Raw Sequencing Quality Control & MultiQC Metrics
High-throughput RNA sequencing begins with thorough inspection of raw FASTQ reads. Assessing base quality scores, adapter contamination, and GC content distributions ensures that downstream alignment steps operate on high-fidelity nucleotide sequences.
Automated aggregation via custom wrapper tools catches outlier sequencing runs early in the lifecycle, preventing skewed expression profiles from propagating into final statistical matrices.
Snakemake Orchestration & Modular Workflow Design
To achieve complete reproducibility across diverse computational environments, our pipelines are structured using the transcriptomics-snakemake repository. Snakemake enables scalable parallel execution from raw FASTQs to count matrices.
By decoupling rule configurations from execution scripts, containerized software environments (Conda/Docker) ensure that every dependency version remains locked and verifiable across independent high-performance computing clusters.
Alignment & Quantification with STAR & featureCounts
Accurate transcript mapping requires splice-aware aligners capable of handling complex exon-exon junctions. Our pipeline integrates STAR alignment modules mapped against reference genomes followed by precise gene-level summarization.
The resulting gene count matrix acts as the foundational quantitative dataset required for rigorous comparative modeling and disease biomarker characterization.
Differential Gene Expression Analysis via DESeq2
Identifying statistically significant transcriptional changes between experimental conditions requires generalized linear models based on negative binomial distributions, effectively accounting for biological overdispersion.
Shrinkage estimators for fold changes (such as apeglm) provide stable ranking metrics, filtering out noise driven by low counts and highlighting robust biological drivers.
Functional Enrichment, GSEA & Downstream Networks
Moving past individual gene lists, functional enrichment analysis and Gene Set Enrichment Analysis (GSEA) contextualize expression findings within canonical pathways and molecular interaction frameworks.
This systems-biology synthesis completes the transcriptomic discovery loop, turning raw sequencing runs into prioritized targets ready for downstream experimental validation.