FA-NIVA: A Nextflow framework for automated analysis of Nanopore based long-read sequencing data for genetic analysis in Fanconi anemia

FA-NIVA is a reproducible, Nextflow-based automated workflow that leverages Nanopore long-read sequencing to comprehensively detect and phase both single nucleotide and structural variants in Fanconi anemia genes, overcoming the limitations of current short-read methods.

The Gist

Imagine your DNA as a massive, intricate instruction manual for building and running a human body. Sometimes, a few pages in this manual get torn out, duplicated, or have words scrambled. This is what happens in Fanconi Anemia (FA), a rare genetic disease.

For years, doctors have tried to find these "typos" using a method called short-read sequencing. Think of this like trying to fix a torn book by taking tiny, 3-word snippets of text and trying to guess where they fit. It works well for small errors, but if a whole paragraph is missing (a large deletion) or if the text is in a section with lots of repeated patterns (like a chorus in a song), the snippets get lost or confused. You might know something is wrong, but you can't pinpoint exactly where the tear is or which copy of the manual is broken.

Enter FA-NIVA, a new digital tool created by a team of scientists. Here is how it works, explained simply:

1. The New Camera: Nanopore Sequencing

Instead of taking tiny snippets, the scientists use a newer technology called Nanopore sequencing. Imagine this as a high-speed camera that can read the entire book, page by page, in one long, continuous stream. It can see the whole story, including the big missing chapters and the tricky repeated sections, all at once.

2. The Problem: The "Lost" Pages

Even with this amazing camera, the software used to analyze the data had a few glitches.

• The Alignment Glitch: When the camera sees a huge missing chunk of text, the old software sometimes got confused about where the text before the gap connected to the text after it. It was like trying to paste two pieces of paper together but putting the tape in the wrong spot.
• The "Which Copy?" Glitch: Humans have two copies of every instruction manual (one from mom, one from dad). To diagnose FA, we need to know if the errors are on both copies (which causes the disease) or just one. Old software often mixed up which error belonged to which copy, especially when a big chunk was missing from one side.

3. The Solution: FA-NIVA

The researchers built FA-NIVA (Fanconi Anemia – Nanopore Indel and Variant Analysis). Think of FA-NIVA as a super-smart, automated librarian that takes the raw video feed from the Nanopore camera and organizes it perfectly.

Here is what makes it special:

• The Perfect Glue (Better Alignment): FA-NIVA uses a smarter "glue" (a tool called pbmm2) to stick the text together. Unlike the old software, it knows exactly how to handle the gaps where big chunks of DNA are missing, ensuring the "pages" are aligned correctly.
• The Detective Team (Variant Calling): It uses two specialized detectives:
  • DeepVariant looks for single-letter typos (like changing an 'A' to a 'G').
  • Sawfish looks for the big missing or extra chapters (structural variants).
    Together, they find almost every error in the manual, no matter how small or large.
• The Twin Tracker (Phasing): This is the magic trick. FA-NIVA doesn't just find the errors; it figures out which "parent's copy" they are on. It uses a special strategy to fix mistakes where the software thought a missing page was just a typo. It ensures the doctor knows: "Okay, Mom's copy has a missing chapter, and Dad's copy has a typo." This confirms the diagnosis with high confidence.
• The Automatic Report: Once the analysis is done, FA-NIVA prints a detailed report card. It tells you exactly what tools were used, how long it took, and what the results are. This is crucial for hospitals because they need to be able to prove their work is accurate and repeatable.

Why Does This Matter?

Before FA-NIVA, finding these genetic errors was like trying to solve a puzzle with missing pieces and blurry photos. It was slow, required many different tests, and sometimes the answer was still unclear.

FA-NIVA is like giving the doctor a high-definition, automated puzzle solver.

• It finds the big missing pieces and the tiny typos in one go.
• It tells you exactly which parent passed down which error.
• It does it automatically, so doctors can focus on treating the patient rather than wrestling with confusing data.

The team even tested it on other diseases (like a type of muscular dystrophy) and found it works there too, proving it's a versatile tool for solving genetic mysteries.

In short: FA-NIVA turns a complex, confusing genetic investigation into a smooth, reliable, and automated process, helping doctors diagnose Fanconi Anemia faster and more accurately than ever before.

Technical Summary

1. Problem Statement

Fanconi Anemia (FA) is a rare genetic disorder caused by biallelic pathogenic variants in at least 22 associated genes. Current diagnostic challenges include:

• Complex Variant Types: FA genes (particularly FANCA and FANCD2) frequently harbor large structural variants (SVs), such as deletions spanning hundreds of kilobases, and variants in repetitive regions (e.g., Alu-rich areas or pseudogenes).
• Limitations of Short-Read Sequencing: While short-read sequencing detects single nucleotide variants (SNVs) well, it struggles to resolve large SVs, precise genomic breakpoints, and repetitive sequences.
• Allelic Discrimination: Confirming compound heterozygosity (two different mutations on separate alleles) requires accurate phasing. Standard methods often fail to distinguish between homozygous and hemizygous states when one allele contains a large deletion.
• Lack of Standardized Workflows: Existing Nanopore pipelines (e.g., nanoseq) are general-purpose and lack specific modules for complex SV detection, breakpoint resolution, and genotype-aware phasing required for FA diagnostics.

