Using the VGP workflows to assemble a vertebrate genome with HiFi and Hi-C data
Author(s) | Delphine Lariviere Alex Ostrovsky Cristóbal Gallardo Brandon Pickett Linelle Abueg Anton Nekrutenko |
Reviewers |
OverviewQuestions:Objectives:
What combination of tools can produce the highest quality assembly of vertebrate genomes?
How can we evaluate how good it is?
Requirements:
Learn the tools necessary to perform a de novo assembly of a vertebrate genome
Evaluate the quality of the assembly
Time estimation: 2 hoursLevel: Intermediate IntermediateSupporting Materials:Published: Apr 6, 2022Last modification: Oct 8, 2024License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MITpurl PURL: https://gxy.io/GTN:T00040rating Rating: 5.0 (1 recent ratings, 2 all time)version Revision: 23
The Vertebrate Genome Project (VGP), a project of the Genome 10K (G10K) Consortium, aims to generate high-quality, near error-free, gap-free, chromosome-level, haplotype-phased, annotated reference genome assemblies for every vertebrate species (Rhie et al. 2020). The VGP has developed a fully automated de-novo genome assembly pipeline, which uses a combination of three different technologies: Pacbio high fidelity reads (HiFi), all-versus-all chromatin conformation capture (Hi-C) data, and (optionally) Bionano optical map data. The pipeline consists of nine distinct workflows. This tutorial provides a quick example of how to run these workflows for one particular scenario, which is, based on our experience, the most common: assembling genomes using HiFi Reads combined with Hi-C data (both generated from the same individual).
AgendaIn this tutorial, we will cover:
Getting started on Galaxy
This tutorial assumes you are comfortable getting data into Galaxy, running jobs, managing history, etc. If you are unfamiliar with Galaxy, we recommend you visit the Galaxy Training Network. Consider starting with the following trainings:
- Introduction to Galaxy
- Galaxy 101
- Getting Data into Galaxy
- Using Dataset Collections
- Understanding the Galaxy History System
- Introduction to Galaxy Analyses
- Downloading and Deleting Data in Galaxy
The VGP-Galaxy pipeline
The VGP assembly pipeline has a modular organization, consisting in ten workflows (Fig. 1). It can used with the following types of input data:
Input data | Assembly quality | Analysis trajectory (Fig. 1) |
---|---|---|
HiFi | The minimum requirement | A |
HiFi + HiC | Better continuity | B |
HiFi + Bionano | Better continuity | C |
HiFi + Hi-C + Bionano | Even better continuity | D |
HiFi + parental data | Better haplotype resolution | E |
HiFi + parental data + Hi-C | Better haplotype resolution and improved continuity | F |
HiFi + parental + Bionano | Better haplotype resolution and improved continuity | G |
HiFi + parental data + Hi-C + Bionano | Better haplotype resolution and ultimate continuity | H |
In this table, HiFi and Hi-C data are derived from the individual whose genome is being assembled. “Parental data” refers to high coverage whole genome resequencing data from the parents of the individual being assembled. Assemblies utilizing parental data for phasing are also called “Trios”. Each combination of input datasets is supported by an analysis trajectory: a combination of workflows designed for generating assembly given a particular combination of inputs. These trajectories are listed in the table above and shown in the figure below.
The first stage of the pipeline is the generation of a k-mer profile of the raw reads to estimate genome size, heterozygosity, repetitiveness, and error rate necessary for parameterizing downstream workflows. The generation of k-mer counts can be done from HiFi data only (Workflow 1) or include data from parental reads for trio-based phasing (Workflow 2). The second stage is contig assembly. In addition to using only HiFi reads (Workflow 3), the contig-building (contiging) step can leverage Hi-C (Workflow 4) or parental read data (Workflow 5) to produce fully-phased haplotypes (hap1/hap2 or parental/maternal assigned haplotypes), using hifiasm
. The contiging workflows also produce a number of critical quality control (QC) metrics such as k-mer multiplicity profiles. Inspection of these profiles provides information to decide whether the third stage—purging of false duplication—is required. Purging (Workflow 6), using purge_dups
identifies and resolves haplotype-specific assembly segments incorrectly labeled as primary contigs, as well as heterozygous contig overlaps. This increases continuity and the quality of the final assembly. The purging stage is generally unnecessary for trio data, as thses assemblies achieve reliable haplotype resolution using k-mer set operations based on parental reads. Generally, HiC-phased contigs also do not need to undergo purging, but if so, then there exists Workflow 6b to purge a single haplotype at a time. The fourth stage, scaffolding, produces chromosome-level scaffolds using information provided by Bionano (Workflow 7), with Bionano Solve
(optional) and Hi-C (Workflow 8) data and YaHS
algorithms. A final stage of decontamination (Workflow 9) removes exogenous sequences (e.g., viral and bacterial sequences) from the scaffolded assembly. A separate workflow (WF0) is used to assemble and annotate a mitogenome.
