ntnn19/gaptrick-af_unmasked
Workflow for multimeric templated-based structure prediction
Overview
Latest release: None, Last update: 2026-09-14
Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=ntnn19/gaptrick-af_unmasked
Quality control: linting: failed formatting: failed
Deployment
Step 1: Install Snakemake and Snakedeploy
Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run
conda create -c conda-forge -c bioconda -c nodefaults --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
For other installation methods, refer to the Snakemake and Snakedeploy documentation.
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/ntnn19/gaptrick-af_unmasked . --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 using apptainer/singularity, use
snakemake --cores all --sdm apptainer
To run the workflow using a combination of conda and apptainer/singularity for software deployment, use
snakemake --cores all --sdm conda apptainer
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.
Workflow overview
This workflow is a best-practice workflow for <detailed description>.
The workflow is built using snakemake and consists of the following steps:
Download genome reference from NCBI
Validate downloaded genome (
pythonscript)Simulate short read sequencing data on the fly (
dwgsim)Check quality of input read data (
FastQC)Collect statistics from tool output (
MultiQC)
Running the workflow
Input data
This template workflow creates artificial sequencing data in *.fastq.gz format.
It does not contain actual input data.
The simulated input files are nevertheless created based on a mandatory table linked in the config.yaml file (default: .test/samples.tsv).
The sample sheet has the following layout:
sample |
condition |
replicate |
read1 |
read2 |
|---|---|---|---|---|
sample1 |
wild_type |
1 |
sample1.bwa.read1.fastq.gz |
sample1.bwa.read2.fastq.gz |
sample2 |
wild_type |
2 |
sample2.bwa.read1.fastq.gz |
sample2.bwa.read2.fastq.gz |
Workflow parameters
The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.
Parameter |
Type |
Description |
Required |
Default |
|---|---|---|---|---|
sample_sheet |
string |
path to sample sheet, mandatory |
yes |
config/samples.tsv |
get_genome |
yes |
|||
. ncbi_ftp |
string |
URL for genome retrieval from NCBI FTP server |
yes |
|
simulate_reads |
yes |
|||
. read_length |
integer |
length of target reads in bp |
yes |
100 |
. read_number |
integer |
number of total reads to be simulated |
yes |
10000 |
Linting and formatting
Linting results
1/home/runner/work/snakemake-workflow-catalog/snakemake-workflow-catalog/.pixi/envs/default/lib/python3.13/site-packages/google/auth/transport/grpc.py:44: FutureWarning: grpcio < 1.83.0 does not support Post-Quantum Cryptography (PQC). Support for non-PQC environments is deprecated. In October 2026, google-auth will raise its minimum requirements to enforce grpcio >= 1.83.0. For more details on Google Cloud's post-quantum security migration, visit: https://cloud.google.com/security/resources/post-quantum-cryptography
2 warnings.warn(
3Cloning github.com/ntnn19/AlphaFold3_workflow to /home/runner/.cache/snakemake/snakemake/source-cache/snakemake-git-cache/github.com/ntnn19/AlphaFold3_workflow
4WorkflowError in file "https://raw.githubusercontent.com/ntnn19/AlphaFold3_workflow/dev/workflow/rules/common.smk", line 34:
5Error validating config file.
6ValidationError: 'mode' is a required property
7
8Failed validating 'required' in schema:
9 {'$schema': 'https://json-schema.org/draft/2020-12/schema',
10 'description': 'AlphaFold3 Snakemake workflow configuration schema',
11 'type': 'object',
12 'required': ['mode', 'af3_flags'],
13 'additionalProperties': True,
14 'properties': {'mode': {'type': 'string',
15 'enum': ['custom',
16 'all-vs-all',
17 'pulldown',
18 'virtual-drug-screen',
19 'stoichio-screen'],
20 'description': 'Workflow run mode. See '
21 'docs/input.md for details.'},
22 'output_dir': {'type': 'string',
23 'default': 'results',
24 'description': 'Root directory for all '
25 'workflow outputs.'},
26 'tmp_dir': {'type': 'string',
27 'default': '/tmp/af3',
28 'description': 'Scratch directory for '
29 'AlphaFold3 temporary '
30 'files.'},
31 'n_seeds': {'type': 'integer',
32 'minimum': 1,
33 'description': 'Number of random seeds per '
34 'job (overrides model_seeds '
