🔬 Bioinformatics Notebook. Scripts for bioinformatics pipelines, with quick start guides for programs and video demonstrations.

Scripts to import your FeatureCounts output into DEXSeq

scripts for RNA-Seq analysis

Quality Control, Mapping and Reads Count for RNA-Seq Analysis

This repository contatins a pipeline for RNA-Seq data processing using featurecounts for gene count generation

A tool for extraction of lexical features from text based on UIMA and MapReduce

This is a code tutorial for rna or dna sequence mapping by myself-RyanYip

nextflow ATACseq pipeline for GG02

Scripts to index and align Bovine genome with HISAT2

This is an automated workflow pipeline for analyzing and processing Bulk-RNA seq data, implemented primarily in bash, python and R, and wrapped in a NextFlow workflow to characterize the gene landscape in the samples.

This is an automated workflow pipeline for analyzing and processing Bulk-RNA seq data, implemented primarily in bash, python and R, and wrapped in a NextFlow workflow to characterize the gene landscape in the samples.

RNA-seq expression and enrichment analysis of Pseudomonas aeruginosa (SigX mutant vs wild-type) using FastQC, fastp, STAR, featureCounts, DESeq2, and STRING to identify antibiotic stress-response pathways and key regulatory genes.

A pipeline to call intron and intergenic eRNAs from PRO-seq Data

rna-seq pipeline for drosophila melanogaster: qc, trimming, alignment, and read counting (81.7% alignment, ~50m bp processed)

Build Docker container for Subread package and (optionally) convert to Apptainer/Singularity.

Proof of concept of a RNA-Seq pipeline from reads to count matrix (including quality control) with Nextflow and additional example RNA-Seq analysis in R

A reproducible RNA-Seq analysis pipeline for Staphylococcus aureus under antibiotic stress, utilizing Nextflow and Singularity. It involves genome mapping, read counting, and statistical analysis to identify differentially expressed genes (DEGs) and generates key visualizations.

🧬 Analyze RNA-seq data of *Pseudomonas aeruginosa* SigX mutant vs. wild-type to uncover antibiotic stress-response pathways and transcriptional changes.