Genome assembly using PacBio data

Author(s)	Anthony Bretaudeau Alexandre Cormier Erwan Corre Laura Leroi Stéphanie Robin Solenne Correard
Reviewers

Overview
Questions:

How to perform a genome assembly with PacBio data ?

How to check assembly quality ?

Objectives:

Assemble a Genome with PacBio data

Assess assembly quality

Requirements:

Introduction to Galaxy Analyses

slides Slides: Quality Control

tutorial Hands-on: Quality Control

Time estimation: 6 hours

Level: Intermediate Intermediate

Supporting Materials:

Workflows

FAQs

instances Available on these Galaxies

Known Working

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Published: Nov 29, 2021

Last modification: Apr 23, 2025

License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MIT

purl PURL: https://gxy.io/GTN:T00033

rating Rating: 5.0 (1 recent ratings, 3 all time)

version Revision: 11

In this tutorial, we will assemble a genome of a species of fungi in the family Aspergillus, Aspergillus niger, from PacBio sequencing data. These data will be downloaded from ENA. The quality of the assembly obtained will be analyzed, in particular by comparing it to a reference assembly, available on ENA.

Agenda

In this tutorial, we will cover:

Get data

Get data from ENA

Get reference genome from ENA

Genome Assembly

Quality assessment

Genome assemblies comparison with Quast

Genome assembly assessment with BUSCO

Conclusion

Get data

We will use long reads sequencing data: HiFi (High Fidelity long reads) from PacBio sequencing of Aspergillus niger genome. This data is available on ENA. We will also use later a reference genome assembly downloaded from ENA.

Get data from ENA

Hands On: Data upload from ENA
Create a new history for this tutorial
Import the files from ENA
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR317/012/SRR31719412/SRR31719412_subreads.fastq.gz
Copy the link location

Click galaxy-upload Upload Data at the top of the tool panel

Select galaxy-wf-edit Paste/Fetch Data

Paste the link(s) into the text field

Press Start

Close the window
Rename the datasets

Check that the datatype is fastq.gz

Click on the galaxy-pencil pencil icon for the dataset to edit its attributes

In the central panel, click galaxy-chart-select-data Datatypes tab on the top

In the galaxy-chart-select-data Assign Datatype, select datatypes from “New type” dropdown

Tip: you can start typing the datatype into the field to filter the dropdown menu

Click the Save button

Get reference genome from ENA

Hands On: Data upload from ENA

Reference genome is available here: ASM4765177v1 assembly for Aspergillus niger

Download the WGS Set FASTA (JBKZXA01.fasta.gz) on your computer Make sure you download WGS Set FASTA (JBKZXA01.fasta.gz) and NOT ALL Set FASTA

Upload this file on Galaxy (Upload –> Choose local file –> Select the file –> Start –> Close)

Check that the datatype is fasta.gz

Click on the galaxy-pencil pencil icon for the dataset to edit its attributes

In the central panel, click galaxy-chart-select-data Datatypes tab on the top

In the galaxy-chart-select-data Assign Datatype, select datatypes from “New type” dropdown

Tip: you can start typing the datatype into the field to filter the dropdown menu

Click the Save button

Genome Assembly

Hands-on: Choose Your Own Tutorial

This is a "Choose Your Own Tutorial" (CYOT) section (also known as "Choose Your Own Analysis" (CYOA)), where you can select between multiple paths. Click one of the buttons below to select how you want to follow the tutorial

Hifiasm and Flye are two well known assembler.

Hifiasm Flye

Hifiasm is a fast haplotype-resolved de novo assembler initially designed for PacBio HiFi reads. In general, hifiasm generates the assembly graphs in the GFA format, so a step of conversion to fasta is necessary. The GFA 1.0 format is a tab-delimited text format for describing a set of sequences and their overlap.
Hifiasm produces arguably the best single-sample telomere-to-telomere assemblies combing HiFi, ultralong and Hi-C reads, and it is one of the best haplotype-resolved assemblers for the trio-binning assembly given parental short reads. For a human genome, hifiasm can produce the telomere-to-telomere assembly in one day.

Hands On: Assembly with Hifiasm

Hifiasm ( Galaxy version 0.25.0+galaxy0) with the following parameters:

“Mode”: Standard

param-file “Input reads”: the raw data (fastq.gz)

“Output log file”: Set to yes

The tool produces five datasets: Haplotype-resolved raw unitig graph, Haplotype-resolved processed unitig graph without small bubbles, Primary assembly contig graph, Alternate assembly contig graph, [hap1]/[hap2] contig graph.

