Complete Guide to Flye Assembly for Oxford Nanopore Sequencing: Protocol, Optimization, and Validation

Andrew West Jan 12, 2026 189

This comprehensive guide provides researchers, scientists, and drug development professionals with a complete workflow for performing genome assembly using Flye with Oxford Nanopore long-read data.

Complete Guide to Flye Assembly for Oxford Nanopore Sequencing: Protocol, Optimization, and Validation

Abstract

This comprehensive guide provides researchers, scientists, and drug development professionals with a complete workflow for performing genome assembly using Flye with Oxford Nanopore long-read data. Covering foundational principles through to advanced validation, the article explores Flye's algorithm tailored for noisy long reads, details step-by-step protocols, addresses common troubleshooting scenarios, and presents comparative analyses against other assemblers. Readers will gain practical knowledge for generating high-quality contiguous assemblies essential for genomic research, structural variant detection, and complex genome analysis.

Why Flye for Nanopore? Understanding Long-Read Assembly Fundamentals

Within the broader thesis on the Flye assembly protocol for Oxford Nanopore data research, this application note provides foundational knowledge and practical protocols. The focus is on utilizing Oxford Nanopore Technologies' (ONT) long-read sequencing data for de novo genome assembly, where Flye is a central, specialized tool designed to leverage the unique characteristics of these reads.

Key Advantages of ONT forDe NovoAssembly

ONT sequencing generates long reads (often >10 kb, with some exceeding 100 kb), which is critical for spanning complex genomic regions. This is contrasted with short-read technologies in the table below.

Table 1: Comparison of Sequencing Technologies for De Novo Assembly

Feature Oxford Nanopore (ONT) Illumina (Short-Read) PacBio HiFi
Read Length Very Long (10 kb - 100+ kb) Short (75-300 bp) Long (10-25 kb) with high accuracy
Primary Error Mode Random indels (~5-15% raw error) Low-rate substitutions (<0.1%) Near-uniform (QV > 30)
Throughput/Run High (10-100+ Gb) Very High (up to 6 Tb) Moderate (up to 360 Gb)
Cost per Gb Moderate Low High
Major Assembly Benefit Resolves repeats, structural variants High base accuracy, coverage depth Combines length and accuracy
Suitable Assembler Flye, Canu, Miniasm, wtdbg2 SPAdes, Velvet, ABySS Flye, Canu, Hifiasm

Detailed Protocol: Flye Assembly for ONT Data

Flye is a de novo assembler specifically designed for noisy long reads. Its algorithm is based on repeat graphs and does not require pre-error correction, making it fast and efficient for ONT data.

Protocol 3.1: Genome Assembly using Flye

Objective: To assemble a contiguous bacterial or eukaryotic genome from ONT reads using the Flye assembler.

Materials & Reagents:

  • Input Data: ONT sequencing data in FASTQ format (basecalled with Guppy or Dorado).
  • Computing Resources: Linux-based server with sufficient RAM (e.g., 100-500 GB for mid-sized genomes) and multiple CPU cores.
  • Software: Flye (v2.9 or later) installed via Conda (conda install -c bioconda flye) or from source.

Method:

  • Data Preparation: Ensure reads are in a single .fastq or .fastq.gz file. Quality check with NanoPlot.
  • Run Flye Assembly: Execute the primary command. The example below targets a ~5 Mb bacterial genome.

  • Post-Assembly Polishing: The initial assembly (assembly.fasta) contains consensus errors. Polish using ONT reads with Medaka:

  • Output Analysis: Key output files include:
    • assembly.fasta: The final polished consensus sequence.
    • assembly_graph.gfa: The assembly repeat graph.
    • assembly_info.txt: Contig statistics (length, coverage, circular status).

Protocol 3.2: Assembly Quality Assessment

Objective: To evaluate the completeness and accuracy of the Flye assembly.

Materials & Reagents:

  • Assembled genome (assembly.fasta).
  • Reference genome (if available for comparison).
  • Software: QUAST, BUSCO.

Method:

  • Run QUAST: Provides general assembly metrics.

  • Run BUSCO: Assesses gene space completeness using universal single-copy orthologs.

  • Compare to Reference (Optional): Use dnaDiff or MUMMmer for alignment-based metrics.

Table 2: Expected Flye Assembly Metrics for a Bacterial Genome

Metric Target Value (Polished) Typical Raw Flye Output
Number of Contigs 1 (for circular chromosome) 1 - 10
Total Length Within 1-2% of expected size Close to expected size
N50 Length Equal to largest contig High (often > 1 Mb)
BUSCO Completeness >95% (for standard dataset) >90%
Indel/Substitution Rate < 0.01% (after polishing) ~0.5-2% (before polishing)

Visualizing the Flye Assembly Workflow

Diagram Title: Flye de novo assembly workflow for ONT data

Diagram Title: Flye's repeat graph approach to assembly

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for ONT De Novo Assembly

Item Function in Protocol Example Product/Kit
Sequencing Kit Prepares genomic DNA for loading onto the flow cell. Determines read length profile. ONT Ligation Sequencing Kit (SQK-LSK114), Ultra-Long DNA Sequencing Kit (SQK-ULK114)
Flow Cell The consumable containing nanopores for sequencing. R10.4.1 (Rev D) or R10.4.1 MinION Flow Cell (FLO-MIN114)
DNA Extraction Kit High Molecular Weight (HMW) DNA isolation is critical for long reads. Qiagen Genomic-tip 100/G, Nanobind CBB Big DNA Kit
DNA Repair & Damage Kit Mitigates base modifications/nicks that hinder library prep. NEBNext FFPE DNA Repair Mix, ONT's DSB repair step
Size Selection Beads Removes short fragments to enrich for long molecules. Circulomics Short Read Eliminator (SRE) Kit, AMPure XP beads
Basecaller Software Converts raw electrical signal to nucleotide sequence (FASTQ). ONT Dorado (GPU-accelerated), Guppy
Assembly Software De novo assembler optimized for long, noisy reads. Flye (v2.9+), Canu
Polishing Tool Corrects consensus errors in the draft assembly using reads. Medaka, Homopolish
QC & Analysis Tools Assesses read quality, assembly completeness, and accuracy. NanoPlot, QUAST, BUSCO

Within the Context of a Thesis on Flye Assembly Protocol for Oxford Nanopore Data Research

The Flye algorithm (v2.9+) is a de novo assembler specifically designed for long, error-prone reads, such as those from Oxford Nanopore Technologies (ONT). Its core innovation lies in constructing and resolving a repeat graph, which directly represents the assembly as a disjointed directed graph where nodes are genomic sequences and edges represent overlaps. This contrasts with overlap-layout-consensus (OLC) assemblers that build contig paths prematurely. Flye’s error-tolerance is intrinsic to this graph structure, allowing it to manage high indel error rates (typically 5-15% in raw ONT data) without aggressive pre-assembly correction, preserving long-range information critical for spanning repeats.

Key Quantitative Benchmarks (Flye v2.9+ vs. Other Assemblers on ONT Data): Table 1: Comparative Assembly Performance on *E. coli ONT R10.4.1 Data (~50x Coverage)*

Assembler N50 (kbp) # Contigs Assembly Length (Mbp) Run Time (min) Max Alignment Identity (%)
Flye ~3,200 1 4.64 25 99.98
Canu ~2,800 1 4.62 180 99.95
wtdbg2 ~3,100 3 4.65 15 99.90

Data synthesized from recent benchmarking studies (2023-2024).

Core Protocol: Repeat Graph Construction and Resolution

This protocol details the primary stages of the Flye assembly workflow.

Protocol: Initial Disjointig Assembly

Objective: Generate accurate, non-branching genomic segments (disjointigs) from raw reads.

  • Input: ONT reads in FASTQ format (basecalled, preferably with duplex or super-accurate models).
  • Minimum Overlap: Compute all-vs-all read overlaps using a pairwise alignment method. The default minimum overlap length is 5,000 bp, with a minimum alignment identity of 85%.
  • Graph Construction: Build an assembly graph where nodes are reads and edges are significant overlaps.
  • Disjointig Pathfinding: Traverse the graph to find long, non-branching paths. Contradictory edges (from sequencing errors) are iteratively removed based on read coverage and edge multiplicity.
  • Output: A set of disjointigs (FASTA). These are the primary building blocks for the repeat graph.

Protocol: Repeat Graph Construction & Resolution

Objective: Build and simplify the repeat graph to produce final contigs.

  • Graph Building: Compute all pairwise overlaps between disjointigs (as in 2.1). Construct the repeat graph where nodes are disjointigs and edges represent overlaps.
  • Graph Simplification:
    • Tip Removal: Trim short, low-coverage dead-ends (likely artifacts).
    • Bubble Merging: Collapse short alternative paths (bubbles) caused by local misassemblies or haplotype differences.
    • Repeat Resolution: Identify edges where coverage is approximately double (or integer multiple) of the flanking edges, indicating a repeat. These are marked as repetitive.
  • Contig Generation: Traverse the simplified graph. At repetitive nodes, the traversal selects an edge based on supporting read mappings. This process "unrolls" repeats using the long-read information to guide path selection.
  • Polishing (Optional but Recommended): Use the original reads to polish the consensus sequence of contigs (e.g., with Medaka). This step corrects residual base-level errors.
  • Output: Final assembled contigs (FASTA).

Visualizing the Flye Workflow and Repeat Resolution

G cluster_1 1. Read Overlap & Disjointig Assembly cluster_2 2. Repeat Graph Construction & Resolution RawReads Raw ONT Reads (Error-prone) Overlap All-vs-All Pairwise Overlap Detection RawReads->Overlap AssemblyGraph Initial Assembly Graph (Reads as Nodes) Overlap->AssemblyGraph Disjointigs Disjointigs (Non-branching Paths) AssemblyGraph->Disjointigs RepeatGraph Build Repeat Graph (Disjointigs as Nodes) Disjointigs->RepeatGraph SimplifiedGraph Simplified Graph (Tips/Bubbles Removed) RepeatGraph->SimplifiedGraph RepEdge Repetitive Edge (2x Coverage) SimplifiedGraph->RepEdge FinalContigs Final Contigs (Repeats Resolved) RepEdge->FinalContigs Polishing Polish with Original Reads FinalContigs->Polishing FinalAssembly Polished Assembly (FASTA) Polishing->FinalAssembly

Diagram Title: Flye Algorithm's Two-Stage Graph Assembly Workflow (76 chars)

Diagram Title: Flye's Read-Guided Resolution of a Repetitive Edge (69 chars)

The Scientist's Toolkit: Essential Reagents & Materials for Flye Assembly

Table 2: Key Research Reagent Solutions for Flye-based ONT Assembly Projects

Item / Solution Function / Purpose Example / Specification
ONT Sequencing Kit Generates long, native DNA reads. The choice affects read length and quality. Ligation Sequencing Kit (SQK-LSK114) for ultra-long reads; Rapid Kit (SQK-RBK114) for speed.
High-Molecular-Weight DNA Input substrate. Integrity is critical for long-range continuity. DNA with average fragment size >50 kbp, assessed via pulsed-field gel electrophoresis or Femto Pulse.
Basecalling Software Translates raw electrical signals (pod5/fast5) to nucleotide sequences (FASTQ). Critical for accuracy. Dorado (latest version) with super-accuracy (sup) or duplex models.
Flye Algorithm Software Core assembly engine implementing repeat graph construction. Flye v2.9+ installed via Conda (conda install -c bioconda flye).
Polishing Toolkit Corrects residual consensus errors after assembly. Medaka (ont-medaka) or PEPPER-Margin-DeepVariant for haplotype-aware polishing.
Compute Infrastructure Executes memory- and CPU-intensive overlap and graph operations. Server with ≥32 CPU cores, ≥128 GB RAM, and ample SSD storage for large datasets.
Reference Genome Used for optional evaluation of assembly accuracy and completeness. Species-specific reference from NCBI or Ensembl.
Assembly Evaluation Suite Quantifies assembly quality independent of a reference. QUAST (quality metrics), BUSCO (completeness), and Mercury (k-mer accuracy).

Within the broader thesis on the Flye assembly protocol for Oxford Nanopore data research, this application note details its specific advantages in managing the high error rates and complex structural variants inherent in noisy long-read sequencing data. Flye (Fast Long-read de-novo Assembly Engine) employs a repeat graph approach that is intrinsically tolerant to sequencing errors, making it a critical tool for generating accurate, contiguous assemblies from uncorrected reads.

Core Algorithmic Advantages and Quantitative Performance

Flye's performance is characterized by its ability to produce highly contiguous assemblies from raw, high-error-rate reads. The following table summarizes key quantitative benchmarks from recent studies comparing Flye to other long-read assemblers using noisy Oxford Nanopore reads.

Table 1: Assembly Performance on Noisy ONT Reads (Human NA12878)

Assembler Input Read Type Consensus Accuracy (QV) Contig N50 (Mb) Runtime (CPU hours) Max Contig Length (Mb) Structural Variant Recall (%)
Flye (v2.9+) Raw ONT R10.4 ~Q45 ~20-30 ~40-60 ~60 >85
Canu Corrected ONT ~Q40 ~15-25 ~120-180 ~45 ~75
miniasm/minipolish Raw ONT ~Q30 ~10-20 ~15-30 ~35 ~65
Shasta Raw ONT ~Q40 ~15-25 ~10-20 ~50 ~70

Data synthesized from recent benchmarks (2023-2024) using human genome datasets. QV: Quality Value, where Q40 = 99.99% accuracy, Q45 = 99.997% accuracy.

Table 2: Performance on Simulated Complex Structural Variants

Variant Type (Size) Flye Detection Sensitivity False Discovery Rate Required Read Coverage (ONT)
Large Deletion (>1 kb) 92% 5% 20x
Novel Insertion (>500 bp) 88% 7% 25x
Inversion (>5 kb) 85% 10% 30x
Tandem Duplication 90% 8% 25x

Detailed Experimental Protocols

Protocol 1: De Novo Genome Assembly from Raw ONT Reads using Flye

This protocol is designed for generating a complete genome assembly from unfiltered, high-error-rate Oxford Nanopore reads, emphasizing the handling of structural variants.

Materials & Reagents:

  • Oxford Nanopore sequencing library (e.g., SQK-LSK114 kit).
  • High molecular weight genomic DNA (>50 kb).
  • Compute server (≥64 GB RAM, 32 cores recommended).
  • Flye software (v2.9 or later).

Procedure:

  • Basecalling and Read Preparation:
    • Perform basecalling of raw POD5/FAST5 files using Guppy (sup or hac model) or Dorado to generate FASTQ files.
    • Do not perform read correction or trimming based on quality scores. Flye is optimized for raw read length distributions.
    • Assess read length distribution (NanoPlot).

  • Flye Assembly Execution:

    • Run Flye with parameters tailored for noisy reads. The --nano-raw flag is critical.

    • Key Parameters Explained:
      • --nano-raw: Specifies raw, uncorrected ONT reads.
      • --genome-size: Approximate genome size (improves initial partitioning).
      • --iterations: Number of polishing iterations (default is 3; increasing may help with low coverage).

  • Post-Assembly Polishing (Optional but Recommended):

    • For maximum consensus accuracy, polish the assembly using long reads with Medaka.

  • Structural Variant Analysis:

    • Map the original reads back to the polished assembly using minimap2.

    • Call structural variants using Sniffles2 or cuteSV.

Protocol 2: Benchmarking Structural Variant Recovery

This protocol validates Flye's ability to reconstruct known complex structural variants from noisy reads.

Procedure:

  • Spike-in Control Generation:
    • Use a synthetic DNA standard (e.g., Sequins) with known structural variants or spike a control genome (e.g., S. cerevisiae) with engineered rearrangements into the sample.
  • Sequencing and Assembly:
    • Sequence the mixed sample using standard ONT protocols.
    • Run Flye assembly as per Protocol 1.
  • Variant Calling and Comparison:
    • Call SVs from the Flye assembly against the reference genome.
    • Compare to the ground truth variant set using truvari.

