Skill Guide

Next-generation sequencing (NGS) data processing (FASTQ, BAM, VCF, CRAM formats)

NGS data processing is the computational workflow for cleaning, aligning, and analyzing high-throughput sequencing data to identify genetic variants, using standardized file formats for raw reads (FASTQ), alignments (BAM/CRAM), and variants (VCF).

This skill directly enables precision medicine, agricultural genomics, and fundamental biological research by transforming raw sequencing data into actionable insights. It reduces diagnostic turnaround time, lowers per-sample analysis costs, and is critical for compliance in clinical reporting pipelines.

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How to Learn Next-generation sequencing (NGS) data processing (FASTQ, BAM, VCF, CRAM formats)

Focus on understanding the core data lifecycle: 1) Raw FASTQ quality control (FastQC, Trimmomatic). 2) The concept of reference genome alignment (Bowtie2, BWA). 3) The purpose and structure of the major file formats (FASTQ, SAM/BAM, VCF).

Move to reproducible workflow construction. Use workflow managers (Snakemake, Nextflow) to script a complete WGS/WES pipeline. Key mistakes to avoid: improper handling of multi-threading/parallelization, failing to index reference genomes, and neglecting to apply variant filtration (GATK VariantRecalibrator).

Master pipeline optimization and strategic tool selection for specific clinical/research questions (e.g., somatic vs. germline, shallow vs. deep sequencing). Architect cloud-native (AWS/GCP) and HPC-integrated solutions. Implement rigorous QC metrics and version control for clinical validation (IVD/CE-IVD compliance).

Practice Projects

Beginner

Project

Build a Basic WGS Variant Calling Pipeline on Local Data

Scenario

You have paired-end FASTQ files from a human whole-genome sequencing run. Your task is to generate a filtered VCF file of SNPs and indels.

How to Execute

1. Download a small public dataset (e.g., from SRA). 2. Run FastQC, then Trim Galore to clean reads. 3. Align to hg38 using BWA-MEM, sort with samtools, and mark duplicates with Picard. 4. Call variants with GATK HaplotypeCaller, then filter using GATK VariantFiltration. Document each step and runtime.

Intermediate

Project

Containerize and Orchestrate a Production-Ready Exome Pipeline

Scenario

Scale the beginner project to handle multiple exome samples reproducibly across different computing environments.

How to Execute

1. Dockerize each tool in the pipeline. 2. Build a Nextflow/Snakemake workflow that processes samples in parallel, handling batch effects. 3. Integrate with a cloud storage bucket (e.g., S3) for input/output. 4. Add comprehensive QC reporting (MultiQC) and implement job retries/ checkpointing for long runs.

Advanced

Project

Architect a Clinical-Grade Somatic Variant Pipeline with QC Gates

Scenario

Design a pipeline for tumor-normal paired samples in a CAP/CLIA lab environment. The pipeline must integrate variant calling, annotation, and automated clinical report generation with strict QC thresholds.

How to Execute

1. Implement callers like Mutect2 (GATK) and Strelka2 for consensus. 2. Build automated QC gates (e.g., tumor purity, coverage depth) that halt the pipeline if metrics fail. 3. Integrate annotation (VEP, ANNOVAR) and tier classification (AMP/ASCO/CAP guidelines). 4. Develop a web-based dashboard for result visualization and audit trails. Deploy with Kubernetes for scalability.

Tools & Frameworks

Core Bioinformatics Tools

BWA-MEM2GATK (Genome Analysis Toolkit)samtoolsFastQC

The workhorses for alignment (BWA-MEM2), variant calling and refinement (GATK), file manipulation (samtools), and quality control (FastQC). Industry standard for germline and somatic analysis.

Workflow Managers & Automation

Nextflow (with nf-core)SnakemakeCromwell (WDL)

Essential for creating reproducible, scalable, and cloud-compatible pipelines. nf-core provides curated community workflows for common NGS analyses.

Cloud & Infrastructure

AWS Batch / Google Life Sciences APIDocker / SingularityTerra (Broad Institute platform)

For elastic compute scaling. Containers ensure reproducibility. Terra provides integrated environments for running GATK workflows at scale.

Interview Questions

Answer Strategy

Test diagnostic thinking and pipeline optimization knowledge. Sample Answer: 'First, I'd use Picard MarkDuplicates to identify duplicates and verify the library complexity. For mapping quality, I'd check for sample contamination or incorrect reference genome. To prevent recurrence, I would implement tighter library prep QC, use UMIs for duplicate marking, and adjust alignment parameters (e.g., -M in BWA) for better handling of split reads.'

Answer Strategy

Tests depth of knowledge on data management and cost optimization. Sample Answer: 'CRAM achieves ~60% compression over BAM by referencing a shared genome, drastically reducing storage and egress costs. I'd advocate for CRAM in archival settings and large biobanks. The trade-off is a dependency on the reference file for decompression and slightly higher CPU usage. For active analysis pipelines where random access is critical, BAM remains simpler.'