rnsherpa/TLand-predict

Snakemake pipeline to create input feature tables and run predictions with TLand

Overview

Latest release: 1.0.0, Last update: 2026-01-13

Linting: linting: failed, Formatting: formatting: failed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda. 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/rnsherpa/TLand-predict . --tag 1.0.0

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.

Setting up the config

Snakemake uses config files to specify things like input files and workflow parameters. This document goes over the necessary steps to set up the config file for TLand-predict.

Machine-specific configurations

Running TLand-predict requires prior setup of specific software and data to your machine. This will result in paths unique to your setup being added to the config.yml file. These are a ONE TIME edit to config.yml and should remain unchanged for any runs performed on your machine.

  1. Set up a local RegulomeDB server following the instructions from this repository

  • Set the gds_dir key in the config to the path to genomic-data-service

  1. Download the necessary Sei files from:

  • Set the sei_dir key in the config to the path to Sei

  1. Download the remaining required files from [] and set:

  • dnase_sig_path to

  • chip_sig_path to

  • organsp_dnase_sig_path to

  • models_path to

Configs under # Resources do not require changing as the files are in the repository.

Run-specific configurations

runs_table: Path to runs table

base_dir: Path to directory where runs should be output

Linting and formatting

Linting results

1FileNotFoundError in file "/tmp/tmpnyztqwv6/rnsherpa-TLand-predict-d9ae160/workflow/Snakefile", line 17:
2[Errno 2] No such file or directory: '/path/to/your/runs_table.tsv'
3  File "/tmp/tmpnyztqwv6/rnsherpa-TLand-predict-d9ae160/workflow/Snakefile", line 17, in <module>
4  File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
5  File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 620, in _read
6  File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
7  File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1880, in _make_engine
8  File "/home/runner/micromamba/envs/snakemake-workflow-catalog/lib/python3.12/site-packages/pandas/io/common.py", line 873, in get_handle

Formatting results

1[DEBUG] 
2[DEBUG] In file "/tmp/tmpnyztqwv6/rnsherpa-TLand-predict-d9ae160/workflow/Snakefile":  Formatted content is different from original
3[DEBUG] 
4[DEBUG] In file "/tmp/tmpnyztqwv6/rnsherpa-TLand-predict-d9ae160/workflow/rules/predict.smk":  Formatted content is different from original
5[DEBUG] 
6[DEBUG] In file "/tmp/tmpnyztqwv6/rnsherpa-TLand-predict-d9ae160/workflow/rules/extract_features.smk":  Formatted content is different from original
7[INFO] 3 file(s) would be changed 😬
8
9snakefmt version: 0.11.2