Visualization of the Flye Workflow and Error-Tolerant Mechanism

G cluster_key Key Advantage Points RawReads Noisy Long Reads (High Error Rate, SVs) Disjointig 1. Disjointig Construction (Error-tolerant overlap) RawReads->Disjointig Minimap2 overlap RepeatGraph 2. Repeat Graph Assembly (Resolves repeats via reads spanning variants) Disjointig->RepeatGraph Construct graph from conflicting overlaps A A: Raw read overlap ignores high mismatch rates Disjointig->A Consensus 3. Repeat Graph Traversal & Consensus Generation RepeatGraph->Consensus Find unambiguous paths B B: Graph edges represent potential SVs RepeatGraph->B Polishing 4. Iterative Polishing (Aligns reads back to graph) Consensus->Polishing Multiple iterations FinalAssembly Final Assembly (Contiguous, SV-aware) Polishing->FinalAssembly C C: Polishing uses original reads, not corrected data Polishing->C

Title: Flye Assembly Workflow for Noisy Reads and SVs

Title: Graph-Based SV Resolution in Flye

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Flye Assembly with ONT Data

Item Function in Protocol Example Product/Version
ONT Sequencing Kit Prepares genomic DNA for sequencing with motor proteins and adapters. SQK-LSK114 Ligation Kit
Flow Cell The consumable containing nanopores for sequencing. R10.4.1 (FLO-PRO114M)
High-Quality HMW DNA Starting material; integrity is crucial for long read length. Circulomics Nanobind, Qiagen Genomic-tip
Basecaller Software Converts raw electrical signals to nucleotide sequences. Dorado v7.0+, Guppy v6.4+
Flye Assembler Core de novo assembler for noisy long reads. Flye v2.9.3+
Polishing Tool Improves consensus accuracy after assembly. Medaka v1.11+
Variant Caller Identifies structural variants from alignments. Sniffles2 v2.2, cuteSV v2.0+
Benchmarking Suite Evaluates assembly completeness and SV recall. QUAST v5.2, truvari v4.1

Application Notes

Flye (v2.9+), a long-read assembler designed for noisy reads, is a cornerstone tool for de novo assembly of Oxford Nanopore Technologies (ONT) sequencing data across diverse genomic applications. Its repeat graph approach and ability to perform self-correction make it particularly suited for resolving complex genomic regions from long, error-prone reads. The following notes detail its application in key domains, with a focus on ONT data derived from platforms like the PromethION and MinION.

Microbial Genomes: Flye excels at generating complete, circularized bacterial and archaeal genomes from pure culture isolates. Its ability to resolve long repeats, such as ribosomal RNA operons, is critical for producing accurate, single-contig assemblies. This is essential for downstream analyses like antimicrobial resistance (AMR) gene profiling, virulence factor identification, and precise phylogenetics. For hybrid assemblies, Flye can be combined with short-read data (e.g., Illumina) for polishing, achieving Q50+ consensus quality.

Eukaryotic Genomes: For small to mid-sized eukaryotic genomes (e.g., fungi, protists, nematodes), Flye can produce highly contiguous assemblies, often yielding chromosome-scale scaffolds when paired with Hi-C or optical mapping data. It effectively handles moderate levels of heterozygosity and can separate haplotypes. For large, complex plant and animal genomes, while Flye produces the initial assembly, extensive manual curation and integration with complementary data are typically required.

Metagenomes: Flye supports the assembly of individual genomes from complex microbial communities (metagenome-assembled genomes, MAGs) without prior cultivation. Its "meta" mode is optimized for uneven sequencing depth and multiple strains. Recovering complete plasmids and phage sequences from metagenomic data is a significant advantage, providing insights into horizontal gene transfer and community dynamics.

Plasmids: Flye is highly effective at reconstructing complete plasmid sequences, even those with multi-copy or repetitive structures, directly from whole-genome or metagenomic sequencing. This capability is vital for tracking plasmid-borne AMR genes in hospital outbreaks or environmental studies. Flye can often separate plasmid and chromosomal DNA based on coverage and graph topology.

Table 1: Performance Metrics of Flye Across Key Use Cases (Representative ONT Data)

Use Case Typical Input (ONT) N50 / Contig Count Key Metric Common Polishing Approach
Microbial Genome ~50x coverage, R10.4.1 flow cell 1-5 contigs; often single circular Completeness (CheckM >99%) Medaka + Polypolish (with short reads)
Small Eukaryote ~50-100x coverage, ultra-long reads N50 > 1 Mb BUSCO completeness >95% NextPolish (with short reads)
Complex Metagenome ~20-50 Gb from community DNA Varies by population abundance Number of high-quality MAGs Medaka (per contig, if depth sufficient)
Plasmid Recovery ~50x host genome coverage Full-length circular contigs Detection of known plasmid replicons Medaka

Detailed Protocols

Protocol 1:De NovoAssembly of a Bacterial Genome using ONT Data and Flye

Objective: To generate a complete, circularized bacterial genome assembly from a pure culture using ONT long reads.

Research Reagent Solutions & Essential Materials:

Item Function
Nanopore Sequencing Kit (SQK-LSK114) Prepares genomic DNA for ligation sequencing.
Flow Cell (R10.4.1) Pores for sequencing; R10 improves homopolymer accuracy.
NEB Next Ultra II FFPE DNA Repair Mix Repairs damaged DNA ends, improving library yield.
Circulomics Nanobind DNA Extraction Kit Produces high-MW, ultra-pure DNA ideal for long reads.
Flye (v2.9.3) Core long-read assembler.
Medaka (v1.11.1) ONT data-based consensus polisher.
Polypolish (v0.6.0) Incorporates short-read data to polish base-level errors.
CheckM2 Assesses assembly completeness and contamination.

Methodology:

  • DNA Extraction & QC: Extract high molecular weight (HMW) genomic DNA using a method that minimizes shear (e.g., Nanobind kit). Assess quantity (Qubit) and quality (pulse-field gel electrophoresis or FEMTO Pulse).
  • Library Preparation & Sequencing: Prepare an ONT sequencing library using the ligation kit (e.g., LSK114) following the manufacturer's protocol. Load onto a PromethION R10.4.1 flow cell. Target ~50x coverage (e.g., ~200 Mb for a 4 Mb genome).
  • Basecalling & Read QC: Perform high-accuracy basecalling (--barcode_kits "SQK-LSK114") and demultiplexing using dorado (v0.5.0+). Filter reads for length (e.g., >5 kb) and quality (Q-score >10) using NanoFilt.
  • Flye Assembly:

  • Assembly QC: Run CheckM2 to assess completeness and contamination. Visualize the assembly graph (assembly_graph.gv) with Bandage.

  • Consensus Polishing: a. Medaka: Create a consensus model and polish.

    b. Polypolish (if Illumina data available): Map short reads and apply polishing.

  • Circularization & Rotation: Identify circular contigs from Flye output (assembly_info.txt). Rotate the sequence to start at the chromosomal origin of replication (dnaA) using seqkit.

Protocol 2: Recovery of Plasmids and MAGs from Metagenomic Data

Objective: To assemble contigs and recover complete plasmids and MAGs from a complex community sample using ONT reads.

Methodology:

  • Community DNA & Sequencing: Extract total community DNA with minimal bias. Prepare and sequence an ONT library as in Protocol 1, targeting high yield (e.g., 20-50 Gb).
  • Read Processing: Basecall and demultiplex with dorado. Perform light quality and length filtering (e.g., Q>7, length>1kb).
  • Flye Meta Assembly:

  • Binning and MAG Generation: Map all reads back to the assembly using minimap2. Generate a coverage profile. Use a binning tool (e.g., MetaBAT2) on the coverage profile and contigs to group contigs into draft MAGs.

  • Plasmid Identification: Screen all contigs, especially unbinned or high-coverage circular contigs, for plasmid markers using PlasmidFinder and examination of the Flye assembly graph for circular topology.

  • Quality Assessment: Evaluate MAG quality using CheckM2 and report completeness/contamination. Classify plasmids by replicon type and mobility.

G Start HMW Community DNA Extraction Seq ONT Library Prep & Sequencing Start->Seq Basecall Basecalling & Read Filtering Seq->Basecall Assemble Flye Meta Assembly Basecall->Assemble Map Read Mapping & Coverage Profiling Assemble->Map Bin Contig Binning (e.g., MetaBAT2) Map->Bin Identify Plasmid & MAG Identification Bin->Identify QC Quality Assessment (CheckM2, BUSCO) Identify->QC

ONT Metagenomic Assembly & Binning Workflow

G A Raw ONT Reads (Error-prone) B Flye Assembly (Repeat Graph) A->B C Initial Contigs B->C D ONT Consensus Polishing (Medaka) C->D Uses same ONT reads E Short-Read Polishing (Polypolish) D->E Optional, uses Illumina reads F Final High-Quality Assembly E->F

Flye Assembly and Polishing Pipeline

The de novo assembly of long, error-prone Oxford Nanopore Technologies (ONT) reads using the Flye assembler requires careful consideration of input parameters. This application note details the quantitative requirements for read length, sequencing coverage, and read quality (Q-score) to achieve optimal assembly contiguity and accuracy. These guidelines are framed within a broader thesis investigating the optimization of Flye for complex genome and metagenome assembly from ONT data, with direct implications for downstream analyses in biomedical and drug development research.

Quantitative Requirements for Flye Assembly

The performance of Flye is influenced by the interplay of read length, coverage, and quality. The following tables summarize current recommended ranges and their impact on assembly metrics.

Table 1: Recommended Input Parameter Ranges for Flye (ONT Data)

Parameter Minimum Recommended Optimal Range Critical Impact on Assembly
Read Length (N50) 10-20 kbp >30 kbp Defines overlap for repeat resolution and contig continuity.
Sequencing Coverage 30x 50x - 100x Ensures sufficient sampling for consensus accuracy and repeat resolution.
Read Quality (Mean Q-score) Q10 Q12+ Reduces error propagation, improves consensus accuracy and base-level correctness.
Total Raw Bases (Genome Size) x 50 (Genome Size) x 80 Provides the substrate for coverage and read filtering.

Table 2: Expected Assembly Outcomes Based on Input Parameters

Input Profile Expected Contiguity (N50) Expected Base Accuracy (QV) Key Limitations
High Length (>30kbp), High Coverage (60x), Low Quality (Q10) Very High Low ( High consensus errors; requires extensive polishing.
Low Length (<10kbp), High Coverage (60x), High Quality (Q15) Low Moderate (Q25-Q30) Poor repeat resolution; fragmented assembly.
High Length (>30kbp), Moderate Coverage (40x), High Quality (Q15+) Optimal: High Optimal: High (Q30+) Balanced for most research applications.

Detailed Experimental Protocols

Protocol 1: Assessing Input Dataset Suitability for Flye Objective: To evaluate raw ONT sequencing data against the minimum requirements for Flye assembly. Materials: Raw FASTQ files, computing environment with NanoPlot, Flye. Procedure: 1. Quality and Length Assessment: Run NanoPlot --fastq raw_reads.fastq.gz --loglength -o nanoplot_output. Examine the generated report for mean/median read length (N50), total gigabases (Gb), and mean Q-score. 2. Coverage Calculation: Calculate estimated coverage: Coverage = (Total Base Pairs) / (Genome Size in bp). Genome size can be estimated from a related organism or via k-mer analysis of the reads. 3. Dataset Filtering (If Required): If mean Q-score <10, consider quality filtering with chopper or filtlong: filtlong --min_length 1000 --min_mean_q 10 raw_reads.fastq.gz > filtered_reads.fastq. 4. Verification: Re-run NanoPlot on filtered reads to confirm parameters meet minimum thresholds in Table 1.

Protocol 2: Executing a Standard Flye Assembly with Parameter Tuning Objective: To perform a de novo assembly using Flye, iteratively optimizing for input parameters. Materials: Filtered FASTQ files, high-memory compute node (e.g., 128+ GB RAM for mammalian genomes). Procedure: 1. Initial Assembly: Execute Flye with default parameters: flye --nano-hq filtered_reads.fastq --genome-size 5.3m --out-dir flye_output_initial --threads 32. 2. Evaluate Assembly: Check assembly_info.txt in the output directory for contig N50, longest contig, and total assembly size. 3. Iterative Improvement: a. If contiguity is low, subset the longest reads (e.g., top 10-20% by length) to increase effective read N50 and re-assemble. b. If consensus accuracy is poor (per medaka or polypolish summary stats), increase input coverage to >70x or apply more stringent initial quality filtering. 4. Polishing: Run a consensus polishing tool (e.g., medaka): medaka_consensus -i raw_reads.fastq -d assembly.fasta -o medaka_polish -m r1041_e82_400bps_sup_v4.2.0. 5. Validation: Assess final assembly quality with QUAST or BUSCO against a benchmark set of conserved genes.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for ONT Sequencing and Flye Assembly

Item Function in Workflow Example/Note
ONT Ligation Sequencing Kit (SQK-LSK114) Prepares genomic DNA for sequencing by adding motor proteins and adapters. Essential for generating high-molecular-weight reads.
High-Quality, High-MW Genomic DNA Starting material. Integrity is critical for long read length. Use agarose gel electrophoresis or FEMTO Pulse to assess DNA size (>50 kbp ideal).
Flow Cell (R10.4.1 or newer) The consumable containing nanopores for sequencing. R10.4.1 chemistry improves raw read accuracy (Q-score).
Guppy (Basecalling Software) Converts raw electrical signal (fast5) to nucleotide sequence (fastq). Use super-accurate (sup) mode for best Q-score.
CPU/GPU High-Performance Compute Cluster Runs compute-intensive basecalling, assembly, and polishing. GPU acceleration dramatically speeds up basecalling with Guppy.
Flye Assembler Software The long-read assembler that constructs sequences from overlaps. Use --nano-hq flag for ONT data that has been pre-filtered or is high-quality.
Medaka or Polypolish Consensus polishing tools that correct systematic errors in the assembly. Applied after Flye to produce the final, high-accuracy consensus.

Visualized Workflows

G Start High-MW Genomic DNA Node1 ONT Library Prep & Sequencing Start->Node1 Node2 Basecalling (Guppy SUP Mode) Node1->Node2 Node3 FASTQ Files (Raw Reads) Node2->Node3 Node4 Quality Control (NanoPlot) Node3->Node4 Node5 Filtering (Length/Q-score) Node4->Node5 If Q<10 or coverage >100x Node6 Flye De Novo Assembly Node4->Node6 If params met Node5->Node6 Node7 Assembly Polishing (Medaka) Node6->Node7 End Final Assembly Evaluation (QUAST/BUSCO) Node7->End

ONT Sequencing to Flye Assembly Workflow

G Input Input: Read Length, Coverage, Q-score Decision1 Read N50 > 30 kbp? Input->Decision1 Decision2 Coverage 50x-100x? Decision1->Decision2 Yes Action1 Subsample longest reads or resequence Decision1->Action1 No Decision3 Mean Q-score > 12? Decision2->Decision3 Yes Action2 Sequence more or filter to target Decision2->Action2 No Action3 Use SUP basecalling or quality filter Decision3->Action3 No Success Proceed to Flye Assembly Decision3->Success Yes Action1->Decision2 Action2->Decision3 Action3->Success

Decision Logic for Assessing Flye Input Read Suitability

Step-by-Step Flye Protocol: From Basecalls to Contigs

This document serves as a foundational technical chapter for a thesis investigating the optimization of de novo genome assembly for microbial and metagenomic samples using Oxford Nanopore Technologies (ONT) long-read sequencing data. Reliable assembly is a critical first step for downstream analyses in comparative genomics, structural variant detection, and targeted gene discovery for drug development. Establishing a reproducible, version-controlled computational environment and installing core assembly software (Flye) and alignment tools (Minimap2) are essential prerequisites. This protocol details the setup using Conda and BioContainers, which are industry standards for managing bioinformatics software and ensuring consistency across research and development pipelines.

Software Installation & Environment Setup Protocols

Conda Environment Creation and Management

Conda is a package and environment management system that resolves dependencies and allows for isolated software environments, crucial for reproducible research.

Protocol:

  • Install Miniconda: Download the latest Linux 64-bit installer for Miniconda.

  • Create a Dedicated Environment: Create a new Conda environment named nanopore_assembly with a specific Python version.

  • Add BioConda Channels: Configure channels to access bioinformatics software.

Installation of Flye and Minimap2

Flye is a long-read assembler using repeat graphs, and Minimap2 is a versatile aligner for long sequences.

Protocol A: Installation via Conda (Recommended for most users)

This command installs both packages and all their dependencies into the active environment.

Protocol B: Installation via BioContainers (Docker/Singularity for HPC & containerized workflows)

  • Docker:

  • Singularity:

    Replace <tag> with a specific version (e.g., 2.9.4--py310haf5c5bc_1).

Verification of Installation

Confirm successful installation and check versions.

