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add rna usecase
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‎doc/_static/CGGA_P438_cancer_report.html‎

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‎doc/usecase/case_one.md‎

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```
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Tumor_R1_file_path,Tumor_R2_file_path,Normal_R1_file_path,Normal_R2_file_path,Sample_name,Target_file_bed,Project
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T_CGGA_D14_r1.fq.gz,T_CGGA_D14_r2.fq.gz,B_CGGA_D14_r1.fq.gz,B_CGGA_D14_r1.fq.gz,CGGA_D14,target.bed,CGGA_WES
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T_CGGA_653_r1.fq.gz,T_CGGA_653_r2.fq.gz,B_CGGA_653_r1.fq.gz,B_CGGA_653_r1.fq.gz,CGGA_653,target.bed,CGGA_WES
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~/projects/CGGA_WES/data/T_CGGA_D14_r1.fq.gz,~/projects/CGGA_WES/data/T_CGGA_D14_r2.fq.gz,~/projects/CGGA_WES/data/B_CGGA_D14_r1.fq.gz,~/projects/CGGA_WES/data/B_CGGA_D14_r1.fq.gz,CGGA_D14,target.bed,CGGA_WES
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~/projects/CGGA_WES/data/T_CGGA_653_r1.fq.gz,~/projects/CGGA_WES/data/T_CGGA_653_r2.fq.gz,~/projects/CGGA_WES/data/B_CGGA_653_r1.fq.gz,~/projects/CGGA_WES/data/B_CGGA_653_r1.fq.gz,CGGA_653,target.bed,CGGA_WES
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```
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## Write an Snakemake file from template
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-j 30 --printshellcmds -s snake_wes.smk --use-singularity \
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--singularity-args "--bind /public/home/:/public/home/,/public/ClinicalExam:/public/ClinicalExam" \
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--latency-wait 300 --use-conda >> wes.log
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```
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```
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### Output
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### case report
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There is a example case report of CGGA_P438
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<a href="../_static/CGGA_P438_cancer_report.html">example report HTML</a>