Comment: A note on data qualityWe suggest at least 30✕ PacBio HiFi coverage and 60✕ Hi-C coverage (both diploid coverage).
Getting the data
The following steps use PacBio HiFi and Illumina Hi-C data from baker’s yeast (Saccharomyces cerevisiae). The tutorial represents trajectory B from Fig. 1 above. For this tutorial, the first step is to get the datasets from Zenodo. Specifically, we will be uploading two datasets:
- A set of PacBio HiFi reads in
fasta
format. Please note that your HiFi reads received from a sequencing center will usually be fastqsanger.gz format, but the dataset used in this tutorial has been converted to fasta for space.. - A set of Illumina Hi-C reads in
fastqsanger.gz
format.
Uploading fasta
datasets from Zenodo
The following two steps demonstrate how to upload three PacBio HiFi datasets into your Galaxy history.
Hands-on: Uploading FASTA datasets from Zenodo
Create a new history for this tutorial
To create a new history simply click the new-history icon at the top of the history panel:
Copy the following URLs into clipboard.
(You can do this by clicking on copy button in the right upper corner of the box below. It will appear if you mouse over the box.)
https://zenodo.org/record/6098306/files/HiFi_synthetic_50x_01.fasta https://zenodo.org/record/6098306/files/HiFi_synthetic_50x_02.fasta https://zenodo.org/record/6098306/files/HiFi_synthetic_50x_03.fasta
Upload datasets into Galaxy.
- set the datatype to
fasta
- 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
Change Type (set all): from “Auto-detect” to
fasta
Press Start
- Close the window
Uploading
fasta
orfasta.gz
datasets via URL.
Uploading fastqsanger.gz
datasets from Zenodo
Illumina Hi-C data is uploaded in essentially the same way as shown in the following two steps.
Warning: Make sure you the choose correct format!When selecting datatype in “Type (set all)” drop-down, make sure you select
fastqsanger
orfastqsanger.gz
BUT NOTfastqcssanger
or anything else!
Hands-on: Uploading fastqsanger.gz datasets from Zenodo
- Copy the following URLs into clipboard.
- you can do this by clicking on copy button in the right upper corner of the box below. It will appear if you mouse over the box.
https://zenodo.org/record/5550653/files/SRR7126301_1.fastq.gz https://zenodo.org/record/5550653/files/SRR7126301_2.fastq.gz
- Upload datasets into Galaxy.
- set the datatype to
fastqsanger.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
Change Type (set all): from “Auto-detect” to
fasta
Press Start
- Close the window
Uploading
fastqsanger
orfastqsanger.gz
datasets via URL.
Click on Upload Data on the top of the left panel:
Click on Paste/Fetch:
Paste URL into text box that would appear:
Set Type (set all) to
fastqsanger
or, if your data is compressed as in URLs above (they have.gz
extensions), tofastqsanger.gz
:
Warning: Danger: Make sure you choose corect format!When selecting datatype in “Type (set all)” dropdown, make sure you select
fastaqsanger
orfastqsanger.gz
BUT NOTfastqcssanger
or anything else!- Rename the datasets as follow:
- Rename
SRR7126301_1.fastq.gz
asHi-C forward reads
- Rename
SRR7126301_2.fastq.gz
asHi-C reverse reads
- Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
- In the central panel, change the Name field
- Click the Save button
Warning: These datasets are large!Hi-C datasets are large. It will take some time (~15 min) for them to be fully uploaded. Please, be patient.
Organizing the data
If everything goes smoothly, your history will look like shown in Fig. 4 below. The three HiFi fasta files are better represented as a collection: Galaxy’s way to represent multiple datasets as a single interface entity (collection). Also, importantly, the workflow we will be using for the analysis of our data takes a collection as an input (it does not access individual datasets). So let’s create a collection using steps outlines in the Tip tip “Creating a dataset collection” that you can find below Fig. 4.