Table 1: Software Version & Resource Requirements (Latest as of Search)

Software Recommended Version Installation Method Approx. Disk Space Key Dependencies
Flye 2.9.4 Conda, BioContainers ~200 MB Python (≥3.7), zlib, gcc runtime
Minimap2 2.26 Conda, BioContainers ~5 MB zlib, klib
Miniconda 23.11.0 Shell script ~1 GB (base) None
Conda Env (nanopore_assembly) N/A Conda create ~1-2 GB Python, specified packages

Table 2: Comparative Advantages of Environment Management Systems

System Primary Use Case Key Advantage for ONT Assembly Research Drawback
Conda/Bioconda Local development, iterative analysis. Easy dependency resolution; mixing Python and binary tools. Potential channel conflicts.
BioContainers (Docker) Reproducible, isolated runtime environments. Full system isolation; "run anywhere" guarantee. Requires root privileges (daemon).
BioContainers (Singularity) High-Performance Computing (HPC) clusters. No root needed on HPC; works with shared filesystems. Slightly more complex build process.

Experimental Protocol: Basic Flye Assembly & Alignment Workflow

This core protocol is cited throughout the broader thesis as the standard assembly method against which optimizations are compared.

Protocol: Initial De Novo Assembly and Read Alignment

  • Input: ONT reads in FASTQ format (reads.fastq), estimated genome size (e.g., 5m for 5 megabases).
  • Step 1: Genome Assembly with Flye

  • Step 2: Read-to-Assembly Alignment with Minimap2

Visualization: Workflow Diagram

G Start Input: ONT FASTQ Reads Conda Conda Environment (nanopore_assembly) Start->Conda FlyeInstall Install Flye & Minimap2 Conda->FlyeInstall Assembly Flye Assembly (--nano-raw, --genome-size) FlyeInstall->Assembly Align Minimap2 Alignment (-ax map-ont) Assembly->Align Output Output: Contigs (FASTA) & Alignments (SAM) Align->Output Thesis Thesis Downstream Analysis: Polishing, QC, Annotation Output->Thesis

Title: Prerequisites to Assembly Analysis Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Computational Materials for ONT Assembly Pipeline

Item Function/Benefit Example/Note
Conda Environment Isolates software dependencies, preventing conflicts between projects. nanopore_assembly environment.
Flye Assembler Constructs accurate assemblies from long, error-prone reads using repeat graphs. Use --nano-hq for Q20+ data.
Minimap2 Aligner Fast pairwise alignment of long reads to references or contigs. map-ont preset is optimized for ONT reads.
High-Performance Compute (HPC) Node Provides sufficient RAM (≥64 GB) and CPU cores for large genomes. Required for vertebrate or plant genomes.
Container Engine (Singularity/Docker) Ensures absolute reproducibility across different computing platforms. Mandatory for clinical or regulated drug development pipelines.
Version Control (Git) Tracks changes to analysis scripts and parameters. Commit messages should record software versions used.
ONT Basecalling & QC Report Provides initial read metrics (length, quality) to guide assembly parameters. Use pycoQC or NanoPlot for assessment.

Within the broader thesis research employing the Flye assembler for de novo genome assembly from Oxford Nanopore Technologies (ONT) long-read data, rigorous data preparation is the critical first step. The raw electrical signal output (FAST5 or POD5) from the sequencer must be converted into nucleotide sequences (FASTQ) through basecalling, followed by comprehensive quality assessment. This protocol details the application of ONT's production-grade basecallers, Guppy and Dorado, and subsequent quality evaluation using NanoPlot, establishing the foundation for a high-quality Flye assembly.

Basecalling: From Signal to Sequence

Guppy (CPU/GPU)

Guppy is a data processing toolkit that performs basecalling, barcode demultiplexing, and adapter trimming. As of late 2023, ONT recommends Dorado for most users, but Guppy remains widely used.

Protocol: Basecalling with Guppy (GPU Example)

  • Input: A directory containing raw FAST5 or POD5 files from an ONT run.
  • Model Selection: Choose the appropriate basecalling model. High-accuracy (HAC) models are recommended for assembly.
    • Check available models: guppy_basecaller --print_workflows
  • Execution Command:

Dorado (GPU Optimized)

Dorado is ONT's next-generation, high-performance basecaller built on the Bonito framework, offering significant speed improvements over Guppy.

Protocol: Basecalling with Dorado (Latest Version)

  • Installation: Install the latest version of Dorado. It is recommended to use the Apptainer/Singularity container or the direct download from the ONT GitHub repository.
  • Download Model: Download the latest suitable basecalling model.

  • Basecalling Execution:

    • First argument: The basecalling model name.
    • --device: cuda:all utilizes all available GPUs.
    • --min-qscore: Filters reads in real-time based on mean Q-score.
    • --emit-fastq: Outputs in FASTQ format.

Table 1: Guppy vs. Dorado Feature Comparison (2024)

Feature Guppy Dorado (Latest)
Primary Platform CPU/GPU GPU-optimized
Speed Standard ~2-3x faster than Guppy
Recommended Use Legacy systems, specific workflows New production workflows
Real-time Filtering Limited Yes (--min-qscore)
Modification Detection Requires separate models Integrated (e.g., 5mC)
Output Formats FASTQ, FASTA, SAM/BAM (with aligner) FASTQ, SAM/BAM (with aligner)
Barcoding/Demux Integrated Integrated

Quality Assessment with NanoPlot

Following basecalling, quality assessment is essential to evaluate read length, quality distribution, and identify potential issues before assembly with Flye. NanoPlot creates a series of visual and statistical summaries.

Protocol: Comprehensive Quality Assessment with NanoPlot

  • Input: A (compressed) FASTQ file from Guppy or Dorado.
  • Basic Quality Summary:

  • Comparative Workflow: If comparing multiple datasets (e.g., different basecallers), use NanoComp:

Table 2: Key Metrics from NanoPlot Output for Assembly QC

Metric Ideal Characteristics for Flye Assembly Interpretation
Mean Read Length (N50) As long as possible, depends on sample. Indicates continuity potential.
Mean Read Quality (Q-score) >Q10 (acceptable), >Q15 (good), >Q20 (excellent). Lower quality may increase assembly errors.
Read Length Distribution A strong peak or smooth distribution. Multiple peaks may indicate contamination.
Quality vs. Length Plot No strong correlation between long reads and low quality. Long, low-quality reads can be problematic.
Total Yield (Gb) Sufficient for intended coverage (e.g., 50x genome coverage). Affects assembly completeness.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for ONT Basecalling & QC Workflow

Item Function Notes
ONT Sequencing Kit (e.g., SQK-LSK114) Prepares genomic DNA for ligation sequencing. Provides sequencing adapters and tether.
Flow Cell (R10.4.1 or newer) The consumable containing nanopores for sequencing. R10.4.1 offers improved accuracy over R9.4.1.
High-Quality, HMW DNA Extraction Kit Isolate long, intact genomic DNA. Critical for obtaining long read lengths.
Guppy/Dorado Basecalling Software Converts raw electrical signal to nucleotide sequence. Dorado is now the recommended production tool.
NanoPlot Package (within NanoPack) Generates quality metrics and plots for long reads. Essential for pre-assembly QC.
GPU (NVIDIA, ≥8GB VRAM) Accelerates basecalling significantly. Required for optimal Dorado performance.
High-Performance Computing Cluster/Workstation Handles data processing and subsequent assembly (Flye). Basecalling and assembly are computationally intensive.

Visualized Workflows

G Start Raw ONT Data (FAST5/POD5) Basecall Basecalling Start->Basecall Guppy Guppy (CPU/GPU) Basecall->Guppy Dorado Dorado (GPU Optimized) Basecall->Dorado FASTQ Basecalled Reads (FASTQ) Guppy->FASTQ Dorado->FASTQ QC Quality Assessment FASTQ->QC NanoPlot NanoPlot QC->NanoPlot Metrics QC Report & Plots NanoPlot->Metrics Decision Data Sufficient for Assembly? Metrics->Decision Decision->Start No Flye Proceed to Flye Assembly Decision->Flye Yes

ONT Data Preparation Workflow

G POD5 POD5 Files Dorado dorado basecaller POD5->Dorado Filter --min-qscore 8 Real-time Filter Dorado->Filter Model Supreme Model (e.g., dna_r10.4.1_e8.2_400bps_sup) Model->Dorado FASTQ Filtered FASTQ Filter->FASTQ NanoPlot NanoPlot --fastq FASTQ->NanoPlot Stats NanoStats.txt (Summary Stats) NanoPlot->Stats Plot1 Quality Histogram NanoPlot->Plot1 Plot2 Length vs Quality Plot NanoPlot->Plot2

Dorado to NanoPlot QC Pipeline

Within the broader thesis investigating optimal genome assembly protocols for Oxford Nanopore Technologies (ONT) long-read sequencing data, the Flye assembler represents a critical component. This de novo assembler, specifically designed for noisy long reads, employs a repeat graph approach to construct accurate and contiguous genomes. This document provides detailed application notes and protocols for executing the core Flye command, framed within a systematic research methodology for genomic analysis in drug development and basic research.

Core Flye Command: Syntax and Essential Parameters

The fundamental command structure for Flye is: flye [options] --nano-raw [input reads] --out-dir [output directory]

The most frequently used parameters for ONT data, based on current best practices, are summarized in the table below.

Table 1: Essential Flye Parameters for Oxford Nanopore Data Assembly

Parameter Argument Type Default Value Recommended Use Case Explanation
--nano-raw input file path None (Required) Standard ONT R9.4+ data, basecalled but not error-corrected. Specifies input as raw, uncorrected Nanopore reads in FASTA/Q format.
--nano-corr input file path None Pre-assembly error-corrected reads (e.g., via Canu). Use if reads have been corrected prior to assembly.
--nano-hq input file path None High-quality Q20+ duplex or super-accurate reads. For premium-quality data, may yield more accurate initial assembly.
--genome-size float (e.g., 5m) None Crucial parameter. Known or estimated genome size (e.g., 4.6m for E. coli). Used for initial read partitioning. Improves assembly speed and accuracy.
--out-dir directory path flye_output All runs. Directory to store all output files (assembly graph, contigs, logs).
--threads integer 1 All multi-core systems. Number of parallel threads to use. Significantly speeds up computation.
--iterations integer 5 Difficult, high-repeat genomes. Number of polishing iterations. Increasing may improve consensus accuracy.
--min-overlap integer Auto-estimated Override for very short or very long read sets. Minimum overlap between reads used for assembly.
--asm-coverage integer Auto-estimated Downsample exceptionally high-coverage data (>100X). Subsets reads to this coverage to reduce memory/time.
--plasmids flag Off Suspected plasmid or extrachromosomal element assembly. Attempts to assemble circular contigs without genome size restriction.
--meta flag Off Metagenomic or multi-genome samples. Enables metagenome mode for uneven sequencing depth.
--scaffold flag Off Produce linked scaffolds where possible. Outputs scaffolds in scaffolds.fasta if breaks can be resolved.

Experimental Protocols

Protocol 1: StandardDe NovoAssembly of a Bacterial Genome from ONT Reads

Objective: Generate a complete, circularized genome assembly from raw Nanopore reads. Materials: ONT sequencing data (FASTQ), high-performance computing node with >= 32GB RAM for bacterial genomes. Procedure:

  • Quality Check: Assess read length (N50) and total coverage using NanoPlot (e.g., NanoPlot --fastq reads.fastq).
  • Command Execution: Run the core Flye assembly.

  • Output Monitoring: Monitor the flye.log file for progress and any error messages.
  • Output Retrieval: The primary assembly consensus will be in flye_assembly/assembly.fasta. The assembly graph is in flye_assembly/assembly_graph.gv.
  • Quality Assessment: Evaluate assembly completeness with QUAST (e.g., quast.py assembly.fasta) and check for circularization in the Flye log.

Protocol 2: Assembly Polishing and Improvement Iteration

Objective: Improve the consensus accuracy of a Flye assembly through iterative polishing. Materials: Initial Flye assembly (assembly.fasta), same set of raw ONT reads. Procedure:

  • Initial Assembly: Complete Protocol 1.
  • Polish with Medaka: Use the ONT-derived polisher Medaka for a consensus step.

(Select the correct Medaka model -m matching your flowcell and basecaller version.)

  • Optional Multiple Rounds: For maximal accuracy, perform further polishing with racon (using raw reads) followed by a final Medaka round.

Protocol 3: Metagenomic Assembly from a Complex Community Sample

Objective: Reconstruct individual genomes from a mixed microbial community sequenced with ONT. Materials: ONT reads from a metagenomic sample, substantial computational resources (high memory). Procedure:

  • Assembly Execution: Run Flye in metagenome mode, omitting --genome-size.

  • Binning: Use coverage and composition information from aligned reads with tools like MetaBAT2 to bin contigs into putative genome bins.
  • Quality Assessment: Assess bin quality and completeness using CheckM or BUSCO.

Visualizations

Diagram 1: Core Flye Assembly Workflow

G Start Sequencing Run ONT R9/R10 Basecall Basecalling Guppy, Dorado Start->Basecall QC Quality Control NanoPlot, FastQC Basecall->QC Decision Read Type? QC->Decision PathRaw Raw Reads Decision->PathRaw Standard PathHQ HQ Duplex/SA Reads Decision->PathHQ Q20+/Duplex FlyeRaw flye --nano-raw PathRaw->FlyeRaw FlyeHQ flye --nano-hq PathHQ->FlyeHQ Assembly Draft Assembly (assembly.fasta) FlyeRaw->Assembly FlyeHQ->Assembly Polish Polish Medaka/Racon Assembly->Polish Final Final Assembly High Accuracy Polish->Final Assess Assessment QUAST, Busco Final->Assess

Diagram 2: Full ONT to Assembly Protocol

The Scientist's Toolkit

Table 2: Essential Research Reagent Solutions & Computational Tools

Item Category Function/Explanation
ONT Sequencing Kit (e.g., Ligation Kit SQK-LSK114) Wet-lab Reagent Prepares genomic DNA libraries for Nanopore sequencing by fragmenting, repairing ends, and ligating adapters.
Flow Cell (R9.4.1, R10.4.1) Hardware/Consumable The solid-state nanopore array where sequencing occurs. Choice impacts read accuracy and yield.
Guppy/Dorado Software Tool ONT's official basecaller. Converts raw electrical signal (squiggle) to nucleotide sequence (FASTQ). Crucial for data quality.
NanoPlot Software Tool Generates quality control plots specifically for long-read Nanopore data (read length distribution, quality scores).
Flye (v2.9+) Software Tool The core de novo assembler discussed here, optimized for long, error-prone reads using repeat graphs.
Medaka Software Tool ONT's neural-network-based consensus polisher. Uses read-to-assembly alignments to correct systematic errors.
Minimap2 Software Tool Ultra-fast and accurate aligner for long reads. Used internally by Flye and for post-assembly read mapping.
QUAST Software Tool Quality Assessment Tool for Genome Assemblies. Reports contiguity (N50), completeness, and misassembly metrics.
CheckM/BUSCO Software Tool Assesses the completeness and contamination of assembled genomes using conserved single-copy gene sets.
High-Memory Compute Node (>= 64GB RAM) Hardware Essential for assembling genomes larger than bacteria or metagenomic samples due to the graph construction step.

Within the broader thesis on de novo assembly of Oxford Nanopore Technologies (ONT) reads using Flye, precise parameter tuning is paramount for generating high-quality, contiguous genomes. This work posits that the interplay between read quality flags (--nano-raw vs. --nano-hq), the user-provided estimate of --genome-size, and computational resource allocation via --threads is the critical determinant of assembly accuracy, completeness, and efficiency. Misconfiguration can lead to fragmented assemblies, chimeric contigs, or excessive resource consumption, undermining downstream analysis in genomics-driven drug discovery.

Parameter Definitions & Current Benchmark Data

The following parameters are central to Flye (v2.9+ as of 2023) assembly performance. Data is synthesized from recent benchmark studies (Kolmogorov et al., 2019; Aury et al., 2022; Shafin et al., 2023) and the Flye documentation.

Table 1: Core Flye Parameters for ONT Data

Parameter Argument Type Default Typical Range Function in Assembly
--nano-raw Read Type Flag Not Set N/A Informs Flye to use untreated, raw ONT reads (basecall accuracy ~92-97%). Activates robust error correction.
--nano-hq Read Type Flag Not Set N/A Informs Flye to use high-quality reads (e.g., Q20+, duplex, super-accurate). Assumes lower error rate, streamlining initial assembly.
--genome-size Integer (bp) None (Required) e.g., 3.2m, 100m, 3.2g Critical initial estimate for repeat resolution and coverage calculation. Significant deviation harms assembly.
--threads Integer 1 1-64+ Parallelizes assembly stages. Scaling is sub-linear; memory use can increase.

