does
 44      not involve boilerplate. Similar to run, you will have direct access to
 45      params, input, output, and wildcards.Only use the run direcive for a
 46      handful of lines.
 47      Also see:
 48      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
 49      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration
 50
 51Lints for rule send_results (line 40, /tmp/tmpzw509uh7/workflow/rules/submit.smk):
 52    * No log directive defined:
 53      Without a log directive, all output will be printed to the terminal. In
 54      distributed environments, this means that errors are harder to discover.
 55      In local environments, output of concurrent jobs will be mixed and become
 56      unreadable.
 57      Also see:
 58      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
 59    * Migrate long run directives into scripts or notebooks:
 60      Long run directives hamper workflow readability. Use the script or
 61      notebook direcive instead. Note that the script or notebook directive does
 62      not involve boilerplate. Similar to run, you will have direct access to
 63      params, input, output, and wildcards.Only use the run direcive for a
 64      handful of lines.
 65      Also see:
 66      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
 67      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration
 68
 69Lints for rule data_upload (line 8, /tmp/tmpzw509uh7/workflow/rules/upload.smk):
 70    * Specify a conda environment or container for each rule.:
 71      This way, the used software for each specific step is documented, and the
 72      workflow can be executed on any machine without prerequisites.
 73      Also see:
 74      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
 75      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 76    * Param data_dir is a prefix of input or output file but hardcoded:
 77      If this is meant to represent a file path prefix, it will fail when
 78      running workflow in environments without a shared filesystem. Instead,
 79      provide a function that infers the appropriate prefix from the input or
 80      output file, e.g.: lambda w, input: os.path.splitext(input[0])[0]
 81      Also see:
 82      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
 83      https://snakemake.readthedocs.io/en/stable/tutorial/advanced.html#tutorial-input-functions
 84
 85Lints for rule msa_upload (line 45, /tmp/tmpzw509uh7/workflow/rules/upload.smk):
 86    * Specify a conda environment or container for each rule.:
 87      This way, the used software for each specific step is documented, and the
 88      workflow can be executed on any machine without prerequisites.
 89      Also see:
 90      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
 91      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
 92    * Param data_dir is a prefix of input or output file but hardcoded:
 93      If this is meant to represent a file path prefix, it will fail when
 94      running workflow in environments without a shared filesystem. Instead,
 95      provide a function that infers the appropriate prefix from the input or
 96      output file, e.g.: lambda w, input: os.path.splitext(input[0])[0]
 97      Also see:
 98      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
 99      https://snakemake.readthedocs.io/en/stable/tutorial/advanced.html#tutorial-input-functions
100
101Lints for rule model_upload (line 80, /tmp/tmpzw509uh7/workflow/rules/upload.smk):
102    * Specify a conda environment or container for each rule.:
103      This way, the used software for each specific step is documented, and the
104      workflow can be executed on any machine without prerequisites.
105      Also see:
106      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
107      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
108    * Param data_dir is a prefix of input or output file but hardcoded:
109      If this is meant to represent a file path prefix, it will fail when
110      running workflow in environments without a shared filesystem. Instead,
111      provide a function that infers the appropriate prefix from the input or
112      output file, e.g.: lambda w, input: os.path.splitext(input[0])[0]
113      Also see:
114      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules
115      https://snakemake.readthedocs.io/en/stable/tutorial/advanced.html#tutorial-input-functions
116
117Lints for rule pkl_reduction (line 116, /tmp/tmpzw509uh7/workflow/rules/upload.smk):
118    * No log directive defined:
119      Without a log directive, all output will be printed to the terminal. In
120      distributed environments, this means that errors are harder to discover.
121      In local environments, output of concurrent jobs will be mixed and become
122      unreadable.
123      Also see:
124      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
125    * Migrate long run directives into scripts or notebooks:
126      Long run directives hamper workflow readability. Use the script or
127      notebook direcive instead. Note that the script or notebook directive does
128      not involve boilerplate. Similar to run, you will have direct access to
129      params, input, output, and wildcards.Only use the run direcive for a
130      handful of lines.
131      Also see:
132      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#external-scripts
133      https://snakemake.readthedocs.io/en/latest/snakefiles/rules.html#jupyter-notebook-integration

Formatting results

 1[DEBUG] 
 2[DEBUG] In file "/tmp/tmpzw509uh7/workflow/rules/alphafold.smk":  Formatted content is different from original
 3[DEBUG] 
 4[DEBUG] 
 5[DEBUG] In file "/tmp/tmpzw509uh7/workflow/rules/common.smk":  Formatted content is different from original
 6[DEBUG] 
 7[DEBUG] In file "/tmp/tmpzw509uh7/workflow/rules/upload.smk":  Formatted content is different from original
 8[DEBUG] 
 9[DEBUG] In file "/tmp/tmpzw509uh7/workflow/Snakefile":  Formatted content is different from original
10[INFO] 4 file(s) would be changed 😬
11[INFO] 1 file(s) would be left unchanged 🎉
12
13snakefmt version: 0.8.0