- Click on galaxy-selector Select Items at the top of the history panel
- Check all the datasets in your history you would like to include
Click n of N selected and choose Build Dataset List
- Enter a name for your collection
- Click Create collection to build your collection
- Click on the checkmark icon at the top of your history again
You can obviously upload your own datasets via URLs as illustrated above or from your own computer. In addition, you can upload data from a major repository called GenomeArk. GenomeArk is integrated directly into Galaxy Upload. To use GenomeArk following the steps in the Tip tip below:
- Open the file galaxy-upload upload menu
- Click on Choose remote files tab
- Click on the Genome Ark button and then click on species
You can find the data by following this path:
/species/${Genus}_${species}/${specimen_code}/genomic_data
. Inside a given datatype directory (e.g.pacbio
), select all the relevant files individually until all the desired files are highlighted and click the Ok button. Note that there may be multiple pages of files listed. Also note that you may not want every file listed.
Once we have imported the datasets, the next step is to import the workflows necessary for the analysis of our data from DockStore.
Importing workflows
All analyses described in this tutorial are performed using workflows–chains of tools–shown in Fig. 1. Specifically, we will use four workflows corresponding to analysis trajectory B: 1, 4, 6, and 8. To use these four workflows you need to import them into your Galaxy account following the steps below. Note: these are not necessarily the latest versions of the actual workflows, but versions that have been tested for this tutorial. To see the latest versions, see the Galaxy Project VGP workflows page and click on the Dockstore links to import workflows. Alternatively, for each section of the tutorial, there will be a “Launch [workflow] (View on Dockstore)” link at the beginning, which you can use to launch the workflow.
Performing the assembly
Workflows listed in Fig. 1 support a variety of “analysis trajectories”. The majority of species that were sequenced by the VGP usually contain HiFi reads for the individual being sequenced supplemented with Hi-C data. As a result most assemblies performed by us follow the trajectory B. This is why this tutorial was designed to follow this trajectory as well.
Genome profile analysis (WF1)
Before the assembly can be run, we need to collect metrics on the properties of the genome under consideration, such as the expected genome size according to our data. The present pipeline uses Meryl for generating the k-mer database and Genomescope2 for determining genome characteristics based on a k-mer analysis.
Launching the workflow
Hands-on: Importing and Launching a Dockstore WorkflowDockstore is a free and open source platform for sharing reusable and scalable analytical tools and workflows.
- Ensure that you are logged in to your Galaxy account.
- Go to DockStore
- Click on “Galaxy” dropdown within the “Launch with” panel located in the upper right corner.
- Select a galaxy instance you want to launch this workflow with.
- You will be redirected to Galaxy and presented with a list of workflow versions.
- Click the version you want (usually the latest labelled as “main”)
- You are done!
The following short video walks you through this uncomplicated procedure:
Hands-on: Running K-mer profile analysis workflow
Identify inputs
The profiling workflow takes the following inputs:
- HiFi reads as a collection
- K-mer length
- Ploidy
Launch k-mer profiling workflow
- Click on the Workflow menu, located in the top bar
- Click on the workflow-run Run workflow buttom corresponding to
kmer-profiling-hifi-VGP1
- In the Workflow: kmer-profiling-hifi-VGP1 menu:
- param-collection “Collection of Pacbio Data”:
HiFi data
collection- “K-mer length”:
31
- “Ploidy”:
2
- Click on the workflow-run Run workflow button
This should look like this:
Comment: K-mer lengthIn this tutorial, we are using a k-mer length of 31. This can vary, but the VGP pipeline tends to use a k-mer length of 21, which tends to work well for most mammalian-size genomes. There is more discussion about k-mer length trade-offs in the extended VGP pipeline tutorial.
Refill your coffee
Assembly is not exactly an instantaneous type of analysis - this workflow will take approximately 15 minutes to complete. The same is true for all analyses in tutorial.
Interpreting the results
Once the workflow has finished, we can evaluate the transformed linear plot generated by Genomescope, which includes valuable information such as the observed k-mer profile, fitted models, and estimated parameters. This file corresponds to the dataset 18
in this history.