2. Methodology: The FA-NIVA Pipeline

FA-NIVA (Fanconi anemia – Nanopore Indel and Variant Analysis) is an automated, modular bioinformatics workflow built on the Nextflow ecosystem and containerized with Docker to ensure reproducibility and scalability.

Key Technical Components:

• Input Flexibility: Accepts raw Nanopore signal files (.pod5, .fast5) and pre-aligned BAM files.
• Basecalling: Uses Dorado (GPU-accelerated) to convert electrical signals to nucleotide sequences.
• Alignment Strategy:
  • Replaces standard minimap2 with pbmm2 (preset -CCS).
  • Rationale: Standard minimap2 often misaligns reads across large deletions due to overcompensation for sequencing noise. pbmm2, tuned for high-quality reads, anchors reads more reliably across large SVs, improving breakpoint accuracy.
• Variant Calling:
  • SNVs: Uses DeepVariant, which achieved an F1 score of 0.998 in internal benchmarks.
  • SVs: Uses Sawfish, a consensus-based caller that extracts sequences near breakpoints to generate contigs. This allows precise identification of deletion breakpoints even in regions with nearly identical flanking sequences.
• Annotation: Utilizes AnnotSV for comprehensive variant annotation (gene names, regulatory elements, clinical relevance).
• Advanced Phasing (Core Innovation):
  • Implements a joint SNV-SV phasing strategy.
  • Genotype-Aware Correction: Corrects the genotypes of SNVs located within large deletions. Standard phasing algorithms often misclassify these as homozygous; FA-NIVA corrects them to hemizygous, preventing haplotagging errors.
  • Incorporates large SVs directly into the phasing process to improve resolution, overcoming the issue of sparse SV distribution.
• Reporting: Generates comprehensive reports via MultiQC, mosdepth, and copyQC, detailing resource usage, command-line parameters, Docker versions, and execution times.

3. Key Contributions

1. First Unified FA-Specific Pipeline: FA-NIVA is the first end-to-end automated workflow specifically designed for Nanopore long-read data in Fanconi Anemia, addressing the unique challenges of FANCA and FANCD2.
2. Optimized Alignment for Large Deletions: The integration of pbmm2 over standard minimap2 significantly improves the alignment of reads spanning large deletions, a critical bottleneck in FA analysis.
3. Genotype-Aware Phasing: The pipeline introduces a novel strategy to correct SNV genotypes within deletion regions, enabling accurate distinction between homozygous and hemizygous states, which is essential for confirming compound heterozygosity.
4. Modular and Reproducible Architecture: Built on Nextflow with Docker containers, the tool ensures that analyses are transparent, reproducible across different computing environments, and easily adaptable for other genetic disorders.

4. Results and Validation

The authors validated FA-NIVA through three specific use cases:

• Use Case 1 (Bi-allelic FANCA): Successfully detected a known 77.8 kb deletion spanning FANCA exon 3 to the 3'-UTR and a non-coding SNV. It provided nucleotide-level breakpoint precision (QUAL 251), resolving limitations of MLPA-only detection.
• Use Case 2 (FANCD2 Insertion): Detected a 299 bp Alu element insertion in FANCD2 from raw signal data (QUAL 504), demonstrating the ability to identify insertions in repetitive regions.
• Use Case 3 (Non-FA Application): Applied to a patient with Limb-Girdle Muscular Dystrophy (LGMD2). The pipeline identified a 227 kb homozygous region in the DYSF gene (5'-UTR) and confirmed true homozygosity via phasing. This was validated by short-read Illumina sequencing, proving the tool's applicability beyond FA.

5. Significance

• Clinical Utility: FA-NIVA bridges the gap between long-read sequencing technology and routine clinical diagnostics by automating complex variant detection and phasing.
• Diagnostic Efficiency: It reduces manual intervention, provides high-resolution breakpoint mapping, and confirms compound heterozygosity in a single workflow.
• Scalability: The modular design allows the pipeline to be adapted for other recessive disorders, consanguineous families (homozygosity mapping), and potentially other long-read platforms (e.g., PacBio).
• Future Outlook: The framework is designed to incorporate additional features, such as methylation analysis, in future versions.

Availability: The source code and documentation are publicly available on GitHub at https://github.com/UKWgenommedizin/FA-NIVA.