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.

This blog is designed as a practical starting point for building bioinformatics workflows focused on germline variant calling. You'll begin with a straightforward, standard approach using bash and reproducible environments. In future posts, we'll explore how to transition to best-practice workflow management with Nextflow, allowing for further optimization, customization, and integration of additional tools to enhance workflow quality.

Tired of basic Bash prompts and repetitive typing? Discover how upgrading to Zsh (Z Shell) can transform your terminal experience with smart autocomplete, syntax highlighting, and beautiful themes. Install it without admin access using Pixi!

HTSlib-based tools like bcftools and samtools provide powerful capabilities for working with genomic data stored on remote servers. Whether your data is in AWS S3, accessible via FTP, or hosted on HTTPS endpoints, these tools allow you to efficiently query and subset remote files without downloading entire datasets. This guide covers authentication, remote file access patterns, and practical workflows.

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.

Running interactive web applications like RStudio, JupyterLab, and Code Server in containers is a powerful way to provide reproducible analysis environments. However, users often need to spawn additional containerized tools from within these applications. Docker out of Docker (DooD) elegantly solves this by allowing containers to access the host's Docker daemon. This post explains how to set up DooD for interactive web applications and why it's the right approach for bioinformatics workflows.

Container technologies have revolutionized software deployment and reproducibility in scientific computing. However, traditional Docker faces significant limitations in High-Performance Computing (HPC) environments. This post explores why Docker struggles on HPC systems and introduces modern alternatives like Docker rootless, Singularity, and Apptainer.

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.

Unix pipes (|) are one of the most powerful yet underutilized features in bioinformatics. They allow you to chain multiple commands together, processing data in a streaming fashion that dramatically reduces memory usage and disk I/O. This post explores why pipes are essential for bioinformatics work and shows how they work under the hood.

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.

Building reproducible bioinformatics pipelines is hard. Every project starts from scratch with its own testing, CI/CD, and deployment strategy. What if you could clone a template, add your analysis tools, and be ready to go?

This post introduces a standardized bioinformatics workflow template featuring consistent testing, CI/CD, and project structure. Developed from real production experience with bioinfor-wf-template, this template reduces setup time from days to minutes, ensures research reproducibility, and promotes modular, reusable code. It is Python-based and ideal for proof-of-concept projects. Support for more advanced and widely adopted bioinformatics frameworks (such as Snakemake and Nextflow) is planned, applying the same core principles while leveraging their native testing systems.

GitHub Actions are powerful for automating bioinformatics pipelines, but waiting 5-10 minutes for each cloud run is painful during development. act lets you run GitHub Actions workflows locally on your machine in seconds, slashing feedback time by 5x.

In this post, we'll explore act, a command-line tool that runs GitHub Actions locally using Docker. Perfect for testing ML pipelines, gene expression analysis, and CI/CD workflows before pushing to GitHub.

Machine learning is transforming bioinformatics, enabling us to discover patterns in biological data. In this first part, we'll build a K-Nearest Neighbors (KNN) classifier from scratch using only Python, then apply it to simulated gene expression data. This post is designed for anyone who knows basic Python and biology—no advanced ML experience required!

Machine learning is transforming bioinformatics by automating pattern discovery from biological data. But what problems can it actually solve? This post shows real-world applications of classification models, then builds the simplest possible classifiers to understand how they work and how to evaluate them. This is Part 0—the practical foundation before diving into complex algorithms like KNN.

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.

Pixi is a fast, modern package management tool built on the conda ecosystem. Whether you're on a personal laptop or an HPC cluster, Pixi makes environment setup simple and reproducible. Here’s a primer on using Pixi. For a full workflow, see: Setting up single-cell RNA-seq analysis with Pixi as collaboration between RIVERXDATA and NGS101

Welcome to Part 2 of our series on version control in bioinformatics. In Part 1, we introduced Git fundamentals, branching strategies, and collaborative workflows. In this post, we'll dive into how Continuous Integration and Continuous Deployment (CI/CD) can transform your bioinformatics projects. If these concepts are new to you, don't worry—this guide will walk you through managing your bioinformatics repository to ensure your work is easily reproducible on any machine. Whether your server is wiped or you need to spin up a new virtual machine, you'll be able to quickly rerun your pipeline. With CI/CD, every code update can automatically trigger tests on a small dataset to verify everything works before scaling up, ensuring that new changes don't break your results or workflows.

In Part 1 (this post), we explore the history of Git, its integration with GitHub, and basic hands-on tutorials. Part 2 (coming soon) will cover real-world bioinformatics examples and advanced workflows with best practices.

This part focuses on practical applications, including NGS quality control using multiqc and fastqc.

In Part 1 and Part 2, we built a complete Slurm HPC cluster from a single node to a production-ready multi-node system. Now let's learn how to manage, maintain, and secure it effectively.

This final post covers daily administration tasks, troubleshooting, security hardening, and integration with data processing frameworks.

In Part 1, we learned the fundamentals by building a single-node Slurm cluster. Now it's time to scale up to a production-ready, multi-node cluster with automated deployment, monitoring, and alerting.

In this post, we'll use Ansible to automate the entire deployment process, making it reproducible and maintainable.

Building a High-Performance Computing (HPC) cluster can seem daunting, but with the right approach, you can create a robust system for managing computational workloads. This is Part 1 of a 3-part series where we'll build a complete Slurm cluster from scratch.

In this first post, we'll cover the fundamentals by setting up a single-node Slurm cluster and understanding the core concepts.