This distribution is the result of the Poisson process underlying the generation of sequencing reads. As we can see, the k-mer profile follows a bimodal distribution, indicative of a diploid genome. The distribution is consistent with the theoretical diploid model (model fit > 93%). Low frequency k-mers are the result of sequencing errors, and are indicated by the red line. Genomescope2 estimated a haploid genome size of around 11.7 Mbp, a value reasonably close to the Saccharomyces genome size. We are using the transformed linear plot because it shows the ploidy better by reducing low ccoverage peaks (like k-mers containing errors) and increasing higher coverage peaks (Ranallo-Benavidez et al. 2020).
It is worth noting that the genome characteristics such as length, error percentage, etc., are based on the GenomeScope2 model, which is the black line in the plot. If the model (black line) does not fit your observed data (blue bars), then these estimated characteristics might be very off. In the case of this tutorial, the model is a good fit to our data, so we can trust the estimates.
Assembly (contiging) with hifiasm
(WF4)
To generate contiguous sequences in an assembly (contigs) we will use the hifiasm assembler. It is a part of the “Assembly with HiC (WF4)” workflow . This workflow uses hifiasm (HiC mode) to generate HiC-phased haplotypes (hap1 and hap2). This is in contrast to its default mode, which generates primary and alternate pseudohaplotype assemblies. This workflow includes three tools for evaluating assembly quality: gfastats, BUSCO and Merqury.
Launching the workflow
Hands-on: Importing and Launching a Dockstore WorkflowDockstore is a free and open source platform for sharing reusable and scalable analytical tools and workflows.
- Ensure that you are logged in to your Galaxy account.
- Go to DockStore
- Click on “Galaxy” dropdown within the “Launch with” panel located in the upper right corner.
- Select a galaxy instance you want to launch this workflow with.
- You will be redirected to Galaxy and presented with a list of workflow versions.
- Click the version you want (usually the latest labelled as “main”)
- You are done!
The following short video walks you through this uncomplicated procedure:
Hands-on: Launching assembly (contiging) workflow
Identify inputs
The assembly workflow takes the following inputs:
- HiFi reads as a collection
- Forward Hi-C reads
- Reverse Hi-C reads
Genomescope
Model Parameters generated by previous (k-mer profiling) workflowGenomescope
Summary generated by previous (k-mer profiling) workflow- Meryl k-mer database generated by previous (k-mer profiling) workflow
- Busco Database
- Busco lineage
Launch the workflow
- Click on the Workflow menu, located in the top bar
- Click on the workflow-run Run workflow button corresponding to
Assembly-Hifi-HiC-phasing-VGP4
- In the Workflow: Assembly-Hifi-HiC-phasing-VGP4 menu fill the following parameters:
- param-collection “Pacbio Reads Collection”:
HiFi data
collection- param-file “HiC forward reads”:
Hi-C forward reads
- param-file “HiC reverse reads”:
Hi-C reverse reads
- param-file “GenomeScope Summary”:
GenomeScope on data X Summary
(contains tag “GenomeScopeSummary
”)- param-file “Meryl database”:
Meryl on data X: read-db.mertyldb
: the Meryl k-mer database (contains tag “MerylDatabase
”)- “Database for Busco Lineage”:
Busco v5 Lineage Datasets
(or the latest version available in the instance)- “Provide lineage for BUSCO (e.g., Vertebrata)”:
Ascomycota
- param-file “GenomeScope Model Parameters”:
GenomeScope on data X Model parameters
(contains tag “GenomeScopeParameters
”)- Click on the workflow-run Run workflow button
Comment: A note about "Homozygous Read Coverage"Hifiasm tries to estimate the homozygous read coverage, but sometimes it can mistake a different peak as the homozygous coverage peak. To get around this, the VGP workflows calculate the homozygous coverage from the haploid coverage estimate from the GenomeScope outputs generated in the k-mer profiling workflow. However, sometimes GenomeScope can also misidentify the haploid peak, leading to it feeding an incorrect homozygous read coverage value into hifiasm. If this is the case, you can run the hifiasm workflows but specify the homozygous read coverage yourself. Otherwise, this parameter is fine to leave blank to let the workflows try to get it right on their own.
Interpreting the results
Warning: There will be two assemblies!Because we are running hifiasm in HiC-phasing mode, it will produce two assemblies: hap1 and hap2!