35 'column in sample sheet).'},
36 'n_samples': {'type': 'integer',
37 'minimum': 1,
38 'default': 5,
39 'description': 'Number of diffusion '
40 'samples per seed.'},
41 'msa_option': {'type': 'string',
42 'enum': ['auto', 'none', 'upload'],
43 'default': 'auto',
44 'description': 'MSA generation '
45 'strategy.'},
46 'exclusive_lock': {'type': 'boolean',
47 'default': False,
48 'description': 'Request exclusive '
49 'node allocation for '
50 'inference jobs '
51 '(SLURM).'},
52 'run_data_pipeline_locally': {'type': 'boolean',
53 'default': False,
54 'description': 'Run the '
55 'data '
56 'pipeline '
57 'locally '
58 'instead '
59 'of via '
60 'SLURM.'},
61 'run_inference_locally': {'type': 'boolean',
62 'default': False,
63 'description': 'Reserved for '
64 'future '
65 'release.'},
66 'n_node_splits': {'type': 'integer',
67 'minimum': 1,
68 'default': 1,
69 'description': 'Number of node '
70 'splits for parallel '
71 'inference dispatch.'},
72 'run_ost_scoring': {'type': 'boolean',
73 'default': False,
74 'description': 'Run OpenStructure '
75 'scoring after '
76 'inference.'},
77 'ground_truth_dir': {'type': 'string',
78 'default': '',
79 'description': 'Directory '
80 'containing '
81 'ground-truth '
82 'structures for '
83 'OST scoring.'},
84 'n_scoring_splits': {'type': 'integer',
85 'minimum': 1,
86 'default': 4,
87 'description': 'Number of splits '
88 'for parallel OST '
89 'scoring.'},
90 'predict_individual_components': {'type': 'boolean',
91 'default': False,
92 'description': 'Also '
93 'predict '
94 'individual '
95 'monomer '
96 'components '
97 'of '
98 'each '
99 'multimer '
100 'job.'},
101 'sample_sheets': {'type': 'object',
102 'description': 'Paths to input '
103 'sample sheets, keyed '
104 'by entry point type. '
105 'Any combination of '
106 'raw_data, '
107 'data_pipeline_ready, '
108 'inference_ready, and '
109 'merge_ready may be '
110 'provided '
111 'simultaneously; each '
112 'stream is processed '
113 'independently and '
114 'all results are '
115 'collected into a '
116 'single final target. '
117 'At least one entry '
118 'point must be '
119 'non-empty.\n',
120 'additionalProperties': False,
121 'properties': {'raw_data': {'type': 'string',
122 'description': 'TSV '
123 'with '
124 'columns '
125 'defined '
126 'in '
127 'docs/input.md. '
128 'Triggers '
129 'full '
130 'pipeline.'},
131 'data_pipeline_ready': {'type': 'string',
132 'description': 'TSV '
133 'pointing '
134 'to '
135 'pre-computed '
136 'monomer '
137 'JSONs. '
138 'Skips '
139 'MSA '
140 'generation.'},
141 'merge_ready': {'type': 'string',
142 'description': 'TSV '
143 'pointing '
144 'to '
145 'pre-merged '
146 'multimer '
147 'JSONs. '
148 'Skips '
149 'MSA '
150 '+ '
151 'merge.'},
152 'inference_ready': {'type': 'string',
153 'description': 'TSV '
154 'pointing '
155 'to '
156 'final '
157 'input '
158 'JSONs. '
159 'Runs '
160 'inference '
161 'only.'},
162 'mutations': {'type': 'string',
163 'description': 'TSV '
164 'with '
165 'mutation '
166 'information '
167 'per '
168 'chain.'}}},
169 'af3_inference_version': {'type': 'string',
170 'description': 'AlphaFold3 '
171 'inference '
172 'version to '
173 'use.'},
174 'af3_flags': {'type': 'object',
175 'description': 'AlphaFold3 container and '
176 'path configuration.',
177 'required': ['af3_data_pipeline_container',
178 'af3_inference_container',
179 'models_dir',
180 'databases_dir'],
181 'additionalProperties': True,
182 'properties': {'af3_data_pipeline_container': {'type': 'string',
183 'description': 'Path '
184 'to '
185 'the '
186 'AlphaFold3 '
187 'Singularity/Apptainer '
188 'image '
189 '(.sif).'},
190 'af3_inference_container': {'type': 'string',
191 'description': 'Path '
192 'to '
193 'the '
194 'AlphaFold3 '
195 'Singularity/Apptainer '
196 'image '
197 '(.sif).'},
198 'models_dir': {'type': 'string',
199 'description': 'Path '
200 'to '
201
202... (truncated)
Formatting results
1[DEBUG]
2[DEBUG]
3[DEBUG] In file "/tmp/tmpjlincqga/workflow/rules/dockq_or_usalgin.smk": Formatted content is different from original
4[DEBUG]
5[DEBUG]
6[DEBUG]
7[DEBUG]
8[DEBUG]
9[DEBUG] In file "/tmp/tmpjlincqga/workflow/Snakefile": Formatted content is different from original
10[DEBUG]
11[INFO] 2 file(s) would be changed 😬
12[INFO] 6 file(s) would be left unchanged 🎉
13
14snakefmt version: 0.11.5