BifrostOmics ver 0.1 This project bridges omics datasets and bioinformatics with non–computer science users through a collection of reproducible, user-friendly pipelines for diverse omics datasets.

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pipeline specific readmes:

# RNA-seq Pipeline v0.1: Differential Gene Expression from your dataset

This workflow implements a reproducible RNA-seq pipeline using publicly available tools. It includes quality control, trimming, alignment, read summarization, and automation via Snakemake.

#Pipeline includes:

Download SRA/FASTQ files based on runinfo.csv QC with FastQC and MultiQC Trim reads with fastp Build HISAT2 index and align reads with hisat2 Index/Align BAM files Count reads with featureCounts Prerequisites:

1. Conda installed (https://docs.conda.io/)
2. Your project directory structure is like this: folder/ #├── reference/ ---- reference genome info here #│ ├── genome.fna #│ └── annotations.gtf #├── raw_data/ #│ └── runinfo.csv ---- this file specifies the samples #├── Snakefile #├── config/ #│ ├── config.yaml #├── envs/ #│ └── rnaseq.yaml #└── run_Snakemake_workflow.sh

Running Clone the folder "rnaseq_project" Run the script "run_Snakemake_workflow.sh" All software is installed via Conda using envs/*.yaml which is embedded in "run_Snakemake_workflow.sh" script To visualize the DAG add the "--dag", eg "bash run_Snakemake_workflow.sh --dag" Outputs fastqc_results/: Quality reports trimmed_data/: Trimmed FASTQ files alignments/: BAM files counts/: Gene counts file(s) Tools Used conda snakemake prefetch fasterq-dump fastqc multiqc fastp hisat2 samtools featureCounts