Table 2: Quantitative Impact of Parameter Selection (Synthetic Benchmark)

Assembly Condition Estimated Genome Size N50 (kb) Assembly Time (hrs) CPU Core Usage Key Insight
--nano-raw, Accurate --genome-size 4.6 Mb (E. coli) 4,600 1.5 16 Robust assembly, optimal for standard reads.
--nano-hq, Accurate --genome-size 4.6 Mb (E. coli) 4,600 1.0 16 30% faster, similar accuracy with high-quality input.
--nano-raw, 10x Overestimate 46 Mb 120 2.5 16 Severe fragmentation due to low perceived coverage.
--nano-raw, 10x Underestimate 0.46 Mb 380 3.0 16 Increased chimerism and mis-assemblies.
--threads increased from 4 to 32 4.6 Mb 4,600 1.0 → 0.7 32 Diminishing returns on time savings.

Detailed Experimental Protocols

Protocol 3.1: Benchmarking Flye Parameter Sets for a Novel Bacterial Genome

Objective: Empirically determine the optimal --nano-raw/--nano-hq and --genome-size combination for a novel bacterial isolate sequenced with ONT R10.4.1.

Materials: See "The Scientist's Toolkit" below. Input Data: 50x coverage of Pseudomonas sp. (~7.2 Mb genome) ONT reads, basecalled with both standard (dorado fast) and super-accurate (dorado super) models.

Method:

  • Read Set Preparation:
    • Create two directories: raw/ for fast basecalled reads, hq/ for super-accurate reads.
    • Assess quality: NanoPlot --fastq raw/reads.fastq.gz --outdir nanplot_raw.
    • Estimate genome size from close relative using NCBI Genome or flow cytometry data.

  • Parameter Matrix Assembly:

    • For each read set (raw, hq), run Flye with three genome-size estimates: 5.5m (under), 7.2m (accurate), 10m (over).
    • Example command for accurate estimate with HQ reads:

  • Assembly Evaluation:

    • Compute assembly metrics: QUAST -o quast_hq_accurate assembly_hq_accurate/assembly.fasta.
    • Check for circularization of the chromosome/plasmids.
    • Assess consensus accuracy with medaka_consensus or by mapping to a reference if available.

  • Analysis:

    • Plot N50 vs. genome-size estimate for both read types.
    • The condition yielding the highest N50, completeness (via BUSCO), and lowest number of contigs is optimal.

Protocol 3.2: Profiling Computational Resource Scaling with--threads

Objective: Characterize the time-memory trade-off across a range of --threads values.

Method:

  • Baseline Profile: Run Flye with --threads 4 on a fixed dataset (e.g., E. coli --nano-raw). Monitor using /usr/bin/time -v and record "User time," "Elapsed (wall clock) time," and "Maximum resident set size."
  • Scaling Experiment: Repeat assembly with --threads = 8, 16, 32, 64, keeping all other parameters constant.
  • Data Processing: Calculate speedup = (Time4 / TimeN). Plot Speedup and Memory vs. Thread count.
  • Interpretation: Identify the point of diminishing returns for your specific compute infrastructure.

Visualization of Parameter Decision Logic

flye_decision start Start: ONT Read Set qc Perform QC: Read Length N50, Mean Q Score start->qc decision_q Mean Q Score >= 20? qc->decision_q param_raw Use --nano-raw (Robust mode) decision_q->param_raw No param_hq Use --nano-hq (Fast mode) decision_q->param_hq Yes decision_gs Genome Size Known? gs_known Set --genome-size to known value (bp) decision_gs->gs_known Yes gs_estimate Estimate via: - k-mer analysis (Meryl) - Close relative decision_gs->gs_estimate No param_raw->decision_gs param_hq->decision_gs thread_tune Set --threads: 4-16 for testing, 32-64 for production gs_known->thread_tune gs_estimate->thread_tune run_flye Execute Flye Assembly thread_tune->run_flye evaluate Evaluate Assembly: N50, BUSCO, QUAST run_flye->evaluate

Diagram Title: Flye Parameter Tuning Decision Workflow

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 3: Key Reagent Solutions for Flye Assembly Workflows

Item Function/Application Example Product/Software
High-Molecular-Weight DNA Isolation Kit To extract intact, long genomic DNA for ONT sequencing. Qiagen Genomic-tip 100/G, Nanobind CBB Big DNA Kit
ONT Sequencing Kit & Flow Cell Generate long-read data. SQK-LSK114 Ligation Kit, R10.4.1 flow cell
Basecalling Software Convert raw electrical signals to nucleotide sequences. Dorado (ONT), Guppy (ONT)
Computational Environment Hardware/Software for assembly. Linux server (>=32 GB RAM, >=16 cores), Miniconda
Read QC & Filtering Tool Assess and pre-process reads before assembly. NanoPlot, NanoFilt, Filthong
Genome Size Estimation Tool Provide accurate --genome-size input. Meryl (k-mer counting), Flow Cytometry
Assembly Evaluation Suite Quantify assembly quality post-Flye. QUAST, BUSCO, Mercury
Consensus Polishing Tool Improve final assembly accuracy. Medaka, BWA-MEM + Racon

Within a comprehensive research thesis employing the Flye assembler for Oxford Nanopore Technologies (ONT) long-read sequencing data, achieving a contiguous assembly is only the first step. The intrinsic higher error rate of raw ONT reads (historically ~5-15%, now improved with latest chemistry to ~1-4%) necessitates post-assembly polishing. This critical step corrects small indels and mismatches in the draft assembly consensus sequence. This document provides detailed Application Notes and Protocols for two prominent polishing tools, Medaka (ONT's official polisher) and NextPolish (a versatile, multi-algorithm polisher), to improve consensus accuracy for downstream analyses such as gene annotation, variant calling, and comparative genomics in drug target discovery.

The choice between Medaka and NextPolish depends on project goals, data type, and computational resources. The following table summarizes their key characteristics.

Table 1: Core Feature Comparison of Medaka and NextPolish

Feature Medaka NextPolish
Primary Developer Oxford Nanopore Technologies Hu et al.
Core Algorithm Convolutional neural network (CNN) trained on specific basecaller/chemistry. Modular pipeline utilizing multiple aligners (minimap2, BWA) and consensus callers.
Input Read Type Native ONT raw reads (FASTQ). Can use ONT reads, PacBio reads, or high-accuracy short reads (Illumina).
Typical Use Case Single-round, fast polishing of ONT-only assemblies. Best with matched model. Multi-round, flexible polishing. Can perform hybrid (long+short) or long-read-only polishing.
Speed Very fast (leverages pre-trained models). Slower, especially with multiple rounds and short-read integration.
Ease of Use Simple one-line command after model selection. Requires more parameter configuration and iterative control.
Accuracy Outcome Excellent at correcting remaining ONT systematic errors when model matches data. Can achieve very high final accuracy, especially when using hybrid data.
Key Requirement Correct medaka model matching flowcell, basecaller, and pore version. For hybrid: high-quality short-read library from the same sample.

Table 2: Quantitative Performance Comparison (Representative Data)

Based on recent benchmarking studies using *E. coli genome with R10.4.1 flowcell, Supers basecalling, and Flye v2.9 assembly.*

Polishing Strategy Consensus Accuracy (QV) INDEL Error Rate (per 100kbp) SNP Error Rate (per 100kbp) Runtime (CPU-hours)
Flye Assembly (Unpolished) ~Q30 (99.9%) 50-150 20-50 N/A
Medaka (single round) ~Q35-40 (99.97-99.99%) 10-30 5-15 0.5
NextPolish (long-read, 2 rounds) ~Q35-38 (99.97-99.98%) 15-40 8-20 3.0
NextPolish (hybrid, 2 rounds) ~Q45+ (99.997+%) <5 <2 5.0

Detailed Experimental Protocols

Prerequisites and Data Preparation

Research Reagent Solutions & Essential Materials

Table 3: The Scientist's Toolkit for Polishing Experiments

Item Function/Explanation
Flye-assembled genome (FASTA) The draft consensus sequence to be polished.
Raw ONT reads (FASTQ) The same read set used for assembly, for self-polishing.
High-quality Illumina paired-end reads (FASTQ) Optional. For hybrid polishing with NextPolish to achieve maximum accuracy.
Medaka software (v1.11+) ONT's neural network-based polisher. Install via conda: conda install -c bioconda medaka.
NextPolish software (v1.4+) Versatile polisher. Install via conda: conda install -c bioconda nextpolish.
Minimap2 (v2.24+) Required for read alignment in both pipelines.
SAMtools (v1.17+) For processing alignment (SAM/BAM) files.
Compute Environment Linux server with sufficient memory (16GB+). Medaka can use GPU for acceleration.

Data Organization:

Protocol A: Polishing with Medaka

Objective: To rapidly improve the accuracy of a Flye assembly using the same ONT reads and a chemistry-specific Medaka model.

Step-by-Step Methodology:

  • Identify Correct Medaka Model: Determine the medaka model name matching your sequencing configuration. Use medaka tools list_models to view available models. The model name incorporates basecaller (e.g., sup for SUPERVISION), pore (e.g., r1041 for R10.4.1), and version (e.g., e82). Example: r1041_e82_400bps_sup_v4.2.0.

  • Execute Medaka Polishing: Run the core medaka_consensus command. It performs alignment and consensus calling in one step.

    • -i: Input ONT reads.
    • -d: Draft assembly FASTA.
    • -o: Output directory.
    • -m: Medaka model name.
    • -t: Number of threads.

  • Output: The primary polished consensus is medaka_polished/consensus.fasta. The original assembly is split into 10kbp chunks, polished, and then merged.

Workflow Diagram: Medaka Polishing Pipeline

medaka_workflow cluster_note Key Process ONT_Reads Raw ONT Reads (FASTQ) Medaka_Tool medaka_consensus ONT_Reads->Medaka_Tool Flye_Assembly Flye Draft Assembly (FASTA) Flye_Assembly->Medaka_Tool Medaka_Model Chemistry-Specific Medaka Model Medaka_Model->Medaka_Tool Polished_Assembly Polished Consensus (FASTA) Medaka_Tool->Polished_Assembly Align 1. Align reads to assembly (minimap2) Medaka_Tool->Align Call 2. Call CNN-corrected consensus Call->Polished_Assembly

Protocol B: Polishing with NextPolish

Objective: To polish a Flye assembly using NextPolish, optionally incorporating Illumina reads for hybrid correction to achieve maximum accuracy.

Step-by-Step Methodology:

  • Setup Configuration File: NextPolish is driven by a run.cfg file. Create one for your project. Below are examples for long-read-only and hybrid polishing.

  • Example A: Long-read-only (2 rounds) run.cfg:

    Create the lgs.fofn file listing the path to your ONT reads:

  • Example B: Hybrid polishing (long reads then short reads) run.cfg:

    Create the sgs.fofn file:

  • Execute NextPolish: Run NextPolish with the configuration file.

    The process runs iteratively as defined in the task parameter.

  • Output: The final polished genome is workdir/genome.nextpolish.fasta. Intermediate files for each round are retained.

Workflow Diagram: NextPolish Hybrid Polishing Logic

nextpolish_logic cluster_cycle1 Cycle 1 cluster_cycle2 Cycle 2 (Repeat) Start Input: genome.fa (Flye Assembly) LR_Align1 Align ONT Reads Start->LR_Align1 LR_Consensus1 Call Consensus LR_Align1->LR_Consensus1 SR_Align1 Align Illumina Reads LR_Consensus1->SR_Align1 SR_Consensus1 Call Consensus SR_Align1->SR_Consensus1 LR_Align2 Align ONT Reads SR_Consensus1->LR_Align2 LR_Consensus2 Call Consensus LR_Align2->LR_Consensus2 SR_Align2 Align Illumina Reads LR_Consensus2->SR_Align2 SR_Consensus2 Call Consensus SR_Align2->SR_Consensus2 End Output: genome.nextpolish.fasta (High-Quality Assembly) SR_Consensus2->End

Validation and Downstream Integration

After polishing, validate the assembly quality using:

  • QUAST: For assembly statistics (N50, length) and reference-based evaluation if a reference genome is available.
  • Merqury/FastK: For k-mer based consensus quality (QV) estimation, which does not require a reference.
  • BUSCO: For assessing gene completeness.

The polished, high-accuracy assembly is now suitable for definitive downstream applications in the Flye-ONT thesis pipeline, such as structural variant analysis, precise antimicrobial resistance gene detection, and comprehensive genome annotation for novel therapeutic target identification.

This document serves as a detailed application note within a broader thesis investigating the optimization of genome assembly for bacterial pathogens using Oxford Nanopore Technologies (ONT) long-read data. The Flye assembler is a critical tool in this pipeline, chosen for its ability to generate accurate and contiguous assemblies from noisy long reads. A precise understanding of its primary output files—the assembly graph, contigs, and log files—is essential for evaluating assembly quality, diagnosing issues, and interpreting biological conclusions relevant to antimicrobial resistance research and drug development.

File Descriptions & Quantitative Data

Flye generates several output files in its result directory ({output_dir}). The core files are summarized below.

Table 1: Core Output Files from Flye Assembly

File Name Format Primary Content Role in Analysis
assembly.fasta FASTA Final contig sequences. Primary consensus sequences for downstream annotation, variant calling, and comparative genomics.
assembly_graph.gfa GFA (Graphical Fragment Assembly) format, typically version 1. Assembly graph in GFA format. Represents the assembly's topology, showing connections, overlaps, and potential repeats. Crucial for manual evaluation and scaffolding.
assembly_info.txt Tab-separated values (TSV). Metrics per contig. Provides per-contig statistics essential for quality filtering and curation.
flye.log Text log. Step-by-step runtime log. Critical for debugging, performance monitoring, and recording software parameters.

Table 2: Key Quantitative Metrics in assembly_info.txt

Column Header Description Typical Range/Value
contig_id Unique identifier for the contig. e.g., contig_1
length Length of the contig in base pairs. Varies by genome size.
coverage Mean read coverage depth for the contig. ~50-100x for typical bacterial ONT runs.
circular Indicates if the contig is assembled as circular. Yes/No; plasmids/chromosomes may be Yes.
repeat Marks contigs identified as repetitive. * if part of a repetitive region.
mult Multiplicity of the contig in the graph. Integer; >1 for repeats.
alt_group Identifier for alternative alleles/haplotypes. Used in heterozygous/polyploid assemblies.

Experimental Protocol: Assembly and Output Analysis

This protocol details the steps for running Flye and systematically analyzing its output files.

Software and Environment

  • Compute Environment: Linux server (Ubuntu 20.04 LTS or similar) with minimum 32 GB RAM for bacterial genomes.
  • Flye: Install via conda: conda install -c bioconda flye. Version used: 2.9 or later.
  • Auxiliary Tools: Bandage (for graph visualization), seqkit, awk.

Step-by-Step Protocol

Part A: Execute Flye Assembly

  • Quality Check Input Reads: Use NanoPlot --fastq {input.fastq} --outdir nanoplot_results to assess read length (N50) and quality.
  • Run Flye Assembly:

Parameters: --nano-raw for uncorrected ONT reads; --genome-size is an estimate (e.g., 5m for 5 Mbp). Use --nano-hq for Guppy SUP reads.

  • Monitor Execution: The process will log progress to {output_dir}/flye.log.

Part B: Analyze Output Files

  • Contig Evaluation:
    • Inspect assembly.fasta with seqkit stats assembly.fasta.
    • Filter assembly_info.txt for circular contigs: awk '$4 == "Yes" {print}' assembly_info.txt. These are candidate chromosomes/plasmids.
    • Plot contig length vs. coverage using data from assembly_info.txt (e.g., with R or Python pandas/matplotlib).
  • Graph Inspection:
    • Visualize the assembly graph using Bandage: Bandage load assembly_graph.gfa.
    • Identify complex bubbles (potential haplotypes), long dead ends (potential misassemblies or low-coverage regions), and circular structures.
  • Log File Audit:
    • Search for warnings or errors in flye.log: grep -i "error\|warn" flye.log.
    • Extract key statistics: total reads used, achieved coverage, and time per stage.

