Assembly Strategies

Last updated on 2025-02-16 | Edit this page

Estimated time: 12 minutes

Overview

Questions

What factors influence the choice of genome assembly strategy?
How do different assembly methods compare in terms of read length, accuracy, and computational requirements?
What are the key steps in evaluating genome assemblies using BUSCO and QUAST?
How do Bionano OGM and Hi-C sequencing improve genome continuity and organization?

Objectives

Understand factors influencing the choice of genome assembly strategy.
Compare different assembly methods based on read length, accuracy, and computational requirements.
Learn how to evaluate genome assemblies using BUSCO and QUAST.
Explore the role of Bionano OGM and Hi-C sequencing in improving genome continuity.

Assembly Strategies

Genome assembly involves choosing the right approach based on sequencing technology, read type, genome complexity, and research objectives. This chapter introduces key factors influencing assembly strategy selection, from read length and coverage requirements to computational trade-offs. We will explore different methods—PacBio HiFi with HiFiasm, ONT with Flye, and hybrid assemblies—along with scaffolding techniques like Bionano Optical Genome Mapping (OGM) and Hi-C, which help improve genome continuity and organization. Finally, we’ll discuss assembly evaluation tools such as BUSCO and QUAST to assess the completeness and quality of assembled genomes.

Factors Influencing the Choice of Strategy

Read length: Affects the ability to resolve repeats; short reads struggle with complex regions, while long reads improve contiguity.
Coverage depth: Crucial for assembly accuracy, with HiFi requiring 20-30x, ONT needing 50-100x, and hybrid assemblies depending on short-read support for polishing.
Genome complexity: Includes repeat content, heterozygosity, and polyploidy, which influence assembly success and determine whether specialized tools or additional scaffolding methods are needed.
Computational resources: Vary across assembly strategies, with HiFiasm being RAM-intensive, Flye being more lightweight, and hybrid assemblies requiring additional processing for polishing.
Sequencing budget: Plays a role, as HiFi sequencing is costlier but highly accurate, ONT is cheaper but requires more data for error correction, and hybrid approaches balance cost and quality.
Downstream analyses: Structural variation detection, gene annotation, or chromosome-level assemblies influence the choice of assembler and the need for scaffolding methods like Hi-C or Bionano OGM.

Comparative Assembly Strategies

Factor	PacBio HiFi	ONT	Hybrid (ONT + Illumina)
Read length	~15-20kb	10-100kb+	Mix of short & long
Read accuracy	High (~99%)	Moderate (~90%)	High (after polishing)
Coverage needed	20-30x	50-100x	ONT: 50x + Illumina: 30x
Cost per Gb	Expensive	Lower	Medium
Error profile	Random errors, low indels	Higher error rate, systematic errors	ONT errors corrected by Illumina
Computational requirements	High RAM required	Moderate RAM required	Moderate
Best for	High-accuracy assemblies	Ultra-long contigs	Combines advantages of both
Repeat resolution	Good	Very good	Very good
Scaffolding needed	Rarely needed	May be needed	Sometimes needed
Polishing required	Not required	Required (Racon + Medaka)	Required (Pilon)
Structural variant detection	Good	Excellent	Good
Haplotype phasing	Excellent	Good	Moderate
Genome size suitability	Suitable for large and small genomes	Best for large genomes	Best for complex genomes
Downstream applications	Reference-quality genome assembly, annotation	Structural variation analysis, de novo assembly	Genome correction, variant calling, scaffolding

Contig vs. Scaffold vs. Chromosome-Level Assembly

Genome assemblies progress through different levels of completeness and organization:

Contig-level assembly: The raw output of assemblers, consisting of contiguous sequences without known order or orientation. Longer contigs indicate better assembly continuity.
Scaffold-level assembly: Contigs linked together using additional data (e.g., long-range mate-pair reads, optical maps, or Hi-C). Gaps (represented as ’N’s) remain where connections exist but sequence information is missing.
Chromosome-level assembly: The highest-quality assembly, where scaffolds are further ordered and oriented into full chromosomes using genetic maps, Hi-C data, or synteny with a reference genome.

Higher levels of assembly provide better genome context, but require additional scaffolding methods beyond de novo assembly.

Workflow for Various Assemblies

In this workshop, we will use HiFiasm for PacBio HiFi assemblies, Flye for ONT assemblies, and Flye in hybrid mode for ONT + Illumina assemblies, followed by quality assessment using BUSCO and QUAST to evaluate completeness and accuracy.

PacBio HiFi Assembly with HiFiasm

PacBio HiFi reads: Highly accurate long reads with low error rates, suitable for de novo assembly without polishing.
HiFiasm: A specialized assembler for HiFi data, leveraging read accuracy and length to resolve complex regions and produce high-quality contigs.
Workflow: Run HiFiasm with HiFi reads, adjust parameters based on genome size and complexity, and evaluate the assembly using Compleasm and QUAST.