Let’s have a look at the stats generated by gfastats. This output summarizes some main assembly statistics, such as contig number, N50, assembly length, etc. The workflow provide a joined table to display the statistics for both haplotype assemblies side-by-side. Below we provide a partial output of this file called Assembly statistics for Hap1 and Hap2
in your history:
Statistic Hap 1 Hap 2 # contigs 16 17 Total contig length 11,304,507 12,160,985 Average contig length 706,531.69 715,352.06 Contig N50 922,430 923,452 Contig N50 922,430 923,452 Contig auN 895,018.82 904,515.40 Contig L50 5 6 Contig L50 5 6 Contig NG50 813,311 923,452 Contig NG50 813,311 923,452 Contig auNG 861,278.90 936,364.06 Contig LG50 6 6 Contig LG50 6 6 Largest contig 1,532,843 1,531,728 Smallest contig 185,154 85,850
According to the report, both assemblies are quite similar; the hap1 assembly includes 16 contigs, whose cumulative length is around 11.3 Mbp. The hap2 assembly includes 17 contigs, whose total length is 12.1Mbp. Both assemblies come close to the estimated genome size, which is as expected since we used hifiasm-HiC mode to generate phased assemblies, and this lowers the chance of false duplications that can inflate assembly size.
Comment: Are you working with pri/alt assemblies?This tutorial uses the hifiasm-HiC workflow, which generates phased hap1 and hap2 assemblies. The phasing helps lower the chance of false duplications, since the phasing information helps the assembler know which genomic variation is heterozygosity at the same locus versus being two different loci entirely. If you are working with primary/alternate assemblies (especially if there is no internal purging in the initial assembly), you can expect higher false duplication rates than we observe here with the yeast HiC hap1/hap2.
Question
- What is the longest contig in the hap1 assembly? And in the hap2 one?
- What is the N50 of the hap2 assembly?
- The longest contig in the hap1 assembly is 1,532,843 bp, and 1,531,728 bp in the hap2 assembly.
- The N50 of the hap2 assembly is 923,452 bp.
Next, we are going to evaluate the outputs generated by BUSCO. This tool provides qualitative assessment of the completeness of a genome assembly in terms of expected gene content. It relies on the analysis of genes that should be present only once in a complete assembly or gene set, while allowing for rare gene duplications or losses (Simão et al. 2015).
As we can see in the report, the results are simplified into four categories: complete and single-copy, complete and duplicated, fragmented and missing.
Question
- How many complete BUSCO genes have been identified in the hap1 assembly?
- How many BUSCOs genes are absent in the hap1 assembly?
- According to the report, our assembly contains the 1,436 complete BUSCO genes – this includes ones that are single-copy and ones that are duplicated.
- 208 BUSCO genes are missing.
Despite BUSCO being robust for species that have been widely studied, it can be inaccurate when the newly assembled genome belongs to a taxonomic group that is not well represented in OrthoDB. Merqury
provides a complementary approach for assessing assembly quality in a reference-free manner via k-mer copy number analysis. Specifically, it takes our hap1 as the first genome assembly, hap2 as the second genome assembly, and the merylDB generated previously from our sequencing reads for k-mer counts.
By default, Merqury
generates three collections as output: stats (completeness stats), plots and assembly consensus quality (QV) stats. Let’s first have a look at the copy number (CN) spectrum plot, known as the spectra-cn plot. The spectra-cn plot looks at both of your assemblies (here, your two haplotypes) taken together (fig. 6a).
Our haplotypes look clean! In the spectra-cn plot for both haplotypes, the peaks are where we should expect them. There is a peak of k-mers present at 1-copy (so, only in either hap1 or hap2) with k-mer multiplicity of ~25, corresponding to heterozygous coverage. These are our heterozygous alleles being represented only once in our diplolid assemblies, which matches with their haploid coverage. We also have a peak of 2-copy k-mers present at diploid coverage, around ~50. This looks good so far, but we should also look at k-mer multiplicity for each individual haplotype separately, not just combined as they were in the previous plot. Figure 6b shows that most of the k-mers in our assembly are 1-copy – we have one copy of all the homozygous regions (the ones with 50x coverage) and of about half the heterozygous regions (the ones with 25x coverage). At the 25x coverage point, there is a “read-only” peak, which is expected, as these are the alternative alleles for those heterozygous loci, and those k-mers are likely in the other assembly, since they were not missing from the overall spectra-cn plot.