Visualization of the Analysis Workflow

Flye Assembly & Output Analysis Pipeline

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials & Tools for Flye Assembly Analysis

Item Function/Description Example/Supplier
ONT Library Prep Kit Prepares genomic DNA for sequencing on Nanopore devices. SQK-LSK114 (Oxford Nanopore)
High Molecular Weight DNA Input material; integrity is critical for long-read assembly. Extracted via CTAB/Phenol-Chloroform or commercial kits (e.g., Nanobind CBB).
Flye Software The long-read assembler that generates the primary outputs. https://github.com/fenderglass/Flye
Bandage GUI tool for visualizing and analyzing assembly graphs. https://rrwick.github.io/Bandage/
SeqKit Efficient command-line toolkit for FASTA/Q file manipulation. https://bioinf.shenwei.me/seqkit/
Python/R with plotting libs For custom scripting and visualization of metrics (length, coverage). pandas, matplotlib, ggplot2
Compute Infrastructure Server/Cluster with sufficient RAM and CPU cores for assembly. Minimum 32 GB RAM for bacterial genomes.

Solving Common Flye Assembly Problems and Performance Tuning

Within the broader thesis on optimizing the Flye assembly protocol for Oxford Nanopore sequencing data in genomic research, the ability to diagnose failed assemblies is critical. Flye log files and error messages contain diagnostic information essential for troubleshooting. This application note provides a structured guide to interpreting these outputs, enabling researchers to rectify issues and achieve successful de novo genome assemblies.

Key Log File Components and Quantitative Benchmarks

Flye log files (flye.log) provide real-time statistics on assembly progression. The following table summarizes key metrics and their indicative ranges for a successful assembly.

Table 1: Critical Flye Log Metrics and Benchmarks

Metric Typical Successful Range/Value Interpretation of Deviation
Reads Processed ~100% of input reads Significant shortfall indicates I/O or read format issues.
Mean Read Length Dataset-specific (e.g., >10 kb) Very low mean length may suggest poor sequencing run.
Total Bases Matches input FASTA/Q summary Discrepancy suggests truncated input.
K-mer Size Selection Auto-selected based on read N50 Manual override may be needed for low-coverage data.
Disjointig Count Decreases sharply after assembly stage High final count suggests unresolved repeats/low coverage.
Contig N50 (final) Increases through repeat, contigger stages Stagnation indicates assembly collapse or fragmentation.
Graph Connections Reported during repeat stage Zero connections indicate severe assembly failure.

Common Error Messages and Diagnostic Protocols

This section details frequent Flye error messages, their root causes, and step-by-step diagnostic protocols.

Error 1: "Not enough reads for reasonable coverage"

  • Root Cause: Insufficient genomic coverage or incorrect input specification.
  • Diagnostic Protocol:
    • Calculate coverage: (Total base pairs in reads) / (Estimated genome size).
    • Verify minimum coverage: For bacterial genomes, aim for >50x; for complex eukaryotes, >30x is a starting point.
    • Ensure reads are in correct format (FASTA or FASTQ, uncompressed or .gz).
    • Check the command line: The --nano-raw flag is for uncorrected reads; --nano-hq is for Q20+.

Error 2: "Disjointig graph is degenerate" or "Zero connections in repeat graph"

  • Root Cause: Highly fragmented assembly graph due to excessive sequencing errors, chimeric reads, or ultra-low coverage.
  • Diagnostic Protocol:
    • Assess Read Quality: Compute mean Q-score with pycoQC or NanoPlot. Mean Q < 9 often leads to issues.
    • Filter Reads: Remove short/low-quality reads using Filtlong (e.g., --min_length 1000 --keep_percent 90).
    • Check for Contamination: Align a subset of reads to a reference (if available) using minimap2; inspect coverage uniformity.
    • Increase Coverage: Sequence more material if coverage is below 20x.

Error 3: Assembly Stalls at "Assembly" or "Repeat" Stage

  • Root Cause: Computational resource exhaustion (typically memory).
  • Diagnostic Protocol:
    • Monitor Memory: Use top or htop during the run. Flye can require >100 GB RAM for large (>100 Mbp) genomes.
    • Check flye.log: Look for lines indicating [stage-NAME] followed by a long pause without progress.
    • Mitigation: Restart Flye with the --resume flag and increased resources. Consider using the --asm-coverage (e.g., --asm-coverage 30) to subsample very high coverage data.

Error 4: "Assertion failed" or Segmentation Fault

  • Root Cause: Software bug, corrupted input file, or system incompatibility.
  • Diagnostic Protocol:
    • Validate Input File: Ensure the input FASTA/Q is not corrupted. Try seqtk seq input.fastq > validate.fastq.
    • Check Flye Version: Ensure you are using a stable release, not a development branch.
    • Reproduce with Subset: Run Flye on a small subset (e.g., 1000 reads) to see if the error persists.
    • Report Issue: If the problem continues, prepare a minimal dataset and report on the Flye GitHub repository.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Toolkit for Flye Assembly Diagnostics

Item / Reagent Function in Diagnosis & Recovery
Flye (v2.9+) Core long-read assembler. Always use the latest stable version for bug fixes.
NanoPlot / pycoQC Generates quality control plots (read length, Q-score distribution) to assess input data.
Filtlong Filters Nanopore reads by length and quality to create an optimal subset for assembly.
Minimap2 Rapid alignment tool to map reads to preliminary contigs or a reference for contamination checks.
Bandage Visualizes assembly graphs to identify fragmentation, collapsed repeats, or tangles.
Seqtk Lightweight toolkit for FASTA/Q file validation, subsampling, and format conversion.
Compute Environment High-memory server (e.g., >128 GB RAM for mammalian genomes) or cluster access.

Visualization of Diagnostic Workflows

flye_diagnostic_workflow Start Flye Assembly Fails LogCheck Inspect flye.log & Terminal Output Start->LogCheck ErrorCat Categorize Primary Error LogCheck->ErrorCat CovError 'Not enough reads' Low Coverage? ErrorCat->CovError GraphError 'Degenerate graph' Zero connections? ErrorCat->GraphError StallError Assembly Stalls (Hangs/Killed)? ErrorCat->StallError CrashError Assertion Fail or Crash? ErrorCat->CrashError Action1 Protocol 1: Calculate Coverage Verify Input Format CovError->Action1 Action2 Protocol 2: Check Read Quality Filter with Filtlong GraphError->Action2 Action3 Protocol 3: Monitor Memory Use --resume --asm-coverage StallError->Action3 Action4 Protocol 4: Validate Input File Update/Reinstall Flye CrashError->Action4 Success Diagnosis Complete Implement Solution Action1->Success Action2->Success Action3->Success Action4->Success

Flye Assembly Failure Diagnostic Decision Tree

flye_data_flow RawReads Raw Nanopore Reads (FASTQ) QC Quality Control (NanoPlot/pycoQC) RawReads->QC Filter Read Filtering (Filtlong) QC->Filter If Q<10 or many short reads FlyeInput Curated Read Set QC->FlyeInput If QC passes Filter->FlyeInput Assembly Flye Assembly Core FlyeInput->Assembly LogFile flye.log Assembly->LogFile Output Assembly Output (contigs.fasta, graph.gv) Assembly->Output Metrics Key Metrics Table (Table 1) LogFile->Metrics Diagnosis Error Diagnosis (Apply Protocols) Metrics->Diagnosis Deviation from benchmarks Output->Diagnosis If contigs.fasta is small/empty

Flye Assembly and Log File Generation Data Flow

Within the broader thesis research on optimizing the Flye assembly protocol for Oxford Nanopore Technologies (ONT) long-read data, addressing low assembly contiguity is a critical challenge. The N50 statistic and the total number of contigs are primary metrics for assessing assembly quality; a higher N50 and fewer contigs indicate a more complete and contiguous reconstruction of the genome. This application note details targeted strategies and protocols to diagnose and remediate causes of fragmented assemblies in the Flye-ONT workflow.

Common Causes of Low Contiguity and Diagnostic Checks

Before optimization, key failure points must be identified. The following table summarizes primary causes, diagnostic indicators, and initial validation steps.

Table 1: Diagnostic Framework for Low-Contiguity Assemblies

Cause Category Specific Issue Diagnostic Indicator Validation Protocol
Input Read Quality Insufficient read length or yield Mean read length < 20 kb; Total yield < 50x coverage for complex genomes. Protocol 1.1: Run NanoPlot --fastq <raw.fastq> to plot read length and yield distributions. Calculate coverage: (Total bp in reads) / (Estimated genome size).
Input Read Quality High error rate or adapter contamination Read N50 << Fragment length distribution from library prep. Many short reads. Protocol 1.2: Run pycoQC or NanoPlot to assess raw read quality (Q-score). Use Porechop or Chopper to remove adapters and filter by length/q-score.
Assembly Parameters Inappropriate --genome-size setting Flye log shows premature termination or unusual repeat graph construction. Protocol 1.3: Re-run Flye with estimated genome size (±0.5 Mbp). Use known close relative or kmer-count (e.g., Meryl) for estimation.
Genomic Complexity High repeat content or heterozygosity Assembly graph (assembly_graph.gv) shows many bubbles and tangled connections. Protocol 1.4: Visualize the assembly graph using Bandage. High frequency of branches indicates unresolved repeats or alleles.
Basecalling Mode High accuracy (HAC) vs. Super Accuracy (SUP) SUP basecalling often improves assembly contiguity but increases compute time. Protocol 1.5: Perform comparative assembly: Assemble subsets of reads basecalled with HAC (dna_r10.4.1_e8.2_400bps_hac) and SUP (dna_r10.4.1_e8.2_400bps_sup) models.

Optimization Protocols

The following protocols outline step-by-step strategies to improve contiguity.

Protocol 2.1: Comprehensive Read Preprocessing for Flye Objective: Generate a curated, high-quality read set optimized for Flye's assembler.

  • Basecalling: Use Guppy (guppy_basecaller) or Dorado with the latest Super Accuracy (SUP) model (e.g., dna_r10.4.1_e8.2_400bps_sup).
  • Adapter Trimming & Filtration: Run chopper (from the Oxford Nanopore tools suite):

  • (Optional) Read Correction: For highly complex genomes, consider a light correction step using NextDenovo in read correction mode or using Canu (correct mode, with -correctedErrorRate=0.045) to reduce noise. Weigh the benefit against potential chimera creation.

Protocol 2.2: Iterative Flye Assembly with Polishing Objective: Leverage Flye's repeat graph and iterative polishing to resolve misassemblies and improve consensus.

  • Initial Assembly:

  • First-Round Polishing: Map raw reads back to the assembly using minimap2 and polish with medaka.

  • Second Assembly Iteration: Use the polished assembly as trusted contigs to guide a new assembly.

    Rationale: The polished contigs from Round 1 provide a more accurate sequence to resolve repeat boundaries in Round 2.

Protocol 2.3: Hybrid Scaffolding for Eukaryotic Genomes Objective: Use complementary short-read (Illumina) or Hi-C data to scaffold a Flye assembly, dramatically increasing N50.

  • Inputs: Flye assembly (assembly.fasta), paired-end Illumina reads (R1.fastq, R2.fastq).
  • Scaffold with LINKS:

  • Alternatively, use Hi-C data with SALSA2 or YaHS:

The Scientist's Toolkit: Key Research Reagent Solutions

Table 2: Essential Materials and Tools for Contiguity Improvement

Item Function & Rationale
ONT Super Accuracy (SUP) Basecalling Model Highest accuracy basecalling (Q20+). Critical for reducing indel errors that fragment assemblies in repetitive regions.
Chopper / Porechop Adapter trimming and read filtering. Ensures only full-length, adapter-free reads enter assembly, reducing false connections.
Medaka ONT-tailored consensus polisher. Uses neural networks to correct systematic errors in the draft assembly, essential for resolving homopolymers.
Bandage Visualizes assembly graphs. Allows diagnosis of tangled repeats, misassemblies, and potential collapse points.
LINKS or YaHS Scaffolding tools. Integrate long-range linkage (from mate-pair, Hi-C, or linked reads) to order, orient, and merge contigs.
Benchmarking Universal Single-Copy Orthologs (BUSCO) Assembly completeness assessment. Identifies missing/fragmented genes, confirming if contiguity improvements translate to biological completeness.

Visualizations

G Start Raw ONT Fast5 Data Basecall Basecalling (SUP Model) Start->Basecall Filter Read Filtering & Trimming (Chopper) Basecall->Filter Assemble De Novo Assembly (Flye) Filter->Assemble Polish Polish (Medaka) Assemble->Polish Evaluate Evaluate Assembly (N50, BUSCO) Polish->Evaluate Decision Contiguity Acceptable? Evaluate->Decision Scaffold Hybrid Scaffolding (LINKS/YaHS) Decision->Scaffold No End Final Assembly Decision->End Yes Scaffold->Polish Re-polish

Title: Flye Assembly Optimization Workflow

Title: Causes and Effects of Low Assembly Contiguity

This document provides application notes and protocols for managing computational memory during de novo genome assembly of large eukaryotic genomes using the Flye assembler with Oxford Nanopore Technologies (ONT) long-read data. Within the broader thesis research, efficient memory utilization is critical for processing datasets spanning several hundred gigabases, such as those from human, wheat, or salamander genomes. The following sections detail current optimization strategies, benchmarked protocols, and reagent solutions to enable successful large-scale assemblies on institutional high-performance computing (HPC) clusters.

Quantitative Data on Memory Usage and Optimization Impact

Recent benchmarks (2024-2025) highlight the memory footprint of Flye across different genomes and the efficacy of optimization strategies.

Table 1: Flye Memory Usage for Selected Eukaryotic Genomes (ONT Data)

Genome (Approx. Size) Read N50 (bp) Coverage Default Flye Peak RAM (GB) Optimized Peak RAM (GB) Key Optimization Applied
Homo sapiens (3.1 Gb) 25,000 50x 850 520 --genome-size 3.1g, --asm-coverage 40, Reduced --iterations
Triticum aestivum (15 Gb) 20,000 40x 3,200 (Failed) 1,850 --meta, --min-overlap scaled, Partitioned reads
Ambystoma mexicanum (32 Gb) 30,000 60x Exceeded 4TB 2,100 --read-selection heuristic, Two-pass assembly
Drosophila melanogaster (180 Mb) 35,000 100x 45 45 Minimal benefit for small genomes

Table 2: Effect of ONT Read Quality Improvement Tools on Flye Memory

Pre-Assembly Processing Tool CPU Time Increase Memory Overhead Resultant Flye RAM Reduction Recommended for >10Gb genomes?
Filternlong (NanoFilt) Low Low 5-10% Yes, for low-complexity genomes
Canu read correction Very High Very High 15-25% No, prohibitive resource cost
NECAT error correction High High 10-20% Selective use for critical datasets

Detailed Experimental Protocols

Protocol 3.1: Two-Pass Assembly for Ultra-Large Genomes (>10 Gb)

This protocol reduces peak memory by performing an initial assembly on a subset of reads to generate a "guide" scaffold.

Materials:

  • HPC cluster with SLURM/PBS job scheduler.
  • Flye (version 2.9.3 or later).
  • SeqKit or samtools view for read sampling.

Method:

  • Read Subsampling: Extract ~20x coverage of the longest reads.

  • First Pass Assembly: Run Flye on the subset with a target genome size.

  • Second Pass Assembly: Use the first-pass assembly as --trusted-contigs for the full dataset.

  • Validation: Compare contiguity (N50) and completeness (BUSCO) between passes.

Protocol 3.2: Memory-Optimized Flye Execution for Mammalian Genomes

A standard protocol for human or mouse-sized genomes aiming to keep RAM under 512 GB.

Method:

  • Resource Allocation: Request a node with 64 CPUs and 500 GB RAM.
  • Flye Command with Critical Parameters:

  • Monitor Memory: Use flye --profile flag or external tools like htop/snakemake to track peak usage in real-time.

Visualization of Optimization Strategies

G ONT_Data Raw ONT Reads Preprocess Read Selection & Subsampling ONT_Data->Preprocess Flye_Core Flye Assembly Core (Repeat Graph Construction) Preprocess->Flye_Core Reduces Input Complexity Params Optimized Parameters: --genome-size --asm-coverage --min-overlap Params->Flye_Core Limits Graph Search Space Output Final Assembly (FASTA) Flye_Core->Output

Diagram Title: Flye Memory Optimization Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Computational & Data Reagents for Large Genome Assembly

Item Function & Relevance Example/Note
ONT Ligation Kit SQK-LSK114 Produces ultra-long reads (N50 >50 kb). Critical for spanning complex repeats, reducing graph complexity. Latest chemistry improves read accuracy, indirectly aiding assembly.
High-Molecular-Weight DNA Isolation Kit Extracts intact DNA molecules >150 kb. Fundamental input quality determinant. e.g., Nanobind CBB Big DNA Kit.
Flye Assembler (v2.9+) De novo assembler based on repeat graphs, optimized for noisy long reads. Key tool for protocol. Requires Python 3.6+.
Compute Node with Large RAM Physical hardware for assembly. Memory is the primary limiting resource. 512 GB - 2 TB RAM, 64+ CPU cores recommended.
SLURM Job Scheduler Manages resource allocation on HPC clusters, enables multi-day jobs. Essential for protocol execution.
SeqKit / Biopython For rapid FASTA/Q manipulation, subsampling, and format conversion. Pre-processing and data assessment.
BUSCO (v5) Assesses assembly completeness against conserved single-copy orthologs. Primary quality metric. Uses lineage-specific datasets (e.g., eukaryota_odb10).

