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Cite with Zenodo run with conda run with docker run with singularity wakatime

Introduction

ClinDet (Clinical variants Detector) is a Snakemake pipeline for comprehensive analysis of cancer genomes and transcriptomes, integrating multiple state-of-the-art tools to generate consensus results. The pipeline supports a wide range of experimental setups, including:

  1. FASTQ input files

  2. Whole genome sequencing (WGS), whole transcriptome sequencing (WTS), and targeted/panel sequencing (WXS)

  3. Paired tumor/normal and tumor-only sample configurations

  4. Most GRCh37 and GRCh38 reference genome builds

  5. Non-human species (e.g., mouse, worm)

Pipeline overview

Installation

To build the complex ClinDet analysis environment, you must first install Conda, Docker, and SingularityCE. Afterwards, please follow the instructions in the Installation chapter of the ClinDet documentation.

Usage

Note

If you are not familiar with snakemake, please refer to this page。

All you need to run ClinDet is a samplesheet.csvfile that contains the paths to your input fastq files.

Tumor_R1_file_path,Tumor_R2_file_path,Normal_R1_file_path,Normal_R2_file_path,Sample_name,Target_file_bed,Project
/AbsoPath/of/projects/CGGA_WES/data/T_CGGA_D14_r1.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/T_CGGA_D14_r2.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/B_CGGA_D14_r1.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/B_CGGA_D14_r2.fq.gz,CGGA_D14,/AbsoPath/of/target.bed,CGGA_WES
/AbsoPath/of/projects/CGGA_WES/data/T_CGGA_653_r1.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/T_CGGA_653_r2.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/B_CGGA_653_r1.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/B_CGGA_653_r2.fq.gz,CGGA_653,/AbsoPath/of/target.bed,CGGA_WES

Then, you can Launch ClinDet by prepared a *.smk file (e.g. snake_wes.smk).

nohup snakemake -j 30 --printshellcmds -s snake_wes.smk \
--use-singularity --singularity-args "--bind /your/homepath/:/your/homepath/" \
--latency-wait 300 --use-conda >> wes.log

For more details and further functionality, please refer to the main ClinDet documentation

Use cases

To facilitate user adoption, ClinDet includes a variety of use cases. These examples are designed to help users become proficient with the software and adjust it for their specific analysis. Detailed information is available in the Use Case chapter of the documentation.

  1. Use case I: SNV and CNV calling from Whole exome sequencing data
  2. Use case II: Fusion genes detection from multiple myeloma patient RNA-seq
  3. Use case III: Whole genome sequencing of COLO829 cell line
  4. Use case IV: Quantifying​​ the contributions of DNA repair defective gene mutations to mutational signatures（C. elegans）

ClinDet Development Visualized​

Click the image above to watch the development demo video on Bilibili.

ClinDet

Credits

The ClinDet pipeline was written and is maintained by Yuliang Zhang (@Yuliang Zhang) , Junyi Zhang and Jianfeng Li from the National Research Center for Translational Medicine at Shanghai.

We thank the following organizations and people for their extensive assistance in the development of this pipeline, listed in alphabetical order:

Citations

You can cite the ClinDet Zenodo record for a specific version using the following DOI: 10.5281/zenodo.16892396

Sustainable data analysis with Snakemake

Mölder, F., Jablonski, K.P., Letcher, B., Hall, M.B., Tomkins-Tinch, C.H., Sochat, V., Forster, J., Lee, S., Twardziok, S.O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., Nahnsen, S., Köster, J.

F1000Research 2021. doi: 10.12688/f1000research.29032.3.

LLM-Assisted Interactive Deployment (Beta)

ClinDet provides a Claude Code skill (https://github.com/zyllifeworld/YULUMINA) that automatically generates pipeline configuration files from natural language descriptions. Users can invoke the skill in VS Code via Claude Code and describe their experiment setup in plain language, for example:

I have human RNA-seq paired-end data in /mnt/data/home/tycloud/data/mm/upload_fq/rna. File names follow the pattern MM-010_R1.fq.gz (sample MM-010, paired-end reads R1/R2). Reference genome: human b37. Project directory: /mnt/data/home/tycloud/project/project_mmrna, output: /mnt/data/home/tycloud/project/project_mmrna/rna. Required analyses: Salmon quantification, RSEM quantification, and fusion gene detection (no mutation calling). Use conda environment r-4-4-1 for R scripts. Generate both local and Slurm execution scripts.

我现在有人类的RNA-seq数据，双端测序，在 /mnt/data/home/tycloud/data/mm/upload_fq/rna目录。文件名是MM-010_R1这样的格式,MM-010是样本名，R1,R2表示双端测序文件。基因组用人类的b37版本，我准备在/mnt/data/home/tycloud/project/project_mmrna项目中运行程序，结果输出目录为/mnt/data/home/tycloud/project/project_mmrna/rna。我想跑salmon和RSEM定量,以及检测融合基因，不需要call mutation，请帮我生成clindet的运行所需文件，使用conda 环境r-4-4-1下的R运行文件,需要本地和slurm运行两个版本

The LLM then produces all files required to run ClinDet — project_config.yaml, sample_sheet.csv, local execution commands, and cluster-ready Snakemake commands — thereby automating the setup of omics data analysis workflows.

ClinDet

Future Work

  1. Advanced downstream analysis based on consensus results (e.g. Driver gene identification)
  2. Benchmark report
  3. ...