Hi-C scaffolding (WF8)
In this final stage, we will run the Scaffolding HiC YAHS (WF8) workflow, which uses the fact that the contact frequency between a pair of loci strongly correlates with the one-dimensional distance between them (i.e., linear distance on a chromosome). This information allows YAHS – the main tool in this workflow – to generate scaffolds that are often chromosome-sized.
Launching Hi-C scaffolding workflow
Warning: The scaffolding workflow is run on ONE haplotype at a time.Contiging (VGP4) works with both (hap1/hap2, primary/alternate) assemblies simultaneously. This is not the case for contiging – it has to be run independently for each haplotype assembly. In this example (below) we run contiging on the hap1 assembly only. You can run the same analysis on the second haplotype by replacing hap1 with hap2.
Hands-on: Importing and Launching a Dockstore WorkflowDockstore is a free and open source platform for sharing reusable and scalable analytical tools and workflows.
- Ensure that you are logged in to your Galaxy account.
- Go to DockStore
- Click on “Galaxy” dropdown within the “Launch with” panel located in the upper right corner.
- Select a galaxy instance you want to launch this workflow with.
- You will be redirected to Galaxy and presented with a list of workflow versions.
- Click the version you want (usually the latest labelled as “main”)
- You are done!
The following short video walks you through this uncomplicated procedure:
Hands-on: Launching Hi-C scaffolding workflow
Identify inputs
The scaffolding workflow takes the following inputs:
- An assembly graph
- Forward Hi-C reads
- Reverse Hi-C reads
- Estimated genome size parsed from GenomeScope summary by the previous run of assembly workflow (VGP4).
- Restriction enzymes used in Hi-C library preparation procedure
- Busco Database
- Busco lineage
Launch scaffolding workflow (WF8)
- Click on the Workflow menu, located in the top bar
- Click on the workflow-run Run workflow button corresponding to
Scaffolding-HiC-VGP8
- In the Workflow: Scaffolding-HiC-VGP8 menu:
- param-file “input GFA”:
usable hap1 gfa
Output of the contiging workflow (VGP4) with a taghic_hap1_gfa
for hap1 assembly.- “Database for Busco Lineage”:
Busco v5 Lineage Datasets
(or the latest version available in the instance)- “Provide lineage for BUSCO (e.g., Vertebrata)”:
Ascomycota
- param-file “HiC forward reads”:
Hi-C forward reads
- param-file “HiC reverse reads”:
Hi-C reverse reads
- “Restriction enzymes”:
Dovetail Omni-C: enzyme-free prep
For this tutorial, we’ll use the Omni-C option as it is the equivalent of not specifying any restriction enzyme cutsites, but for your own data you would want to select the appropriate option.- param-file “Estimated genome size - Parameter File”:
Estimated genome size
: An output of the contiging workflow (VGP4) with a tagestimated_genome_size
.- Click on the workflow-run Run workflow button
Interpreting the results
In order to evaluate the Hi-C hybrid scaffolding, we are going to compare the contact maps before and after running the HiC hybrid scaffolding workflow (Fig. below). They will have the following tags:
- Before scaffolding:
pretext_s1
- After scaffolding:
pretext_s2
Below is the comparison of the two maps obtained from the yeast tutorial data versus real example from a zebra finch (Taeniopygia guttata) genome assembly:
The regions marked with red circles highlight the most notable difference between the two contact maps, where inversion has been fixed.
Conclusion
To sum up, it is worthwhile to compare the final assembly with the S. cerevisiae S288C reference genome:
With respect to the total sequence length, we can conclude that the size of our genome assembly is very similar to the reference genome. It is noteworthy that the reference genome consists of 17 sequences, while our assembly includes only 16 chromosomes. This is due to the fact that the reference genome also includes the sequence of the mitochondrial DNA, which consists of 85,779 bp. (The above comparison is performed using Quast ( Galaxy version 5.2.0+galaxy1) using Primary assembly generated with scaffolding workflow (WF8) and yeast reference.)
If we compare the contact map of our assembled genome with the reference assembly (Fig. above), we can see that the two are indistinguishable, suggesting that we have generated a chromosome level genome assembly.