Long-read assemblers like Flye are essential for constructing complete genomes from Oxford Nanopore Technologies (ONT) data. However, genomic regions with repeats, structural variations, or uneven coverage can lead to misassemblies and chimeric contigs. These errors manifest as incorrect joins (misassemblies) or fusions of disparate genomic segments (chimeras), compromising downstream analysis in genome finishing, variant discovery, and comparative genomics. This document provides application notes and protocols for identifying and correcting these artifacts within the context of a Flye-based assembly pipeline.

Identification of Misassemblies and Chimeras

Table 1: Quantitative Metrics for Misassembly Identification

Tool/Metric Data Input Key Output Typical Threshold/Indicator
Assembly QA: QUAST Assembly contigs, Reference genome # misassemblies, # relocations, # translocations Misassembly count >0 indicates issues.
Read Mapping: Minimap2 Assembly contigs, Raw ONT reads PAF/BAM file for coverage/alignment analysis Sudden coverage drops, read orientation flips.
Consensus QA: Mercury Assembly contigs, Raw ONT reads QV (Quality Value), k-mer completeness QV < 40 suggests potential misassemblies.
Structural Check: Inspector Assembly contigs, Raw ONT reads Misassembly breakpoint coordinates Identifies precise locations of errors.

Protocol 2.1: Rapid Diagnostic with QUAST and Read Mapping

Objective: Identify large-scale misassemblies and coverage anomalies.

  • Run QUAST for Reference-Based Evaluation:

    Inspect the report.txt for misassembly counts and locations (icarus.html viewer).

  • Map Reads to Assembly for Coverage Analysis:

  • Visualize Coverage & Alignment:

    Import mapped.bam into IGV. Look for contigs with sharp, sustained drops in read depth to zero (potential breaks) or regions where read pairs map inconsistently.

Correction Approaches

Table 2: Comparison of Correction Tools & Strategies

Approach Primary Tool Input Requirements Advantage Limitation
Targeted Cutting & Rejoining Inspector Assembly, aligned BAM file Precise breakpoint detection; produces corrected FASTA. Requires manual review of suggested cuts.
Local Reassembly Medaka (polyploidy mode) Assembly, raw reads, BAM Polishes and can resolve small haplotypic bubbles. Not for large structural errors.
Iterative Refinement Flye --iterative Raw reads, initial assembly Flye-native; uses read graph for correction. Computationally intensive.
Hybrid Scaffolding/Breaking RagTag Assembly, reference genome Can break misjoints and scaffold correctly. Reference-dependent.

Protocol 3.1: Correction Using Inspector

Objective: Precisely identify and cut at misassembly junctions.

  • Run Inspector for De Novo Misassembly Detection:

  • Analyze the Output: Examine misassembly_breakpoints.txt. Each line suggests a potential cut site (contig, position, support).

  • Execute the Correction:

    The corrected assembly is in inspector_corrected/corrected_assembly.fasta.

Protocol 3.2: Iterative Refinement with Flye

Objective: Use Flye's own algorithm to reconcile the assembly with the read graph.

  • Perform Iterative Assembly Polishing:

    This command rebuilds the assembly graph from the initial contigs and raw reads, often resolving repeat-related misjoins.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Misassembly Resolution Workflow

Item / Reagent Function / Purpose Example/Note
High-Molecular-Weight (HMW) DNA Starting material for ONT sequencing. Essential for long-range continuity. QIAGEN Genomic-tip, Monarch HMW DNA Extraction Kit.
ONT Ligation Sequencing Kit (SQK-LSK114) Prepares DNA libraries for sequencing, preserving read length. Latest chemistry balances yield and length.
Computational Server (High RAM) Runs assembly and correction tools (Flye, Inspector). ≥ 64 GB RAM for bacterial genomes; ≥ 512 GB for mammalian.
Reference Genome (if available) Provides anchor for QUAST/RagTag for evaluation and scaffolding. NCBI GenBank, ENSEMBL.
Visualization Software (IGV) Critical for manual validation of breakpoints and coverage. Integrates BAM, VCF, and assembly files.

Visualization of Workflows

G Start Raw ONT Reads A1 Flye De Novo Assembly Start->A1 A2 Initial Assembly (contigs.fasta) A1->A2 B1 Read Mapping (minimap2/samtools) A2->B1 B2 Aligned BAM File B1->B2 C Misassembly Identification B2->C C1 Coverage Analysis & QUAST C->C1 C2 Breakpoint Detection (Inspector) C->C2 D Correction Decision C1->D C2->D E1 Targeted Cut (Inspector --apply) D->E1 E2 Iterative Refinement (Flye --iterative) D->E2 End Validated Assembly E1->End E2->End

Workflow for Identifying and Correcting Assembly Errors

G Chimera Chimeric Contig SubA Segment A (Genomic Locus 1) Chimera->SubA SubB Segment B (Genomic Locus 2) Chimera->SubB CovDrop Sharp Coverage Drop & Read Discontinuity SubA->CovDrop Junction SubB->CovDrop Reads ONT Reads MapA Map to A Reads->MapA MapB Map to B Reads->MapB MapA->SubA MapB->SubB Break Identified Breakpoint CovDrop->Break Cut Cut & Trim Break->Cut Resolved Resolved Contigs Cut->Resolved

How a Chimera is Detected and Resolved

Within the broader thesis on the Flye assembler for Oxford Nanopore Technologies (ONT) long-read data, this Application Note details advanced strategies for complex metagenomic datasets. We focus on the implementation and rationale of Flye's --meta and emergent --meta-meta flags, and contextualize them within co-assembly workflows. These approaches are critical for researchers and drug development professionals seeking to reconstruct complete genomes from uncultured microbial communities, enabling the discovery of novel biosynthetic gene clusters and resistance markers.

Flye is a de novo assembler designed for long, error-prone reads, making it ideal for ONT data. For single-isolate genomes, it uses a repeat graph approach. Metagenomic samples, however, contain multiple genomes with varying abundances, making assembly challenging due to interspecies repeats and uneven coverage. The standard --meta flag modifies the algorithm for this heterogeneity. The --meta-meta flag represents a further optimization for highly complex communities, often applied to large-scale co-assemblies of multiple samples.

Core Algorithmic Flags:--metavs--meta-meta

Flye's metagenomic modes adjust key parameters to handle uneven coverage and contamination.

Table 1: Comparison of Flye Assembly Modes for Metagenomics

Parameter Default Mode --meta Flag --meta-meta Flag (Emergent Practice)
Primary Use Case Single isolate, high coverage Single metagenomic sample Highly complex communities; co-assembly of multiple samples
Coverage Assumption Uniform Uneven (polymorphic) Extremely uneven & fragmented
Repeat Resolution Relies on uniform coverage Disabled for low-frequency edges Aggressively disabled; prioritizes contiguity of abundant sequences
Minimum Overlap Default setting Reduced (--min-overlap adjusted) Often further reduced
Flye Version All 2.9+ All 2.9+ Recommended in 2.9+ for extreme complexity
Expected Outcome Complete circular chromosomes Improved strain separation, more contigs Maximized assembly size (N50), potentially higher misassembly rate

Quantitative Data Summary: Benchmarks on ZymoBIOMICS Even/Odd mock communities show --meta improves unique completion by 15-25% over default. --meta-meta applied to a 50-sample co-assembly increased the total assembled bases by ~3x compared to individual --meta assemblies, but BUSCO duplication rate rose from 1.5% to 4.2%.

Protocol: End-to-End Co-assembly Workflow with Flye

This protocol is designed for assembling multi-sample ONT metagenomic datasets.

Materials:

  • Input: ONT sequencing data (FASTQ) from multiple metagenomic samples, basecalled with Guppy or Dorado, preferably demultiplexed.
  • Computing: High-memory server (≥1 TB RAM for large co-assemblies), Linux environment.
  • Software: Flye (≥2.9), minimap2, samtools, metaWRAP (optional for binning).

Procedure:

Step 1: Read Quality Control and Normalization

  • Concatenate all reads from samples intended for co-assembly: cat sample1.fastq sample2.fastq > all_reads.fastq.
  • (Optional but recommended) Use filtlong to retain high-quality reads: filtlong --min_length 1000 --keep_percent 95 --target_bases 5000000000 all_reads.fastq > co_reads.filt.fastq. This controls dataset size and error rate.

Step 2: Co-assembly with Flye --meta-meta

  • Run Flye in --meta-meta mode, specifying the large genome size:

    Note: The --meta-meta flag is used in conjunction with --meta. The --genome-size is an approximate total size of all genomes in the community.

Step 3: Read Mapping and Coverage Calculation

  • Map all reads from each individual sample back to the co-assembly:

  • Repeat for each sample. This per-sample coverage information is critical for downstream binning.

Step 4: Binning and Refinement

  • Use a coverage-aware binning tool like metaWRAP:

  • Perform bin refinement: metaWRAP bin_refinement -o bin_refinement -A metabat2_bins -B maxbin2_bins -C concoct_bins -c 50 -x 10.

Step 5: Quality Assessment

  • Check bin quality with CheckM2 or BUSCO:

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for ONT Metagenomic Co-assembly

Item Function in Workflow Example/Specification
ONT Ligation Sequencing Kit (SQK-LSK114) Library preparation for long-read genomic DNA sequencing. Ensures high molecular weight DNA input, critical for metagenome assembly continuity.
ZymoBIOMICS Microbial Community Standard Mock community for validating assembly and binning performance. Contains known genomes at staggered abundances to benchmark --meta mode accuracy.
Mag-Bind TotalPure NGS Beads Size selection and clean-up post-library prep. Retains long fragments (>10 kb), directly improving Flye's ability to resolve repeats.
Qubit dsDNA HS Assay Kit Accurate quantification of low-concentration metagenomic DNA. Essential for determining optimal input mass for sequencing, affecting coverage evenness.
ProNex Size-Selective Purification System Gel-free size selection of high molecular weight gDNA. Improves read length (N50) prior to sequencing, a key determinant of assembly contiguity.

Visualization of Workflows and Logical Relationships

G cluster_decision Flye Mode Decision Start Multiple ONT Metagenomic Samples QC Read QC & Normalization Start->QC Concat Read Concatenation (Co-assembly Input) QC->Concat FlyeMeta Flye Assembly (--meta --meta-meta) Concat->FlyeMeta Map Per-Sample Read Mapping FlyeMeta->Map Bin Coverage-Aware Binning Map->Bin Assess Bin Quality Assessment Bin->Assess End Metagenome-Assembled Genomes (MAGs) Assess->End D1 Single Sample? Moderate Complexity? D2 Many Samples? High Complexity? D1->D2 No D3 Use Flye --meta D1->D3 Yes D4 Use Flye --meta --meta-meta D2->D4 Yes

Title: Co-assembly workflow and Flye mode decision logic

G cluster_key Graphical Key cluster_default Default Flye Mode (Assumes Uniform Coverage) cluster_meta Flye --meta-meta Mode (Polymorphic Coverage) k1    Consensus Sequence    Repeat Region    Low-Coverage/Contaminant Edge D1 A D2 R1 D1->D2 50x D3 B D2->D3 50x D4 R1 D2->D4 Resolved D3->D4 50x D5 C D4->D5 50x M1 A' M2 R1 M1->M2 80x M3 B' M2->M3 80x M4 R1 M3->M4 5x M6 X M3->M6 75x M5 C' M4->M5 5x M6->M4 75x

Title: Flye graph behavior in default vs. meta-meta mode

Within the broader thesis investigating the optimization and application of the Flye assembler for Oxford Nanopore Technologies (ONT) long-read sequencing data, rigorous benchmarking is paramount. This protocol details the application of AssemblyQC metrics and computational resource tracking to evaluate Flye assemblies. The goal is to provide a standardized framework for assessing assembly continuity, accuracy, and efficiency, enabling informed decisions for downstream analyses in genomics research and drug target discovery.

Key Benchmarking Metrics (AssemblyQC)

AssemblyQC is a suite of metrics for evaluating genome assemblies. The following table summarizes the core quantitative metrics used to benchmark a Flye assembly against a reference genome.

Table 1: Core AssemblyQC Metrics for Benchmarking Flye Assemblies

Metric Category Specific Metric Description Optimal Value (General)
Contiguity Total Assembly Length Total sum of all contig/scaffold lengths. Close to expected genome size.
Number of Contigs Total number of contiguous sequences. Lower is better (more contiguous).
N50 / L50 N50: contig length such that 50% of the assembly is in contigs of this size or longer. L50: the number of contigs at N50. Higher N50, lower L50 is better.
NG50 / LG50 Similar to N50/L50 but calculated relative to the reference genome size. Higher NG50, lower LG50 is better.
Completeness Genome Fraction (%) Percentage of reference genome bases covered by the assembly. Higher is better (closer to 100%).
BUSCO Score (%) Percentage of universal single-copy orthologs found complete in the assembly. Higher is better.
Accuracy Misassemblies Number of large-scale structural errors (relocations, translocations, inversions). Lower is better (0 is ideal).
Indel/ Mismatch Rate (per 100kb) Number of small-scale base errors (insertions, deletions, mismatches). Lower is better.
QV (Quality Value) Phred-scaled consensus accuracy: QV = -10*log10(error rate). Higher is better (e.g., QV40 = 99.99% accurate).

Experimental Protocol: Benchmarking a Flye Assembly

Protocol Title: Integrated Workflow for Benchmarking Flye Assemblies with AssemblyQC and Resource Profiling.

Objective: To generate, assess, and benchmark a de novo genome assembly from ONT data using the Flye assembler, quantifying both output quality and computational resource consumption.

Materials & Software:

  • Input Data: ONT long-read genomic DNA sequencing data (FASTQ format).
  • Reference Genome: High-quality reference genome for the target species (FASTA format).
  • Software: Flye (v2.9+), QUAST (v5.2.0+), BUSCO (v5.4+), time command or /usr/bin/time -v, compute cluster or high-performance workstation.
  • System: Unix/Linux environment with sufficient memory and storage.

Detailed Methodology:

Step 1: Data Preprocessing (Optional but Recommended).

  • Activity: Filter reads by length and quality using filtlong or NanoFilt.
  • Command Example: NanoFilt -l 1000 -q 10 input.fastq > filtered_reads.fastq
  • Purpose: Remove very short and low-quality reads to improve assembly efficiency and quality.

Step 2: Genome Assembly with Flye.

  • Activity: Execute Flye assembly while initiating resource monitoring.
  • Command Example: /usr/bin/time -v -o flye_resource_usage.txt flye --nano-hq filtered_reads.fastq --genome-size 5m --out-dir flye_output --threads 32
  • Protocol Detail: The --nano-hq flag is used for high-quality ONT Q20+ kits. The --genome-size parameter guides the assembler. Resource usage (time, CPU, memory) is logged via /usr/bin/time -v.

Step 3: Assembly Quality Assessment with QUAST.

  • Activity: Compute AssemblyQC metrics using QUAST, with and without a reference genome.
  • Command Example (with reference): quast.py flye_output/assembly.fasta -r reference_genome.fasta -o quast_results_ref --threads 16
  • Command Example (without reference): quast.py flye_output/assembly.fasta -o quast_results_no_ref --threads 16
  • Protocol Detail: This generates comprehensive reports (report.txt, report.pdf) detailing contiguity statistics, misassemblies, and genome fraction.

Step 4: Biological Completeness Assessment with BUSCO.

  • Activity: Assess gene space completeness using a lineage-specific BUSCO dataset.
  • Command Example: busco -i flye_output/assembly.fasta -l bacteria_odb10 -o busco_results -m genome --cpu 16
  • Protocol Detail: BUSCO reports the percentage of complete, fragmented, and missing conserved genes.

Step 5: Computational Resource Analysis.

  • Activity: Parse the output from /usr/bin/time -v to extract key resource metrics.
  • Extract Data: Compile the following into a table from the flye_resource_usage.txt file: Elapsed (wall-clock) time, Maximum Resident Set Size (peak memory), Percent of CPU usage, and System/User CPU times.

Step 6: Data Integration and Reporting.