GFA to FASTA ( Galaxy version 0.1.2) with the following parameters:

param-file “Input GFA file”: primary assembly contig graph

Question

What are the different output datasets from Hifiasm?

Haplotype-resolved raw unitig graph: This graph keeps all haplotype information, including somatic mutations and recurrent sequencing errors.

Haplotype-resolved processed unitig graph without small bubbles: This graph ‘pops’ small bubbles in the raw unitig graph; small bubbles might be caused by somatic mutations or noise in data, which are not the real haplotype information.

Primary assembly contig graph: This graph includes a complete assembly with long stretches of phased blocks, though there may be some haplotype collapse.

Alternate assembly contig graph: This graph consists of all contigs that are discarded from the primary contig graph.

[hap1]/[hap2] contig graph: Each graph consists of phased contigs (output only with Hi-C phasing enabled).

We will use Flye, a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PacBio / ONT reads as input and outputs polished contigs. Flye also has a special mode for metagenome assembly. All informations about Flye assembler are here: Flye.

Hands On: Assembly with Flye

Flye ( Galaxy version 2.9.5+galaxy1) with the following parameters:

param-file “Input reads”: the raw data (fastq.gz)

“Mode”: PacBio HiFi

“Number of polishing iterations”: 1

“Reduced contig assembly coverage”: Disable reduced coverage for initial disjointing assembly

“Generate a log file”: Set to yes

The tool produces four datasets: consensus, assembly graph, graphical fragment assembly and assembly info

Question

What are the different output datasets?

The first dataset (consensus) is a fasta file containing the final assembly (1461 contigs). You may notice that the result (contigs number) you obtained is sligthy different from the one presented here. This is due to the Flye assembly algorithm which doesn’t always give the eact same results.

The second and third dataset are assembly graph files. These graphs are used to represent the final assembly of a genome, they are based on reads and their overlap information. Some tools such as Bandage allow to visualize the assembly graph.

The fourth dataset is a tabular file (assembly_info) containing extra information about contigs/scaffolds.

Quality assessment

Genome assemblies comparison with Quast

A way to calculate metrics assembly is to use QUAST = QUality ASsessment Tool. Quast is a tool to evaluate genome assemblies by computing various metrics. The manual of Quast is here: Quast Quast allows to compare the assembly carried out with the reference genome and produces statistics such as the genome fraction. However, when comparing to a reference genome, Quast results do not display the assembly N50, therefore, in this tutorial, you will generate two quast reports : One specifying a reference genome and one not specifying a reference genome.

Hands On: Quast

Quast ( Galaxy version 5.0.2+galaxy3) specifying a reference genome with the following parameters:

“Assembly mode?”: Individual assembly (1 contig file per sample)

“Use customized names for the input files?”: No, use dataset names

param-collection “Contigs/scaffolds file”: JBKZXA01.fasta.gz (reference assembly), fasta file (output of GFA to FASTA tool) and/or consensus (output of Flye tool)

“Type of assembly”: Genome

“Use a reference genome?”: Yes

“Reference genome”: JBKZXA01.fasta.gz (reference assembly)

“Type of organism”: Fungus: use of GeneMark-ES for gene finding, ...

Quast ( Galaxy version 5.0.2+galaxy3) without specifying a reference genome with the following parameters:

“Assembly mode?”: Individual assembly (1 contig file per sample)

“Use customized names for the input files?”: No, use dataset names

param-collection “Contigs/scaffolds file”: JBKZXA01.fasta.gz (reference assembly), fasta file (output of GFA to FASTA tool) and/or consensus (output of Flye tool)

“Type of assembly”: Genome

“Use a reference genome?”: No

“Type of organism”: Fungus: use of GeneMark-ES for gene finding, ...

Question

Compare the different metrics obtained for the assembly you generated and the reference genome.

We compare the metrics of the three assemblies:

The reference genome (JBKZXA01.fasta.gz): 8 contigs, Contig N50 = 3.5Mb, Contig length max = 6.2 Mb, size = 35.4Mb, 49.51% GC

The Hifiasm assembly: 105 contigs, N50 = 4.9 Mb, length max = 7.1Mb, assembly size = 42.1 Mb, 47.91% GC

The Flye assembly: 13 contigs, N50 = 4.9Mb, length max = 6.8Mb, size = 38.7 Mb, 49.31% GC

Genome assembly assessment with BUSCO

BUSCO (Benchmarking Universal Single-Copy Orthologs) allows a measure for quantitative assessment of genome assembly based on evolutionarily informed expectations of gene content. Details for this tool are here: Busco website

Hands On: BUSCO on assembly

Busco ( Galaxy version 5.8.0+galaxy0) with the following parameters:

“Tool version”: Galaxy Version 5.8.0+galaxy0

param-file “Sequences to analyse”: Multiple datasets

param-collection “Sequences to analyse”: JBKZXA01.fasta.gz (reference assembly), fasta file (output of GFA to FASTA tool) and/or consensus (output of Flye tool)

“Auto-detect or select lineage”: Select lineage

“Lineage”: Fungi

“Which outputs should be generated”: short summary text; summary image

Question

Compare the number of BUSCO genes identified in the generated assembly and the reference genome. What do you observe ?