‎doc/usecase/case_two.md‎

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# Use case II: Fusion genes detection from multiple myeloma patient RNA-seq
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## Background
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​​Clinical Applications of RNA-Seq in Diagnostic Testing​​
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RNA sequencing (RNA-Seq) is a high-throughput transcriptome profiling technology that enables comprehensive analysis of gene expression, splicing variants, fusion events, and novel transcripts. In clinical diagnostics, it serves as a powerful tool for:
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∙
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​​Cancer Subtyping​​: Identifying tumor-specific gene expression signatures, fusion genes (e.g., BCR-ABL1), and aberrant splicing events to guide targeted therapies.
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∙
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​​Rare Disease Diagnosis​​: Detecting dysregulated pathways and aberrant expression in Mendelian disorders where DNA-based tests are inconclusive.
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∙
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​​Infectious Disease Characterization​​: Profiling host-pathogen interactions and pathogen expression in complex infections.
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∙
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​​Biomarker Discovery​​: Validating expression-based biomarkers for disease monitoring and treatment response.
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## Setup a project folder
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````{note}
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Before starting the analysis, please ensure that you have set up the analysis environment using the build_conda_env.sh script.
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````
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Create a folder named project/CGGA_WES in your home directory and activate the Clindet conda environment.
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```{code} bash
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mkdir -p ~/projects/MM_RNA
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cd ~/projects/MM_RNA
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conda activate clindet
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```
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## Download data and
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Download Multiple myeloma and COLO829 cellline RNA-seq data from the SRA database using wget and prepare the sample information file, make sure fastq-dump are in in $PATH (if don't install it first)
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```{code} bash
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cd ~/projects/MM_RNA
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mkdir -p data && cd data
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## Methods one multiple myeloma RNA-seq data
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wget -q -c -O A26.11 https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR12099713/SRR12099713
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wget -q -c -O A27.19 https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR12099714/SRR12099714
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wget -q -c -O A28.15 https://sra-pub-run-odp.s3.amazonaws.com/sra/SRR12099715/SRR12099715
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fastq-dump --gzip -O /public/ClinicalExam/lj_sih/projects/project_clindet/data/GSE153380 --split-3 ./A26.11
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fastq-dump --gzip -O /public/ClinicalExam/lj_sih/projects/project_clindet/data/GSE153380 --split-3 ./A27.19
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fastq-dump --gzip -O /public/ClinicalExam/lj_sih/projects/project_clindet/data/GSE153380 --split-3 ./A28.15
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```
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Next, create a CSV file named pipe_rna.csv in the ~/projects/MM_RNA directory with the following content:
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```
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Tumor_R1_file_path,Tumor_R2_file_path,Normal_R1_file_path,Normal_R2_file_path,Sample_name,Target_file_bed,Project
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~/projects/MM_RNA/data/A26.11_1.fastq.gz,~/projects/MM_RNA/data/A26.11_2.fastq.gz,MF1
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~/projects/MM_RNA/data/A27.19_1.fastq.gz,~/projects/MM_RNA/data/A27.19_2.fastq.gz,MS3
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~/projects/MM_RNA/data/A28.15_1.fastq.gz,~/projects/MM_RNA/data/A28.15_2.fastq.gz,CD1
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```
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## Write an Snakemake file from template
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For this project, modify the sample sheet and create a new Snakemake file named **snake_rna.smk** (see below). Set the following parameters in the Snakemake file:
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1. **configfile (str)**: config file for softwares and resource parameters.
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1. **stage (list)**: analysis steps. avaiable options:`['RSEM','arriba','TRUST4','samlom','kallisto']`
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## Download data
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## write Snakemake file
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For this project, we need change the sample sheet info.
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```{code} python
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import pandas as pd
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samples_info = pd.read_csv('',index_col='Sample_name') # set sample sheet path
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unpaired_samples = samples_info.loc[pd.isna(samples_info['Normal_R1_file_path'])].index.tolist()
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paired_samples = samples_info.loc[~pd.isna(samples_info['Normal_R1_file_path'])].index.tolist()
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samples_info = pd.read_csv('./pipe_rna.csv',index_col='Sample_name')
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unpaired_samples = samples_info.loc[pd.isna(samples_info['R2_file_path'])].index.tolist()
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paired_samples = samples_info.loc[~pd.isna(samples_info['R1_file_path'])].index.tolist()
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configfile: "" # set config file path
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project = samples_info["Project"].unique().tolist()[0]
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genome_version = 'WBcel235' # set genome version
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configfile: "/public/ClinicalExam/lj_sih/projects/project_clindet/build_log/config.yaml"
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import os
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if not os.path.exists("logs/slurm"):
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os.makedirs("logs/slurm")
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pre_pon_db = False
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if not os.path.exists('analysis/pindel_normal/log'):
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os.makedirs('analysis/pindel_normal/log')
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groups = ['NC','T']
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germ_caller_list = ['caveman']
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caller_list = ['strelkasomaticmanta','caveman','muse','cgppindel_filter']
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recall_pon = False
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recall_pon_pindel = False
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recal = False
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stages = ['RSEM','arriba','TRUST4','samlom','kallisto']
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caller_list = ['sentieon_anno_rnaedit','Mutect2_filter']
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project = 'RNA'
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genome_version = 'b37'
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rna_res_list = [
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##### for isoform expression ######
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"{project}/{genome_version}/results/summary/RSEM/{sample}/{sample}.genes.results" if 'RSEM' in stages else None,
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##### ka
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"{project}/{genome_version}/results/summary/kallisto/{sample}/abundance.tsv" if 'kallisto' in stages else None,
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##### for fusion gene detection #####
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"{project}/{genome_version}/results/fusion/{sample}_arriba_fusion.tsv" if 'arriba' in stages else None,
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##### for TRUST4 immu analysis #####
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"{project}/{genome_version}/results/IG/TRUST4/{sample}_report.tsv" if 'TRUST4' in stages else None,
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#### Case report #####
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]
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rna_res_list = list(filter(None, rna_res_list))
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rule all:
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input:
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## paired sample
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expand([
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"{project}/{genome_version}/results/dedup/paired/{sample}-{group}.sorted.bam",
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"{project}/{genome_version}/results/sv/paired/DELLY/{sample}/SV_delly_{sample}_filter.vcf",
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"{project}/{genome_version}/results/vcf/paired/{sample}/strelkasomaticmanta.vcf",
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"{project}/{genome_version}/results/vcf/paired/{sample}/muse.vcf",
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'{project}/{genome_version}/logs/paired/caveman_{sample}.log',
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],
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expand(rna_res_list,
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# sample = paired_samples,
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sample = ['CD1','COLO829'],
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project = project,
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genome_version = genome_version,
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sample = paired_samples,
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group = groups,
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caller = caller_list)
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include: "workflow/WGS/Snakefile" # the relative path of clindet workflow WGS subfolder snakefile
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genome_version = genome_version
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),
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##### Modules #####
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include: "workflow/RNA/Snakefile"
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```
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:::
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## Run clindet
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There is two way you can run clindet
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1. run on a local server
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2. submit to HPC through slurm
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### Run on local node
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```{code} bash
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nohup snakemake -j 30 --printshellcmds -s snake_rna.smk \
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--use-singularity --singularity-args "--bind /your/home/path:/your/home/path" \
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--latency-wait 300 --use-conda >> rna.log
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```
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``` bash
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### Submit to HPC use slurm
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we provide a slurm config.yaml under clindet/workflow/config_slurm folder.
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```{code} bash
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nohup snakemake --profile workflow/config_slurm \
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-j 30 --printshellcmds -s snake_wgs_worm.smk \
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--use-singularity \
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--singularity-args "--bind /public/home/:/public/home/,/public/ClinicalExam:/public/ClinicalExam" \
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--latency-wait 300 --use-conda -
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-j 30 --printshellcmds -s snake_rna.smk --use-singularity \
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--singularity-args "--bind /your/home/path:/your/home/path" \
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--latency-wait 300 --use-conda >> rna.log
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```
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### Output
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## Results
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```{image} ../img/usecase/usecase_two/Fusion_gene.jpeg

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