  • Activity: Summarize all quantitative results into a final benchmarking report table.

Table 2: Integrated Benchmarking Results for a Flye Assembly

Aspect Metric Result Benchmark Threshold
Contiguity No. of Contigs [Value] < 100 for bacterial genome
N50 (bp) [Value] > 50% of expected chromosome size
Completeness Genome Fraction (%) [Value] > 95%
BUSCO Complete (%) [Value] > 95%
Accuracy QV [Value] > 40
Misassemblies (count) [Value] Minimize, ideally 0
Resources Wall-clock Time (hrs) [Value] Project-dependent
Peak Memory (GB) [Value] Project-dependent
CPU Utilization (%) [Value] Project-dependent

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Tools for Flye Assembly Benchmarking

Item Function/Description Example/Note
ONT Sequencing Kit Generates the long-read input data. Chemistry defines raw read accuracy. Ligation Sequencing Kit (SQK-LSK114), Ultra-long Sequencing Kit.
Genomic DNA Source High molecular weight (HMW), purified genomic DNA. Isolated using protocols that minimize shearing (e.g., MagAttract HMW DNA Kit).
Reference Genome Gold-standard sequence for accuracy and completeness assessment. Downloaded from NCBI RefSeq database.
QUAST Software Primary tool for calculating AssemblyQC metrics (contiguity, misassemblies). Use the --nanopore flag for ONT-specific error models.
BUSCO Lineage Dataset Set of conserved orthologs used as benchmarks for biological completeness. Selected based on target organism (e.g., bacteria_odb10, eukaryota_odb10).
Compute Infrastructure Hardware for running computationally intensive assembly and analysis. High-core-count CPUs, >64 GB RAM, and fast NVMe storage are recommended.
Resource Profiler (time) System utility to measure CPU time, memory, and I/O of the assembly process. The -v (verbose) flag in /usr/bin/time is critical for detailed metrics.

Visualization of the Benchmarking Workflow

G cluster_input Input Phase cluster_process Processing & Analysis Phase cluster_output Output & Decision Phase ONT_Data ONT Raw Reads (FASTQ) Preprocess 1. Preprocess Reads (Filter by length/quality) ONT_Data->Preprocess Reference Reference Genome (Optional, FASTA) QUAST 3. QUAST Analysis (Contiguity, Accuracy) Reference->QUAST Flye 2. Flye Assembly (De novo graph construction) Preprocess->Flye Flye->QUAST assembly.fasta BUSCO 4. BUSCO Analysis (Gene Completeness) Flye->BUSCO assembly.fasta Profile Resource Profiling (time, memory) Flye->Profile monitors Metrics Integrated Metrics Table (Table 2) QUAST->Metrics BUSCO->Metrics Profile->Metrics Decision Benchmark Evaluation (Pass/Fail/Iterate) Metrics->Decision

Diagram Title: Flye Assembly Benchmarking Workflow

Visualization of Metric Relationships and Goals

G Goal Optimal Assembly Contiguity High Contiguity (High N50, Low Contig #) Contiguity->Goal Completeness High Completeness (High Genome Fraction, BUSCO) Completeness->Goal Accuracy High Accuracy (High QV, Low Misassemblies) Accuracy->Goal Efficiency Computational Efficiency (Reasonable Time & Memory) Efficiency->Goal constraint Input Quality of Input Reads Input->Contiguity Input->Accuracy Params Flye Parameters (genome-size, iterations) Params->Contiguity Params->Efficiency Hardware Available Compute Resources Hardware->Efficiency

Diagram Title: Goals and Factors in Assembly Benchmarking

Assessing Assembly Quality and Comparing Flye to Canu, Raven, and Shasta

Within the broader thesis research employing the Flye assembler for Oxford Nanopore Technologies (ONT) sequencing data, the rigorous assessment of assembly quality is paramount. This document outlines the critical metrics—Completeness, Contiguity, and Accuracy—detailing their application, interpretation, and the experimental protocols for their calculation in the context of de novo genome assembly for downstream applications in biomedical and drug discovery research.

Core Quality Metrics: Definitions and Interpretation

Completeness: BUSCO Analysis

Benchmarking Universal Single-Copy Orthologs (BUSCO) assesses the completeness of a genome assembly based on evolutionarily informed expectations of gene content.

  • Principle: BUSCO evaluates the presence and copy number of a set of universal single-copy orthologs from a specified lineage (e.g., bacteria_odb10, eukaryota_odb10).
  • Output: Results are categorized as: Complete (single-copy and duplicated), Fragmented, and Missing.
  • Target: A high-quality assembly aims for >95% complete BUSCOs, with the vast majority being single-copy.

Table 1: Example BUSCO Results for a Bacterial Genome Assembly

BUSCO Category Count Percentage Interpretation
Complete (C) 138 98.6% Ideal target met
Complete single-copy (S) 137 97.9% Excellent, indicates low duplication
Complete duplicated (D) 1 0.7% Minimal duplication is acceptable
Fragmented (F) 1 0.7% Low fragmentation is good
Missing (M) 1 0.7% Minimal missing content
Total BUSCO groups searched 140 100% Lineage: bacteria_odb10

Contiguity: N50/L50 Statistics

Contiguity metrics describe the assembly's fragmentation level. N50 is the most commonly reported.

  • Principle: N50 is the length of the shortest contig/scaffold at which 50% of the total assembly size is contained in contigs/scaffolds of that length or longer. L50 is the count of such contigs.
  • Interpretation: A higher N50 and a lower L50 indicate a more contiguous assembly. For circular bacterial genomes assembled with Flye, the ideal is a single contig (L50=1) with an N50 equal to the genome size.

Table 2: Contiguity Metrics for Theoretical Assemblies

Assembly Total Size (Mb) # Contigs N50 (kb) L50 Longest Contig (kb) Assessment
Assembly A (Flye) 5.2 12 1,050 2 2,800 Good contiguity
Assembly B 5.1 85 145 11 420 Fragmented

Accuracy: Consensus Quality (QV) and Identity

Accuracy measures the per-base correctness of the consensus sequence.

  • Quality Value (QV): A logarithmic score where QV = -10 * log10(Error Rate). A QV of 30 implies 1 error per 1,000 bases (99.9% accuracy), QV 40 implies 1 error per 10,000 bases (99.99% accuracy).
  • Read-to-Assembly Mapping Identity: The average percent identity of original reads aligned back to the consensus assembly.
  • Target: For ONT data polished with high-fidelity tools, a QV > 40 is often achievable and desirable for variant-sensitive applications.

Table 3: Accuracy Metrics Pre- and Post-Polishing

Assembly Stage Consensus QV Estimated Error Rate Read-to-Assembly Identity Recommended for
Flye Draft Assembly ~25-30 1/316 to 1/1000 ~97-98% Structural analysis
After Medaka Polishing ~35-45 1/3162 to 1/31,623 ~99-99.9% Gene annotation, SNP calling

Application Notes & Protocols

Protocol 1: Generating and Evaluating a Flye Assembly with ONT Data

Objective: Produce a de novo assembly from ONT reads and calculate core quality metrics.

Materials & Input Data:

  • ONT sequencing reads in FASTQ format (basecalled, >=Q10 recommended).
  • Sufficient compute resources (Flye is memory-intensive; ~1 GB RAM per 1 Mbp of genome size).
  • Installed software: Flye, BUSCO, minimap2, QUAST.

Procedure:

  • Assembly:

  • Assess Contiguity & Basic Stats (using QUAST):

    Output: Report (report.txt) containing N50, L50, total length, # contigs.

  • Assess Completeness (using BUSCO):

    Output: Summary in busco_result/short_summary.txt.

  • Assess Accuracy via Consensus QV: a. Map reads to assembly:

    b. Calculate QV using merqury or yak:

    Output: QV value in merqury_output/qv.

Protocol 2: Polishing for Improved Accuracy (QV)

Objective: Improve consensus accuracy of a Flye draft assembly using the same ONT reads.

Procedure:

  • Perform Medaka Polishing (requires basecall model):

  • Re-evaluate all metrics (Completeness, Contiguity, Accuracy) on the polished assembly using steps in Protocol 1.

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Materials and Tools for Assembly & QC

Item Function/Description Example/Note
ONT Ligation Sequencing Kit (SQK-LSK114) Prepares genomic DNA for sequencing on Nanopore platforms. Standard for whole-genome sequencing.
Flye (v2.9+) De novo assembler for long, error-prone reads. Uses repeat graphs. Optimal for ONT data; --nano-hq mode for Q20+ reads.
BUSCO (v5+) Assesses completeness using conserved single-copy orthologs. Select appropriate lineage database.
Medaka Neural network-based tool to polish assemblies using ONT signals. Requires matching basecall model name.
Minimap2 Fast all-vs-all aligner for long reads to reference/assembly. Used for read mapping in QC and polishing.
QUAST Quality Assessment Tool for Genome Assemblies. Calculates N50, L50, misassemblies.
Merqury / Yak K-mer based evaluation for consensus quality (QV) and assembly spectrum. Requires high-quality Illumina data or original reads.

Workflow Diagrams

G cluster_0 Input Phase cluster_1 Core Flye Assembly cluster_2 Polish & Refine cluster_3 Quality Control & Metrics A High Molecular Weight DNA B ONT Sequencing (MinION/PromethION) A->B C Basecalled Reads (FASTQ) B->C D Flye Assembler --nano-hq C->D E Draft Assembly (FASTA) D->E F Medaka Polishing E->F G Polished Assembly (FASTA) F->G H QUAST (Contiguity: N50) G->H I BUSCO (Completeness) G->I J Merqury / Mapping (Accuracy: QV) G->J K Final QC Report H->K I->K J->K

Title: Flye Assembly & QC Workflow for ONT Data

G Start Polished Genome Assembly M1 Metric 1: Completeness 'Did we capture everything?' Start->M1 M2 Metric 2: Contiguity 'Is it in large pieces?' Start->M2 M3 Metric 3: Accuracy 'Is the sequence correct?' Start->M3 P1 Protocol: BUSCO Analysis M1->P1 P2 Protocol: QUAST N50/L50 M2->P2 P3 Protocol: QV via Merqury M3->P3 O1 Output: % Complete, Fragmented, Missing P1->O1 O2 Output: N50 (bp), L50 count P2->O2 O3 Output: QV score, Error Rate P3->O3 D1 Decision for Thesis: Proceed if >95% Complete O1->D1 D2 Decision for Thesis: Proceed if N50 > target O2->D2 D3 Decision for Thesis: Proceed if QV > 40 O3->D3 End Assembly PASS/FAIL for Downstream Analysis D1->End D2->End D3->End

Title: Three-Pillar Assembly QC Decision Logic

Application Notes: Overview of Long-Read Assemblers In the context of advancing the thesis on the Flye assembly protocol for Oxford Nanopore data, a comparative analysis of speed and simplicity against other long-read assemblers is essential. Raven and Miniasm represent contrasting approaches within the long-read assembly landscape.

Quantitative Comparison Table: Key Metrics Table 1: Comparative Performance Metrics (Based on Published Benchmarks)

Metric Flye (v2.9+) Raven (v1.8+) Miniasm (v0.3+)
Assembly Algorithm Repeat graph (consensus via partial order alignment) Overlap-layout-consensus (OLC) with RAV Overlap-layout (no consensus step)
Typical Speed (CPU hours, Human data) ~40-60 ~10-20 ~2-5
Peak RAM Usage (Human data) Moderate-High (~150 GB) Low-Moderate (~80 GB) Very Low (~20 GB)
Requires Error Correction No (self-correction during assembly) Yes (requires RAV or external polisher) Yes (requires external polishing)
Contiguity (N50) High Moderate-High Moderate (depends on input)
Accuracy (pre-polishing) High Moderate Low (consensus step omitted)
Ease of Use / Simplicity High (single command) High (single command) High (minimalist design)

Detailed Experimental Protocols

Protocol 1: Genome Assembly with Flye Objective: Assemble an Oxford Nanopore reads dataset into a complete genome using Flye's repeat-graph algorithm.

  • Data Input: Gather ONT reads in FASTA or FASTQ format. Basecalling is assumed to be complete.
  • Quality Control: Optional but recommended. Use NanoPlot or pycoQC to assess read length distribution and quality.
  • Assembly Command: Execute Flye with a single command:

    • --nano-raw: Specifies uncorrected Nanopore reads.
    • --genome-size: Estimated genome size (crucial for parameter tuning).
    • --out-dir: Directory for all output files.
    • --threads: Number of parallel threads.
  • Output: The primary assembly is assembly.fasta in the output directory. Flye internally performs repeat resolution and consensus generation.
  • Post-Assembly: Polishing with Medaka is recommended for final consensus accuracy: medaka_consensus -i reads.fastq -d assembly.fasta -o medaka_output -t 32.

Protocol 2: Genome Assembly with Raven Objective: Assemble ONT reads using Raven's OLC-based pipeline which includes read-overlap, RAV consensus, and layout.

  • Data Input: Same as Protocol 1.
  • Assembly Command: Execute Raven in a single step:

    • Raven automatically performs overlapping, consensus (via RAV), and layout.
  • Post-Assembly: Raven's output typically requires polishing. Use Medaka as in Step 5 of Protocol 1.

Protocol 3: Genome Assembly with Miniasm + Minipolish Objective: Achieve a rapid draft assembly using Miniasm's overlap-layout approach, followed by external consensus polishing.

  • Data Input: Same as Protocol 1.
  • Overlap Computation: Use minimap2 to find all-vs-all read overlaps:

  • Layout Assembly: Run Miniasm to construct the assembly graph and generate unitigs:

  • Extract Sequence: Convert the Graph FASTA (GFA) format to FASTA:

  • Consensus Polishing: Apply minipolish (a wrapper for Racon) to generate a consensus sequence:

Visualizations

Title: Workflow Comparison: Flye vs. Raven & Miniasm

assembler_decision_tree start Goal: Assemble ONT Reads opt_speed Primary constraint: Speed & Low RAM? start->opt_speed opt_complete Need single-command, polish-ready assembly? opt_speed->opt_complete No choice_miniasm Choose Miniasm+ MiniPolish opt_speed->choice_miniasm Yes opt_draft Accept draft needing separate polishing? opt_complete->opt_draft No choice_flye Choose Flye opt_complete->choice_flye Yes choice_raven Choose Raven opt_draft->choice_raven Yes opt_draft->choice_flye No

Title: Assembler Selection Logic for Speed & Simplicity

The Scientist's Toolkit: Research Reagent Solutions Table 2: Essential Materials for Comparative Assembly Analysis

Item / Solution Function in Protocol
Oxford Nanopore Flow Cell (e.g., R10.4.1) Generates the raw electrical signal data for basecalling into nucleotide reads.
High Molecular Weight (HMW) DNA Isolation Kit Extracts long, intact genomic DNA, which is critical for generating long reads that span repeats.
Library Preparation Kit (e.g., Ligation Sequencing Kit) Prepares DNA with motor proteins and adapters for loading onto the Nanopore flow cell.
Computational Node (High RAM, >128 GB) Essential for running memory-intensive assemblers like Flye on large genomes (e.g., mammalian).
Basecaller Software (e.g., Dorado) Converts raw signal (*.pod5) to nucleotide sequences (*.fastq).
Quality Assessment Tool (e.g., NanoPlot) Provides read length (N50) and quality (Q-score) metrics to assess input data suitability.
Consensus Polishing Tool (e.g., Medaka) Uses neural networks to correct systematic errors in draft assemblies; required for Raven/Miniasm outputs.
Assembly Evaluation Suite (e.g., QUAST) Computes quantitative metrics (N50, misassemblies, completeness) to compare final assembly quality.

Within the broader thesis on optimizing the Flye assembly protocol for Oxford Nanopore data research, a critical task is understanding how assembly algorithms are specialized for different long-read sequencing technologies. This analysis directly compares Flye (designed for noisy, continuous long reads) and Shasta (optimized for high-fidelity long reads) to elucidate their handling of distinct read types and inform protocol adaptations for Nanopore data.

Flye employs a repeat graph approach, iteratively extending contigs via disjointig assembly. It is designed to leverage the ultra-long length of Oxford Nanopore reads, using an overlap-based assembly strategy that is tolerant of higher error rates (~5-15%). Its iterative error correction and consensus building are crucial for noisy data.

Shasta is an overlap-based assembler specifically optimized for PacBio HiFi reads, which are long (>10 kbp) but have very high single-read accuracy (>99.9%). It uses a run-length encoding representation to efficiently compute alignments, assuming high fidelity. It is not designed for the raw error profile of standard Nanopore reads.

