🐍 CVRCseq 🐍

CVRCseq is a collection of commonly used pipelines integrated into a single workflow via Snakemake. Previously, these existed as individual Snakemake workflows. This unified workflow is designed to run on NYU's UltraViolet HPC, which utilizes Slurm and offers a variety of different node types.

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Available Pipelines

🧬 RNA-seq Analysis

Currently, there are 5 RNA-seq analysis pipelines available:

1. RNAseq_PE
   Paired-end data: fastqc → fastp → STAR → featurecounts

2. RNAseq_SE
   Single-end data: fastqc → fastp → STAR → featurecounts

3. RNAseq_HISAT2_stringtie
   Paired-end data: fastqc → fastp → HISAT2 → stringtie

4. RNAseq_HISAT2_stringtie_nvltrx
   Paired-end data: fastqc → fastp → HISAT2 → stringtie → novel transcript identification

5. RNAseqTE_PE
   Paired-end data: fastqc → fastp → STAR → TEcount

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🧬 Small RNA-seq Analysis

Currently, there is 1 small RNA-seq analysis pipeline available, designed to work with the QIAseq miRNA Library Kit from Qiagen:

1. sRNAseq_SE
   Single-end data: fastqc → umi-tools → STAR → featurecounts

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🧬 DNA Binding/Enrichment Analysis

Currently, there are 3 DNA binding/enrichment pipelines available:

1. ChIPseq_PE
   Paired-end data: fastqc → fastp → bowtie2 → macs2

2. CUT-RUN_PE
   Paired-end data: fastqc → fastp → bowtie2 → seacr & macs2

3. ATACseq_PE
   Paired-end data: fastqc → fastp → bowtie2 → macs2

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📁 File Descriptions

Snakefiles

• workflow/Snakefile - Launches individual pipelines located in workflow/rules

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Configuration Files

config/samples_info.tab

Tab-delimited file containing sample metadata:

1. R1 and R2 fastq file names - As received from sequencing center (remove lane numbers L00X for multi-lane samples)
2. Simple sample names - User-defined identifiers
3. Condition - Experimental condition (e.g., diabetic vs non_diabetic)
4. Replicate number - Biological replicate identifier
5. Antibody column - Required for ChIPseq/CUT-RUN (specifies antibody vs control samples)
6. Final sample ID - Concatenation of sample name, condition, replicate, and antibody columns
7. Additional metadata - Can be added for downstream analysis

Notes:

1. cat_rename.py handles concatenation of multi-lane fastq files and renaming based on this table.
2. Paired samples - For ChIPseq/CUT-RUN, sample name/condition/replicate should be identical between antibody and control pairs

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config/config.yaml

Contains general and workflow-specific configuration parameters:

Generic Requirements:

• sample_file - Location of samples_info.tab (default: config/samples_info.tab)
• workflow - Name of workflow being used
• genome - Location of indexed genome:
  • RNAseq_PE/RNAseq_SE/sRNAseq_SE: STAR 2.7.7a index
  • HISAT2 workflows: HISAT2 index
  • ChIPseq/CUT-RUN/ATACseq: bowtie2 index
• GTF - Location of annotation file

Workflow-Specific config settings:

CUT-RUN_PE:

• spike_genome - Spike-in genome index (bowtie2)
• chromosome_lengths - Required for spike-in normalization. This file can be found in the STAR genome index folder (chrLength.txt)
• effective_genome_size - For MACS2

ChIPseq_PE & ATACseq_PE:

• effective_genome_size - For MACS2

RNAseq_HISAT2_stringtie variants:

• prepDE_length - Average fragment length for stringtie prepDE script

RNAseqTE_PE:

• TE_GTF - GTF file with TE annotations (available from MGH lab)

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config/profile/config.yaml

Defines default Slurm resources for each rule.

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📝 Scripts

workflow/scripts/cat_rename.py

Preprocessing script that:\

1. Concatenates fastq files split across multiple sequencing lanes\
2. Renames fastq files from verbose sequencing center IDs to user-defined names\
3. Creates new files as sample_id_Rx.fastq.gz\
4. Executed automatically via snakemake_init.sh\

Skip option: Use -c flag with snakemake_init.sh to bypass this step.

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workflow/scripts/snakemake_init.sh

Main execution script that:\

1. Executes cat_rename.py\
2. Loads conda environment\
3. Launches Snakemake pipeline\
4. Runs MultiQC for quality control\

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workflow/scripts/launch_sbatch.sh

Launches pipeline from compute node (recommended over login node). Edit the snakemake_init.sh command with desired parameters and submit via sbatch.

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workflow/scripts/condaload_CVRCseq.sh

Sets environment variables and loads the conda environment.

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workflow/scripts/FRP.py

Computes Fraction of Reads in Peaks (FRP) and outputs a summary table with:\

• FRP values\
• Total fragments\
• Fragments within peaks\

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workflow/scripts/combine_TE_counts.py

combines counts from TEcount into a single .csv file.

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Environment

workflow/envs/CVRCseq.yml

Contains conda environment specifications for the pipeline.

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🚀 Usage Instructions

Getting Started

1. Clone repository
   git clone https://github.com/mgildea87/CVRCseq.git

2. Update sample information
   Edit config/samples_info.tab with fastq.gz file names and desired sample, condition, replicate names, and Antibody/IgG control status (if using)

3. Configure workflow
   Update config.yaml with project-specific settings

4. Customize parameters (optional)
   Set workflow specific parameters in the appropriate worklow/rules .smk file if desired. e.g. alignment parameters.

5. Launch pipeline
   bash workflow/scripts/snakemake_init.sh Description of parameters:
   -h help"
   -d .fastq directory"
   -s parameters to pass to snakemake (e.g. --unlock)
   -w workflow name (e.g. 'RNAseq_PE')
   -c Skip cat_rename.py. Use to skip copying, concatenating, and renaming of .fastq files to the workflow/inputs/fastq/ local directory\

Software links

snakemake, STAR, fastqc, fastp, subread - featurecounts, HISAT2, stringtie, TEcount, umi-tools, bowtie2, macs2, seacr