Short summary generated by BUSCO indicates that reference genome (JBKZXA01.fasta.gz) contains:

751 Complete BUSCOs (of which 749 are single-copy and 2 are duplicated),

0 fragmented BUSCOs,

7 missing BUSCOs.

Short summary generated by BUSCO indicates that Hifiasm assembly contains:

756 complete BUSCOs (754 single-copy and 2 duplicated),

0 fragmented BUSCOs

2 missing BUSCOs.

Short summary generated by BUSCO indicates that Flye assembly contains:

755 complete BUSCOs (753 single-copy and 2 duplicated),

0 fragmented BUSCOs

3 missing BUSCOs.

BUSCO analysis confirms that these two assemblies are of similar quality, with similar number of complete, fragmented and missing BUSCOs genes.

Conclusion

This pipeline shows how to generate and evaluate a genome assembly from long reads PacBio data. Once you are satisfied with your genome sequence, you might want to annotate it: have a look at the RepeatMasker and Funannoate tutorials to learn how to do it!

You've Finished the Tutorial

Key points

PacBio data allows to perform good quality genome assembly

Quast and BUSCO make it easy to compare the quality of assemblies

Frequently Asked Questions

Have questions about this tutorial? Have a look at the available FAQ pages and support channels

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Citing this Tutorial

Anthony Bretaudeau, Alexandre Cormier, Erwan Corre, Laura Leroi, Stéphanie Robin, Solenne Correard, Genome assembly using PacBio data (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/assembly/tutorials/flye-assembly/tutorial.html Online; accessed TODAY
Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

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	title = "Genome assembly using PacBio data (Galaxy Training Materials)",
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	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol}
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Funding

These individuals or organisations provided funding support for the development of this resource

Gallantries

This project (2020-1-NL01-KA203-064717) is funded with the support of the Erasmus+ programme of the European Union. Their funding has supported a large number of tutorials within the GTN across a wide array of topics.

Congratulations on successfully completing this tutorial!

Do you want to extend your knowledge?
Follow one of our recommended follow-up trainings:

tutorial Hands-on: Masking repeats with RepeatMasker

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/assembly/tutorials/flye-assembly/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: flye
  owner: bgruening
  revisions: 276f5d8712d5
  tool_panel_section_label: Assembly
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: flye
  owner: bgruening
  revisions: 13e27b9094e8
  tool_panel_section_label: Assembly
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: hifiasm
  owner: bgruening
  revisions: 5d365d5cbe9d
  tool_panel_section_label: Assembly
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: busco
  owner: iuc
  revisions: 46ae58b1d792
  tool_panel_section_label: Annotation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: busco
  owner: iuc
  revisions: e5c372c91e46
  tool_panel_section_label: Annotation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: fasta_stats
  owner: iuc
  revisions: 0dbb995c7d35
  tool_panel_section_label: FASTA/FASTQ
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: gfa_to_fa
  owner: iuc
  revisions: e33c82b63727
  tool_panel_section_label: Convert Formats
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: quast
  owner: iuc
  revisions: 7594365c546b
  tool_panel_section_label: Assembly
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

t{ hist[0] | to_stars }} 1

t{ hist[0] | to_stars }} 2

March 2025

5 stars: Liked: Very easy to follow and understand Disliked: I believe it would be best to include an estimate to how long certain processes might take. The hands on stuff can be done very quickly, the waiting period is the long part. The time requirement in the begining might be missleading, becuase it makes the user believe they need six hours of active work to complete the tutorial Also, it would be great to include small tips in how to visualize the results of Flye. A program was recommended but from this tutorial it is not clear how to actually do it. I understand it is something outside of galaxy, so not the goal of the tutorial, but it would just be a nice thing to add to make the learning proccess more complete.

September 2022

4 stars: Liked: To test the tools of assembly in a control dataset Disliked: I think it could be valable to understand the tools and the parameters of the tools while using them: to not do it without thinking it through. I think it is better to understand to remember.

June 2022

4 stars: Liked: straightforward Disliked: would have liked to have some practice viewing the assembly graphs and with visual methods for comparing genomes (e.g. dot, synteny plots)