From Sequencing Data to Clinical Insight

ZETOBIT LLC Bioinformatics Insight Series · 2026

2026

From Sequencing Data to Clinical Insight —
How a Bioinformatics Pipeline Actually Works

Genomic sequencing has become remarkably accessible. But the sequencing instrument is only the beginning. The bioinformatics pipeline — the computational infrastructure that transforms raw sequence reads into actionable biological insights — is where the real scientific work happens.

Whether you are a VP of R&D evaluating a new NGS-based assay, a principal investigator preparing a grant, or a business development executive trying to understand what your bioinformatics partner actually does, this is your guide.

What Is a Bioinformatics Pipeline?

A bioinformatics pipeline is a structured sequence of computational steps that process raw sequencing data — typically FASTQ files from an Illumina or Oxford Nanopore instrument — and produce interpretable biological results. These results might include a list of somatic mutations, a table of differentially expressed genes, a copy number profile, or a set of fusion transcripts.

Pipelines are not monolithic programs. They are composed of multiple specialized software tools, each performing a distinct function, stitched together through workflow management systems such as Nextflow, Snakemake, or WDL. The tools are open-source and well-validated; the expertise lies in knowing which tools to use, how to configure them for your specific assay and organism, and how to interpret the outputs in their scientific context.

Figure 1 — End-to-End Bioinformatics Pipeline

The standard stages of an NGS bioinformatics pipeline, from raw reads to clinical or scientific report.

Stage	Input	Output / Tool
Raw reads	FASTQ files from sequencer	Quality metrics — FastQC
QC & trimming	Adapter sequences, low-quality reads	Trimmed FASTQ — Trimmomatic / Fastp
Alignment	Trimmed reads + reference genome (hg38)	BAM file — STAR / BWA-MEM2
Deduplication	Aligned BAM	Deduplicated BAM — Picard / samblaster
Variant / expression calling	Deduplicated BAM	VCF / count matrix — GATK / STAR-Fusion / featureCounts
Annotation	Raw variant calls or counts	Annotated results — VEP / ANNOVAR / DESeq2
Reporting	Annotated results + metadata	Clinical/scientific report — MultiQC + custom Rmd

Stage by Stage

• Raw Reads Raw reads arrive as FASTQ files — text files encoding both the DNA sequence and a quality score for each base call. The first step is quality control: FastQC generates summary statistics on read length distribution, per-base quality, GC content, and adapter contamination. Problems caught here prevent downstream analytical errors.
• Trimming Adapter trimming removes synthetic oligonucleotide sequences introduced during library preparation. These sequences are not biological and will cause misalignment if left in. Tools such as Trimmomatic and Fastp are highly configurable and can be tuned to the specific library preparation kit used.
• Alignment Alignment maps trimmed reads to the reference genome. For RNA-seq, STAR is the standard aligner, capable of detecting splice junctions. For DNA sequencing (WGS or WES), BWA-MEM2 provides fast, accurate alignment to hg38.
• Deduplication Deduplication removes PCR duplicate reads that artificially inflate coverage and variant allele frequencies. For liquid biopsy applications using unique molecular identifiers (UMIs), this step is especially critical.
• Variant Calling Variant or expression calling transforms the aligned reads into the scientific signal of interest. For DNA, GATK HaplotypeCaller or Mutect2 identifies germline or somatic variants. For RNA, featureCounts or RSEM quantifies gene expression, while STAR-Fusion identifies chimeric transcripts.
• Annotation Annotation translates raw variant calls or expression values into biological meaning — mapping variants to genes, functional consequences, population frequencies, and clinical significance databases such as ClinVar and COSMIC.

Why QC Is Not Optional

Quality control is the most underappreciated component of any NGS pipeline. A poorly sequenced library that passes visual inspection can generate tens of thousands of false-positive variants or dramatically distort expression estimates. Systematic QC checkpoints — with defined pass/fail thresholds — are what separate research-grade pipelines from clinical-grade ones.

Figure 2 — Key QC Metrics and Acceptance Thresholds

Standard quality control checkpoints and action thresholds for NGS pipelines.

QC Metric	Acceptable Range	Warning Threshold	Action if Failed
Total reads	>20M (RNA-seq)	10–20M	Re-sequence or flag for low-depth analysis
% mapped reads	>80%	60–80%	Check contamination or genome build
% duplicates	<30%	30–50%	Review library prep; check input amount
Insert size	150–350 bp (WGS)	<150 or >400 bp	Fragmentation issue; review protocol
Uniformity of coverage	>90% at 0.2× mean	80–90%	Investigate capture bias or GC content

Zetobit Standard At Zetobit, every pipeline we build or deploy includes automated MultiQC reporting at each major stage, with defined acceptance criteria specific to the assay type and downstream application. Samples that fail QC are flagged before analysis proceeds, protecting the integrity of downstream results.

The Reporting Layer

The final output of a bioinformatics pipeline is not a file — it is a scientific interpretation. Raw variant tables and expression matrices must be translated into conclusions that are scientifically defensible, clinically relevant, and actionable for your program. This interpretive layer — combining domain expertise in oncology, immunology, or rare disease with fluency in computational methods — is where Zetobit adds its deepest value.

References

1. Andrews S. FastQC: a quality control tool for high throughput sequence data. Bioinformatics. 2010. babraham.ac.uk/projects/fastqc
2. Dobin A, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
3. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler Aligner. Bioinformatics. 2009;25:1754–1760.
4. Van der Auwera GA, et al. From FastQ data to high-confidence variant calls: the GATK best practices pipeline. Current Protocols in Bioinformatics. 2013;43:11.10.1–11.10.33.
5. Ewels P, et al. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–3048.
6. Cock PJ, et al. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Research. 2010;38:1767–1771.

© 2026 Zetobit LLC · Bioinformatics Insight Series · Lexington, KY