star-aligner

RNA-seq analysis from FASTQ to biology: STAR alignment, gene-level counts, DESeq2 differential expression, and GO/KEGG/GSEA. Metadata-driven design with human/mouse/rat support.

A modular, containerized NGS pipeline for RNA-seq, long-read, and metagenomic analysis

Immunopeptidogenomics pipeline that builds a cryptic peptide database from RNA-seq and identifies non-canonical (cryptic) peptides in immunopeptidomics mass spectrometry data.

Empirical benchmark suite comparing execution times, peak RAM usage, and cloud compute costs across RNA-Seq aligners, single-cell frameworks, and variant callers.

Modular RNA-seq variant calling and annotation framework (hg38) integrating STAR and GATK Best Practices to extract high-confidence SNPs and enable gene-level and systems biology analyses of expressed genetic variation.

Automated RNA-seq reference builder for STAR and Salmon. Features container-aware memory detection, automatic Ensembl/GENCODE downloads, and version-controlled index generation.

RNA-Sequencing

RNA-seq QC benchmark (custom Python vs FastQC), adapter trimming, STAR alignment, and htseq-count-based strand-specificity analysis

Differential gene expression analysis for samples with replicates using STAR-DeSeq2 pipeline

End-to-end RNA-seq pipeline for BRCA tumor vs normal differential expression analysis

Independently maintained, performance-oriented successor to the STAR RNA-seq aligner

Parallel end-to-end bulk RNA-seq pipeline on a PBS/HPC job array: FastQC, Trimmomatic, STAR alignment, and featureCounts, with a Bioconda setup script.

A reproducible RNA-seq analysis pipeline built with Nextflow and Docker, supporting QC, alignment, quantification, differential expression, transcript discovery, and automated reporting.

End-to-end RNA-seq differential expression analysis workflow for RUNX3 knockout gastric cancer cells using STAR, MultiQC, featureCounts and DESeq2.

Workflow for differential gene expression analysis for non-replicate samples using DEGseq

Reproducible RNA-seq workflow using STAR, featureCounts, and OOP Python deployed on AWS

STAR + featureCounts + DESeq2 bulk RNA-seq as a Snakemake DAG, with SLURM/AWS profiles, SQLite, PySpark, and a verified Airflow DAG

counting Insertion (I), deletion (D), splcing(N) events from CIGAR string in sam file generated by STAR aligner

Bulk RNA-seq pipeline: transcriptional characterization of anti-PD1 responders vs non-responders in metastatic melanoma