ONT Assembly with Flye

ONT reads: Ultra-long reads with higher error rates, requiring additional error correction and polishing steps.
Flye: A de novo assembler optimized for long reads, capable of resolving complex repeats and generating high-quality assemblies.
Workflow: Run Flye with ONT reads, adjust parameters based on genome size and complexity, and polish the assembly using Medaka for basecalling and consensus polishing.

Hybrid (ONT + PacBio) Assembly with Flye

Hybrid assembly: Combines the strengths of both technologies for improved accuracy and contiguity.
Workflow: Run Flye with both ONT and PacBio reads, adjust parameters for hybrid mode, and polish the assembly using ONT or PacBio reads for error correction and consensus polishing.

Assembly Evaluation

Why assessing genome assembly quality is crucial before downstream analyses.
Metrics to determine assembly completeness, accuracy, and contiguity.

1. BUSCO (Benchmarking Universal Single-Copy Orthologs)/ Compleasm

Compleasm is a faster alternative to BUSCO for assessing genome completeness based on single-copy orthologs. It evaluates genome completeness by checking for highly conserved, single-copy genes expected to be present in nearly all members of a given lineage. These genes are essential for basic cellular functions, making them reliable markers for assessing genome assembly quality.
We expect these genes to be present in our organism because they are evolutionarily conserved and critical for survival. If many BUSCO genes are missing or fragmented, it suggests gaps, misassemblies, or sequencing errors, which can compromise downstream analyses like gene annotation and functional studies. A high BUSCO completeness score indicates a well-assembled genome with minimal missing data.
The BUSCO reports provide detailed statistics on the number of complete, fragmented, and missing BUSCO genes, as well as the percentage of genome completeness. The output helps identify areas for improvement and guide further optimization steps in the assembly process.

2. QUAST (Quality Assessment Tool for Genome Assemblies)

Comprehensive Assembly Statistics: QUAST provides detailed metrics beyond N50 and L50, including total number of contigs, GC content, genome size estimation, and misassembly rates, allowing in-depth evaluation of genome continuity and structure.
Reference-Based and Reference-Free Evaluation: It can assess assemblies against a reference genome (identifying misassemblies, inversions, and duplications) or work in reference-free mode, making it useful for de novo assemblies without a known genome sequence.
Structural Error Detection and Gene Feature Analysis: QUAST integrates gene annotation tools like BUSCO and GeneMark, highlights misassemblies based on alignment breaks, and detects gaps, relocations, and translocations, making it particularly useful for validating scaffolding approaches and hybrid assemblies.

3. Merqury: K-mer based assembly evaluation

Assembly Accuracy Check: Merqury compares k-mers from sequencing reads to the assembled genome, identifying mismatches, missing k-mers, and sequencing errors without requiring a reference genome.
Haplotype Purity and Phasing: It calculates QV (quality value) scores and provides completeness metrics for haplotypes, helping assess whether an assembly accurately represents both parental haplotypes or contains chimeric sequences.
Consensus and Read Support Validation: By analyzing k-mer spectra, Merqury detects underrepresented or overrepresented regions, highlighting assembly errors, collapsed repeats, or sequencing biases that may impact downstream analyses.

Bionano and Hi-C Reads in Genome Assembly

Bionano Optical Genome Mapping (OGM) provides ultra-long, label-based maps of DNA molecules, helping to scaffold contigs, detect misassemblies, and resolve large structural variations. It improves genome continuity by linking fragmented sequences, especially in repeat-rich or complex genomes.

Hi-C sequencing captures chromatin interactions, allowing scaffolding of contigs into chromosome-scale assemblies based on physical proximity in the nucleus. It helps in ordering and orienting scaffolds, identifying misassemblies, and resolving haplotypes, making it essential for generating chromosome-level genome assemblies.

Bionano and HiC for assembly improvement

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Key Points

Genome assembly strategy depends on read type, genome complexity, and computational resources, with PacBio HiFi, ONT, and hybrid approaches offering different advantages in accuracy, cost, and contiguity.
Assembly evaluation is critical for assessing completeness and accuracy, using tools like BUSCO for gene completeness, QUAST for structural integrity, and Merqury for k-mer-based validation.
Scaffolding methods like Bionano OGM and Hi-C improve genome organization, resolving large structural variations and ordering contigs into chromosome-level assemblies.
A well-assembled genome is essential for downstream applications such as annotation, comparative genomics, and structural variation analysis, with missing or misassembled regions potentially leading to incorrect biological conclusions.