Comparative Summary Table: Core Algorithmic Characteristics

Feature Flye Shasta
Primary Read Target Noisy Long Reads (ONT, CLR) High-Fidelity Long Reads (PacBio HiFi)
Assembly Paradigm Repeat Graph & Disjointig Assembly Overlap-Layout-Consensus (OLC)
Error Tolerance High; integrates polishing Very Low; relies on input read accuracy
Key Strength Handles high repeat content with long reads Speed and efficiency with accurate reads
Typical Input ONT R9.4.1, R10.4; PacBio CLR PacBio HiFi (CCS) reads
Best Use Case De novo assembly with noisy, ultra-long reads Fast, efficient assembly of accurate long reads

Quantitative Performance Comparison

Performance data synthesized from recent benchmark studies (2023-2024).

Table 1: Assembly Metrics on Model Organism Data (Human CHM13)

Assembler Read Type Contiguity (NG50, Mb) Base Accuracy (QV) Runtime (CPU hrs) Memory (GB)
Flye (v2.9+) ONT Ultra-long (N50>50kb) 45 - 65 ~30-40 (pre-polish) 80 - 120 ~100
Flye PacBio HiFi 25 - 35 ~40-45 (pre-polish) 60 - 90 ~80
Shasta (v0.11.0) PacBio HiFi 20 - 30 >45 (directly) 5 - 15 ~30
Shasta ONT Reads Often fails/fragmented Very Low - -

Table 2: Repeat Resolution & Computational Efficiency

Metric Flye Shasta
Repeat Resolution Excellent with ultra-long reads Good for HiFi-sized repeats
Polishing Required Mandatory for ONT data Often optional; built-in consensus
Scalability High memory for large genomes Highly scalable, low memory
Multi-Platform Data Can mix read types HiFi-specific

Experimental Protocols

Protocol A: Flye Assembly for Oxford Nanopore Data This protocol is central to the encompassing thesis.

  • Input Preparation: Basecalled ONT reads (FASTQ). Recommended: Q-score >10, read N50 >20kb. Use filtlong to retain longest reads covering ~40x genome coverage.
  • Assembly Command:

    Flags: --nano-hq for Q20+ data, --pacbio-raw for CLR, --pacbio-hifi for HiFi.
  • Iterative Polishing (Critical for ONT): Use Medaka or NextPolish with the raw reads.

  • Evaluation: Assess with QUAST, BUSCO, and Merqury (if available).

Protocol B: Shasta Assembly for PacBio HiFi Data

  • Input Preparation: PacBio HiFi reads (FASTQ). No filtering typically required.
  • Configuration & Assembly:

  • Optional Polishing: Usually unnecessary. For maximal accuracy, one round of racon or marginpolish can be applied.
  • Evaluation: As in Protocol A.

The Scientist's Toolkit: Key Reagent Solutions

Table 3: Essential Materials for Long-Read Assembly Workflows

Item Function & Relevance
Oxford Nanopore Ligation Sequencing Kit (SQK-LSK114) Prepares genomic DNA for sequencing on Nanopore devices, producing the noisy long reads Flye is designed for.
PacBio SMRTbell Prep Kit 3.0 Prepares DNA for PacBio sequencing, enabling the generation of HiFi reads for optimal Shasta assembly.
MGI/NEB Next Ultra II DNA Library Prep Kit Optional for generating complementary short-read data for hybrid polishing or validation.
DNeasy Blood & Tissue Kit (Qiagen) High-quality, high-molecular-weight DNA extraction is a prerequisite for both read types.
BluePippin or SageELF System Size selection system to enrich ultra-long DNA fragments (>50 kb), critical for maximizing Flye's performance on ONT data.

Visualization of Assembly Workflows & Decision Logic

G Start Start: Sequencing Data Decision1 Primary Read Type? Start->Decision1 ONT Oxford Nanopore (Noisy, Ultra-Long) Decision1->ONT Yes HiFi PacBio HiFi (High Accuracy) Decision1->HiFi No Tool1 Primary Assembler: FLYE ONT->Tool1 Tool2 Primary Assembler: SHASTA HiFi->Tool2 Polish Iterative Polishing (Medaka/Racon) Tool1->Polish Mandatory Eval Evaluation (QUAST, BUSCO) Tool2->Eval Polishing Optional Polish->Eval End Final Assembly Eval->End

Title: Assembly Workflow Decision Logic for Flye vs. Shasta

G cluster_flye Flye Protocol for ONT Data cluster_shasta Shasta Protocol for HiFi Data F1 1. Input: Noisy Long Reads F2 2. Overlap & Disjointig Construction F1->F2 F3 3. Repeat Graph Assembly F2->F3 F4 4. Iterative Consensus & Error Correction F3->F4 F5 5. Output: Draft Assembly F4->F5 S1 1. Input: HiFi Reads S2 2. Run-Length Encoding (RLE) S1->S2 S3 3. Overlap Detection & Read Alignment S2->S3 S4 4. Marker Graph & Consensus Call S3->S4 S5 5. Output: High-Quality Assembly S4->S5

Title: Core Algorithmic Stages of Flye and Shasta

This protocol details a critical validation module for a broader thesis investigating high-accuracy genome assembly from Oxford Nanopore Technologies (ONT) long reads using the Flye assembler. While Flye effectively resolves large-scale genome structure, residual per-base errors necessitate polishing. This document establishes a rigorous, orthogonal validation pipeline using short-read Illumina data for polishing followed by comprehensive assessment with the QUAST (Quality Assessment Tool for Genome Assemblies) toolkit against a trusted reference genome. This two-step process confirms both consensus accuracy and structural fidelity, essential for downstream applications in comparative genomics and target identification for drug development.

Experimental Protocols

Short-Read Polishing with POLCA

Objective: Correct residual indel and substitution errors in the Flye assembly using high-accuracy short-read data.

Materials:

  • Input Flye assembly (flye_assembly.fasta)
  • Paired-end Illumina reads (R1.fastq.gz, R2.fastq.gz)
  • High-performance computing (HPC) cluster or server with ≥16 GB RAM.

Procedure:

  • Software Installation: Install MASURCA v4.1.0, which includes POLCA.

  • Run POLCA:

    • -a: Input assembly FASTA.
    • -r: Space-separated list of read files.
    • -t: Number of threads.
    • -m: Memory usage per thread.
  • Output: The polished assembly is saved as flye_assembly.fasta.PolcaCorrected.fa. This file is used for all downstream validation.

Reference-Based Assessment with QUAST

Objective: Quantify assembly accuracy and completeness by aligning the polished assembly to a high-quality reference genome.

Materials:

  • Polished assembly (flye_assembly.fasta.PolcaCorrected.fa)
  • Reference genome (reference.fasta)
  • (Optional) Reference gene annotations (reference.gff)

Procedure:

  • Install QUAST: Install the latest version (v5.2.0 as of latest search).

  • Execute QUAST with Reference:

  • Analyze Output: Key reports are in quast_results/report.txt, icarus.html, and transposed_report.tex.

The following table summarizes key quantitative metrics from a QUAST analysis, comparing a Flye assembly before and after short-read polishing against the GRCh38 human reference. Data is simulated based on typical results from current literature.

Table 1: Comparative QUAST Metrics for Flye Assembly Pre- and Post-Polishing

Metric Flye (Unpolished) Flye + POLCA (Polished) Improvement
Total Length (bp) 2,998,456,123 2,998,501,456 +45,333
Reference Coverage (%) 99.7 99.7 0.0
# Misassemblies 142 85 -57
# Mismatches per 100 kbp 385.2 12.7 -372.5
# Indels per 100 kbp 89.6 5.3 -84.3
Largest Alignment (bp) 85,432,112 122,567,890 +37,135,778
NGA50 (contigs) 24,567,890 35,678,123 +11,110,233
# Genes 59,123 59,845 +722
# Complete Genes (%) 96.2 99.1 +2.9%
Genome Fraction (%) 98.5 99.4 +0.9%

Visualization of Workflows

G ONT ONT Flye Flye ONT->Flye Long Reads Draft Draft Assembly (flye_assembly.fasta) Flye->Draft POLCA POLCA Draft->POLCA Illumina Illumina Illumina->POLCA Short Reads Polished Polished Assembly POLCA->Polished QUAST QUAST Polished->QUAST Reference Reference Reference->QUAST Report QUAST Report (HTML/PDF) QUAST->Report

Title: Orthogonal Assembly Validation Workflow

G Inputs Polished Assembly & Reference Genome QUAST_Core QUAST (quast.py) Inputs->QUAST_Core ContigAlign Contig Alignment & Parsing QUAST_Core->ContigAlign MetricCalc Metric Calculation ContigAlign->MetricCalc VizGen Visualization Generation MetricCalc->VizGen Output1 report.txt (report.pdf) VizGen->Output1 Output2 icarus.html (Interactive Viewer) VizGen->Output2 Output3 transposed_report.* (Latex/TSV) VizGen->Output3

Title: QUAST Analysis Pipeline Structure

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials and Software for Validation

Item Function/Benefit Example/Version
Flye Assembler Specialized assembler for long, error-prone reads, creating initial contigs. v2.9.3
Illumina Paired-End Reads High-accuracy (~Q30) short reads for orthogonal polishing of consensus errors. NovaSeq 6000, 2x150 bp
POLCA (MASURCA) Fast, stand-alone polishing module that uses short reads to fix indels/substitutions. MASURCA v4.1.0
QUAST Comprehensive quality assessment tool for comparing assemblies to a reference. v5.2.0
Reference Genome High-quality, trusted reference (e.g., from RefSeq) for alignment and metric calculation. GRCh38.p14 (Human)
Gene Annotation File (GFF/GTF) Enables assessment of gene space completeness (Complete/Broken Genes). NCBI RefSeq .gff
Compute Infrastructure HPC or server with sufficient memory (>32 GB recommended) and multi-core CPUs. 16+ cores, 64+ GB RAM
Visualization Software For interpreting QUAST's Icarus contig browser and generating publication figures. Modern Web Browser, Adobe Illustrator

Application Notes

This document presents a performance benchmark of the Flye long-read assembler within a broader thesis research context focused on optimizing de novo assembly pipelines for Oxford Nanopore Technologies (ONT) sequencing data. Flye is a repeat graph-based assembler designed specifically for noisy long reads, making it a leading candidate for ONT datasets. The following notes summarize its performance across diverse genomic scales.

Table 1: Flye Assembly Performance Across Genomic Datasets (Representative Metrics)

Dataset Type Sample/Strain Approx. Genome Size Read N50 (ONT) Flye Assembly Contiguity (N50) Estimated Completeness (BUSCO) Key Challenge Addressed
Microbial Escherichia coli K-12 4.6 Mb ~30 kb >4.5 Mb (circularized) 99.8% Rapid, accurate bacterial genome finishing.
Microbial Saccharomyces cerevisiae W303 12.2 Mb ~25 kb ~12.1 Mb 99.5% Resolving yeast telomeres and repeats.
Plant Arabidopsis thaliana (Col-0) 135 Mb ~15 kb ~10-15 Mb ~98.5% Polypoidy and moderate repeats.
Plant Oryza sativa (Rice) 400 Mb ~10-20 kb ~2-5 Mb ~97.8% Large, complex repetitive genome.
Human (T2T) HG002 (Diploid) 3.1 Gb Ultra-long (>100 kb) ~70-100 Mb (chr-arm scale) >99.9%* Gapless, telomere-to-telomere haplotypes.

Note: Human completeness is assessed against specialized T2T reference benchmarks. BUSCO: Benchmarking Universal Single-Copy Orthologs.

Key Insights: Flye demonstrates robust performance across all scales, excelling in microbial genome closure and producing highly contiguous assemblies for complex eukaryotes when coupled with ultra-long ONT reads. Its repeat graph algorithm is particularly effective in untangling large, identical repeats prevalent in plant and human genomes.

Detailed Protocols

Protocol 1: StandardDe NovoAssembly with Flye for Microbial Genomes

Objective: Generate a complete, circularized bacterial genome assembly from ONT data.

Research Reagent Solutions & Essential Materials: Table 2: Key Research Reagent Solutions

Item Function
ONT Ligation Sequencing Kit (SQK-LSK114) Prepares genomic DNA for sequencing with motor proteins and adapters.
Flow Cell (R10.4.1 or newer) The solid-state sensor for electrophoretic sequencing of DNA strands.
Guanidine Hydrochloride (GuHCl) in Wash Buffer Common wash buffer additive to improve read length and yield.
Flye (v2.9.3 or later) Core assembly algorithm software.
MiniASM/Purge Dups Optional tool for haploid microbial assembly polishing and duplication removal.
BUSCO (v5) with bacteria_odb10 Assesses genomic completeness and assembly quality.

Methodology:

  • DNA Preparation & Sequencing: Extract high-molecular-weight (HMW) genomic DNA using a gentle protocol (e.g., phenol-chloroform). Prepare library using the ONT Ligation Sequencing Kit according to manufacturer protocol, with a target input of 1-3 µg. Load onto a PromethION or MinION flow cell (R10.4.1 recommended).
  • Basecalling & Quality Filtering: Perform high-accuracy basecalling using dorado (v0.5.0+) in super-accuracy mode. Filter reads based on length and quality (e.g., NanoFilt -l 5000 --minq 10).
  • Flye Assembly:

  • Assembly Evaluation: Run BUSCO to assess completeness.

  • Circularization & Polishing: Identify circular contigs from Flye output (assembly_info.txt). Rotate sequences if needed. Optional polishing with medaka (using an appropriate model) can be applied.

Protocol 2: Hybrid Assembly for Complex Plant Genomes

Objective: Generate a chromosome-scale assembly for a mid-sized plant genome using ONT long reads and Hi-C scaffolding.

Methodology:

  • Data Acquisition: Generate ~50x coverage of ONT long reads (N50 >15 kb) using HMW DNA from a single individual. Generate ~100x coverage of Illumina paired-end reads for polishing. Generate Hi-C library data for scaffolding.
  • Initial Flye Assembly: Run Flye on the filtered ONT reads with the expected genome size (e.g., --genome-size 135m for Arabidopsis).
  • Polishing: Polish the initial Flye assembly using NextPolish2 with the Illumina reads, following a multi-round (sgs then lgs) protocol to correct small indels and SNPs.
  • Hi-C Scaffolding: Use Juicer and 3D-DNA or SALSA2 to scaffold the polished assembly into pseudo-chromosomes using the Hi-C data. Manually review and correct the assembly in Juicebox.
  • Evaluation: Assess contiguity with QUAST. Evaluate assembly accuracy and phasing using Merqury (if parental k-mer counts available) and BUSCO with the viridiplantae_odb10 lineage.

Visualizations

G cluster_workflow Flye Assembly and Benchmarking Workflow cluster_bench Benchmarking Suite A HMW DNA Extraction B ONT Library Prep & Sequencing A->B C Basecalling & Read QC B->C D Flye De Novo Assembly C->D E Assembly Polishing (Medaka/NextPolish) D->E F Scaffolding (Hi-C/Ultra-long) E->F G Benchmarking F->G G1 Contiguity (QUAST) G2 Completeness (BUSCO) G3 Accuracy (Merqury)

Diagram 1: Flye Assembly and Benchmarking Workflow

G cluster_pathway Flye Repeat Graph Resolution Logic Input Noisy Long Reads RG Construct Repeat Graph Input->RG TD Tangled Repeats RG->TD Resolve Resolve via Read Disagreements TD->Resolve Yes Untangle Untangle Graph TD->Untangle No Resolve->Untangle Output Disjointig Paths (Contigs) Untangle->Output

Diagram 2: Flye Repeat Graph Resolution Logic

Conclusion

Flye stands as a robust, efficient, and purpose-built assembler for Oxford Nanopore long-read data, successfully balancing the challenges of high error rates with the power of long-range genomic information. Through its repeat graph approach, it excels in producing contiguous assemblies, resolving complex regions, and detecting structural variants—key requirements for modern genomic research. The protocol's effectiveness hinges on proper data preparation, parameter selection tailored to read quality (raw vs. HQ), and systematic post-assembly polishing. While tools like Canu offer alternative strategies and Raven provides speed, Flye's consistent performance and active development make it a cornerstone of many nanopore analysis pipelines. Future directions include enhanced integration with duplex and ultra-long reads, improved real-time assembly capabilities, and tailored workflows for clinical and single-cell applications. As nanopore sequencing continues to evolve in accuracy and throughput, Flye's methodologies will remain critical for unlocking the full potential of long-read genomics in biomedical discovery, pathogen surveillance, and personalized medicine.