18 posts tagged with "nextflow"

Recent national genome initiatives highlight the growing need for population-specific genomic resources. After reading VN1K: a genome graph-based and function-driven multi-omics and phenomics resource for the Vietnamese population and EGP1K: Whole-Genome Sequencing of 1,024 Egyptians Characterizes Population Structure and Genetic Diversity, it becomes clear that building a national-scale 1000-genome project is increasingly important for understanding genetic diversity, improving disease research, and enabling precision medicine.

While nf-core provides a comprehensive, well-maintained library of Nextflow modules for bioinformatics, organizations often need domain-specific variants tailored to their analysis pipelines and computational infrastructure. The gianglabs/nf-modules repository demonstrates how to extend the nf-core framework by integrating both shared nf-core modules and custom organization-specific modules optimized for genomic data processing, particularly variant discovery and annotation workflows. This post explores the core differences between standard nf-core modules and the customized gianglabs implementation, including specialized module variants, domain-focused subworkflows, and adapted CI/CD practices.

In Part 2 of our CI/CD series, we showed how to set up automated testing with make test-e2e—running your workflow with test data and checking if it produces results. If the script runs without crashing, you might think "everything works fine." But here's the uncomfortable truth: a pipeline that runs successfully doesn't mean it produces correct results.

This post explains why testing goes far beyond "running code and checking it doesn't crash." We'll explore the different types of tests bioinformaticians should care about and show practical examples of how to catch real bugs that simple end-to-end tests would miss.

Creating and maintaining a library of reusable Nextflow modules is a significant challenge for bioinformatics teams. Without a consistent structure, code quality standards, and automated testing, modules quickly become difficult to share, validate, and integrate into pipelines. The nf-modules-template solves this by providing a production-ready template that uses Copier to scaffold new module repositories, GitHub Actions for automated CI/CD workflows, pre-commit hooks for code quality, and nf-test with intelligent sharding for scalable module testing. This post explores how these technologies work together to enable reproducible, maintainable Nextflow module libraries.

Structural variants (SVs) are large-scale genomic alterations (≥50 bp) including deletions, duplications, inversions, and translocations. While short-read sequencing has revolutionized SNP and small indel detection, structural variant calling remains a significant challenge. This blog explores SV calling tools for germline samples with 30X short reads, benchmarks five popular callers (Manta, TIDDIT, Delly, Smoove, CNVnator), and reveals fundamental limitations in both detection tools and evaluation methods. The results show that even the best-performing tool (Manta) achieves only 36.8% precision when evaluated with Truvari, while alternative evaluation methods from 2019 report >80% precision - highlighting the complexity and ambiguity inherent in SV analysis.

Whole Genome Sequencing (WGS) projects generate massive amounts of data. While analysis costs are significant, storage costs often become the dominant expense over time. The key challenge: you need to preserve raw data and alignments for potential re-analysis with new tools, but you can't afford unlimited storage. This blog post explores how CRAM format provides a solution, achieving 45% storage savings compared to BAM while maintaining full lossless compression and re-alignment capability. Therefore, on the new version, nf-germline-short-read-variant-calling supports cram file for better storage cost and re-analysis.

Structural variants (SVs) — deletions, insertions, duplications, inversions, and translocations — are large genomic alterations (typically ≥50 bp) that play a major role in disease but are much harder to detect than SNPs or small indels. In this post, we benchmark Manta, the SV caller integrated in nf-core/sarek, against the GIAB HG002 truth set using Truvari, and explore why short-read SV calling remains a fundamentally difficult problem.

After calling variants with high accuracy from the previous benchmark, the next step is variant annotation—understanding what each variant tells us about the sample. Variant annotation can be broadly categorized into two approaches: (1) using computational tools to predict the functional effect of variants, and (2) cross-referencing against databases of known variant effects.

In many bioinformatics workflows, pipelines keep getting more complex — more preprocessing steps, more tools, more layers of abstraction. But sometimes a simple question is worth asking: Do we actually need all of that? While benchmarking germline variant calling on the HG002 sample from the Genome In A Bottle (GIAB) truth set. Surprisingly, the results were very similar to the ones produced by nf-core/sarek, achieving >99% accuracy for SNPs and INDELs on HG002.

We have a series for variant calling, however, one of the most important thing is not about how your workflow is advanced with modern framework, it is about the scoring system that show your workflow achieve high quality score with gold standard criteria. Therefore, in this series, I used the Genome In A Bottle (GIAB) with the truth set variants are curated. With nf-core/sarek-it shows the consistency with the results has been published in its paper with more than 99% in SNP/INDEL variants.

In Part 3, we have a working pipeline, but we're missing a key concept: testing and linting workflows. This blog will show you why these practices are essential and how to implement them.

In Part 1, we built a solid bash baseline. In Part 2, we migrated to Nextflow with MD5 validation. Now it's time to deploy on HPC clusters with SLURM and optimize for production scale: configure executors for small clusters, tune resources per tool, replace bottleneck steps with faster alternatives (fastp + Spark-GATK), and demonstrate scaling from 1 to 100 samples. This practical guide will help you run your variant calling pipeline efficiently on real HPC infrastructure.

In Part 1, we built a complete 10-step GATK variant calling pipeline in bash—perfect for academic research and 1-10 samples. But what happens when you need to scale to 100+ samples? This is where Nextflow becomes essential.

📁 Repository: All code from this tutorial is organized in the nf-germline-short-read-variant-calling repository. The structure follows best practices with separate directories for bash (bash-gatk) and Nextflow (nextflow-gatk) implementations.

Setting up a robust development environment for Nextflow training across local and HPC systems requires a unified solution. Code-server provides a browser-based VS Code interface accessible from any machine, making it perfect for teams collaborating on Nextflow workflows. This guide walks you through configuring a complete Nextflow training environment with code-server, Singularity containers, and Pixi-managed tools.

For a comprehensive introduction to Pixi and package management, see our Pixi new-conda era.

Whether your lab uses bash scripts, Python workflows, Snakemake pipelines, or custom solutions—your in-house pipeline works fine locally. It's been running for years. But as your research scales, you face a hard truth: in-house pipelines don't scale, aren't reproducible across teams, and require constant manual fixes.

Docker containers are revolutionizing bioinformatics by automating reproducibility and portability across platforms. But what problems can they actually solve? This post shows real-world applications of containers in bioinformatics workflows, then guides you through the simplest possible ways to use, build and debug them.

Amazon S3 (Simple Storage Service) is built around the concept of storing files as objects, where each file is identified by a unique key rather than a traditional file system path. While this architecture offers scalability and flexibility for storage, it can present challenges when used as a standard file system, especially in bioinformatics workflows. When running Nextflow with S3 as the input/output backend, there are trade-offs to consider—particularly when dealing with large numbers of small files. In such cases, Nextflow may spend significant time handling downloads and uploads via the AWS CLI v2, which can impact overall workflow performance.On this blog post, we will start with downloading input first. Let’s explore this in more detail.

Many bioinformatics tools provide options to adjust the number of threads or CPU cores, which can reduce execution time with a modest increase in resource cost. But does doubling computational resources always result in processes running twice as fast? In practice, the speed-up is often less than linear, and each tool behaves differently.