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Discipline 08 of 18 38 Tools Stage 8: De Novo Assembly & Pangenomic Graphs

🧱 8. Genome Assembly, Scaffolding, Polishing & Pangenomes

De novo telomere-to-telomere long-read assembly, diploid haplotype phasing, chromosome conformation scaffolding, and graph-based pangenome variation representation.

Essential Software: hifiasm Flye Verkko SPAdes MEGAHIT Minigraph-Cactus
Comparative Analysis Production Benchmarks

Algorithm Architecture & Decision Matrices

Detailed comparison cards highlighting algorithmic trade-offs, strengths, weaknesses, and recommended production setups.

De Novo Assemblers (Short, Long & Hybrid)
hifiasm (HiFi/Hi-C) Flye / metaFlye Verkko (T2T) Canu / HiCanu Shasta Raven MEGAHIT SPAdes / hybridSPAdes IDBA-UD Unicycler OPERA-MS
De Bruijn graph traversal, string graph phasing, telomere-to-telomere diploid chromosome-level assembly.
Scaffolding & Polishing
YaHS SALSA2 3D-DNA Juicebox Medaka Racon Pilon Polypolish NextPolish DeepPolisher IPA (PacBio)
Hi-C contact graph ordering, neural consensus polishing, short-read insert verification, and homopolymer indel correction.
QA Metrics, Annotation & Pangenomes
BUSCO compleasm Merqury (QV) QUAST Inspector WhatsHap HapCUT2 BRAKER3 MAKER / Liftoff RepeatMasker / EDTA minigraph-cactus PGGB / vg
Conserved ortholog completeness, k-mer base accuracy (QV60+), structural phasing, and reference pangenome variation graphs.
Hardware & Execution Profiles

Primary Software & Algorithms in Genome Assembly & Pangenomics

Direct command-line recipes, Conda environment specifications, memory footprints, and license metadata.

De Novo Phased Assembler
🚀 32GB+ RAM C • Phased String Graph

hifiasm

Docs ↗ By Haoyu Cheng & Heng Li (Harvard)
(License: MIT)

Haplotype-resolved de novo genome assembler designed for PacBio HiFi reads. Produces fully phased maternal and paternal haplotype assemblies with ultra-low error rates.

Formats: Input: HiFi FASTQ, Hi-C FASTQ • Output: GFA, FASTA
💻 16-32 CPU Cores • 64-128 GB RAM
conda install -c bioconda hifiasm
Long-Read Assembler
🚀 32GB+ RAM Python / C++ • Repeat Graph

Flye

Docs ↗ By Mikhail Kolmogorov (NIH)
(License: BSD-3-Clause)

Fast and robust de novo assembler for single-molecule long reads (ONT and PacBio). Builds repeat graphs from error-prone reads without preliminary error correction.

Formats: Input: FASTQ, FASTA • Output: assembly.fasta, assembly_graph.gfa
💻 16-32 CPU Cores • 32-64 GB RAM
conda install -c bioconda flye
Microbial Assembler
💻 16-32GB RAM C++ • Multi-k-mer de Bruijn

SPAdes

Docs ↗ By Prjibelski et al. (St. Petersburg)
(License: GPL-2.0)

Versatile de novo assembler for bacterial, fungal, viral, single-cell MDA, and metagenomic short reads using iterative multi-sized de Bruijn graphs.

Formats: Input: Paired FASTQ • Output: contigs.fasta, scaffolds.fasta
💻 8-16 CPU Cores • 16-64 GB RAM
conda install -c bioconda spades
Metagenome Assembler
💻 16-32GB RAM C++ • Succinct de Bruijn Graph

MEGAHIT

Docs ↗ By Dinghua Li et al. (HKU)
(License: GPL-3.0)

Ultra-fast, ultra-low memory de novo assembler for large-scale metagenomic data (soil, ocean, gut). Capable of assembling terabytes of sequencing reads on a single server.

Formats: Input: FASTQ (.gz) • Output: final.contigs.fa
💻 16-32 CPU Cores • 32-64 GB RAM
conda install -c bioconda megahit
Nanopore Long-Read Assembler
🚀 32GB+ RAM C++ • Marker Graph Alignment

Shasta

Docs ↗ By Chan Zuckerberg Biohub
(License: MIT)

Nanopore de novo long-read assembler designed to produce a human genome assembly in hours using marker k-mers and Linux shared memory POSIX primitives.

Formats: Input: Nanopore FASTQ • Output: Assembly.fasta, Assembly.gfa
💻 32+ CPU Cores • 128-256 GB RAM (Shared memory)
conda install -c bioconda shasta
Reference-Guided Scaffolder
💻 < 8GB RAM Python • Minimap2 Wrappers

RagTag

Docs ↗ By Michael Alonge (JHU)
(License: GPL-3.0)

Tool for reference-guided scaffolding, assembly improvement, and structural variant discovery. Orders and orients draft contigs against high-quality reference genomes.

Formats: Input: Reference FASTA, Query FASTA • Output: ragtag.scaffold.fasta
💻 4-8 CPU Cores • 8-16 GB RAM
conda install -c bioconda ragtag
Assembly Graph Visualizer
💻 < 8GB RAM C++ • Qt GUI & CLI

Bandage

Docs ↗ By Ryan Wick (Melbourne)
(License: GPL-3.0)

Bioinformatics Application for Navigating De novo Assembly Graphs Easily. Visualizes GFA/FASTG contig connection paths, repeat bridges, and blast hits interactively.

Formats: Input: GFA, FASTG • Output: Vector SVG, PNG, BLAST mappings
💻 2-4 CPU Cores • 4-8 GB RAM
conda install -c bioconda bandage
Recommended Workflow Progression

Proceed to Stage 9: Metagenomics, Microbiome & Functional Pathways

Sequencing pipelines proceed sequentially. Move forward to the next analytical domain to explore downstream algorithms and execution profiles.

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