An Introduction to Genome Assembly
OverviewQuestions:Objectives:
How do we perform a very basic genome assembly from short read data?
Requirements:
assemble some paired end reads using Velvet
examine the output of the assembly.
- Introduction to Galaxy Analyses
- Sequence analysis
- Quality Control: slides slides - tutorial hands-on
Time estimation: 30 minutesLevel: Introductory IntroductorySupporting Materials:Last modification: Oct 18, 2022
Genome assembly with Velvet: Background
Velvet is one of a number of de novo assemblers that use short read sets as input (e.g. Illumina Reads). The assembly method is based on the manipulation of de Bruijn graphs, via the removal of errors and the simplification of repeated regions.
CommentFor information about Velvet, you can check its (nice) Wikipedia page.
For this tutorial, we have a set of reads from an imaginary Staphylococcus aureus bacterium with a miniature genome (197,394 bp). Our mutant strain read set was sequenced with the whole genome shotgun method, using an Illumina DNA sequencing instrument. From these reads, we would like to rebuild our imaginary Staphylococcus aureus bacterium via a de novo assembly of a short read set using the Velvet assembler.
We also have a sequence for a reference genome that we will use later in the tutorial.
AgendaIn this tutorial, we will deal with:
Get the data
Hands-on: Getting the data
Create and name a new history for this tutorial.
Import from Zenodo or from the data library the files:
https://zenodo.org/record/582600/files/mutant_R1.fastq https://zenodo.org/record/582600/files/mutant_R2.fastq https://zenodo.org/record/582600/files/wildtype.fna
Change the name of the files to
mutant_R1
,mutant_R2
andwildtype.fna
.As a default, Galaxy uses the link as the name of the new dataset. It also does not link the dataset to a database or a reference genome.
Inspect the content of a file.
Question
- What are four key features of a FASTQ file?
- What is the main difference between a FASTQ and a FASTA file?
The reads have been sequenced from an imaginary Staphylococcus aureus bacterium using an Illumina DNA sequencing instrument. We obtained the 2 files we imported (mutant_R1
and mutant_R2
)
QuestionWhy do we have 2 files here if we only sequenced the bacteria once?
Evaluate the input reads
Before doing any assembly, the first questions you should ask about your input reads include:
- What is the coverage of my genome?
- How good is my read set?
- Do I need to ask for a new sequencing run?
- Is it suitable for the analysis I need to do?
We will evaluate the input reads using the FastQC tool. This tool runs a standard series of tests on your read set and returns a relatively easy-to-interpret report. We will use it to evaluate the quality of our FASTQ files and combine the results with MultiQC.
Hands-on: FastQC on a fastq file
- FastQC tool with the following parameters
- param-files “Raw read data from your current history”:
mutant_R1.fastq
andmutant_R2.fastq
- MultiQC tool with the following parameters
- “Software name”:
FastQC
- “Result file”: All raw data files generated by FastQC.
MultiQC generates a webpage combining reports for FastQC on both datasets. It includes these graphs and tables:
-
General statistics
This is important in setting maximum k-mer size for an assembly.
Comment: Getting the length of sequences- Click on Configure Columns
- Check Length
- Close the window
Question- How long are the sequences?
- What is the average coverage of the genome, given our imaginary Staphylococcus aureus bacterium has a genome of 197,394 bp?
-
Sequence Quality Histograms
Dips in quality near the beginning, middle or end of the reads may determine the trimming/cleanup methods and parameters to be used, or may indicate technical problems with the sequencing process/machine run.
Figure 1: The mean quality value across each base position in the read Question- What does the y-axis represent?
- Why is the quality score decreasing across the length of the reads?
-
Per Sequence GC Content
High GC organisms tend not to assemble well and may have an uneven read coverage distribution.
-
Per Base N Content
The presence of large numbers of Ns in reads may point to a poor quality sequencing run. You will need to trim these reads to remove Ns.
-
k-mer content
The presence of highly recurring k-mers may point to contamination of reads with barcodes or adapter sequences.
CommentFor a fuller discussion of FastQC outputs and warnings, see the FastQC website link, including the section on each of the output reports, and examples of “good” and “bad” Illumina data.
We won’t be doing anything to these data to clean it up as there isn’t much need. Therefore we will get on with the assembly!
Assemble reads with Velvet
Now, we want to assemble our reads to find the sequence of our imaginary Staphylococcus aureus bacterium. We will perform a de novo assembly of the reads into long contiguous sequences using the Velvet short read assembler.
The first step of the assembler is to build a de Bruijn graph. For that, it will break our reads into k-mers, i.e. fragments of length k. Velvet requires the user to input a value of k (k-mer size) for the assembly process. Small k-mers will give greater connectivity, but large k-mers will give better specificity.
Hands-on: Assemble the reads
- FASTQ interlacer tool with the following parameters
- “Type of paired-end datasets”:
2 separate datasets
- “Left-hand mates”:
mutant_R1.fastq
- “Right-hand mates”:
mutant_R2.fastq
Currently our paired-end reads are in 2 files (one with the forward reads and one with the reverse reads), but Velvet requires only one file, where each read is next to its mate read. In other words, if the reads are indexed from 0, then reads 0 and 1 are paired, 2 and 3, 4 and 5, etc. Before doing the assembly per se, we need to prepare the files by combining them.
- velveth tool with the following parameters
- “Hash Length”:
29
- “Input Files”
- Click on param-repeat “Input Files”
- In “1: Input Files”
- “file format”:
fastq
- “read type”:
shortPaired reads
- param-files “Dataset”: outputs of FASTQ interlacer
The tool takes our reads and break them into k-mers.
- velvetg tool with the following parameters
- param-files “Velvet Dataset”: outputs of velveth
- “Using Paired Reads”:
Yes
This last tool actually does the assembly.
Two files are generated:
-
A Contigs file
This file contains the sequences of the contigs longer than 2k. In the header of each contig, a bit of information is added:
- the k-mer length (called “length”): For the value of k chosen in the assembly, a measure of how many k-mers overlap (by 1 bp each overlap) to give this length
- the k-mer coverage (called “coverage”): For the value of k chosen in the assembly, a measure of how many k-mers overlap each base position (in the assembly).
-
A Stats file
This is a tabular file giving for each contig the k-mer lengths, k-mer coverages and other measures.
Collect some statistics on the contigs
Question
- How many contigs have been built?
- What is the mean, min and max length of the contigs?
This table is limitted, but we will now collect more basic statistics on our assembly.
Hands-on: Collect fasta statistics on our contigs
- Quast tool with
- “Contigs/scaffolds output file”: outputs of velvetg
- “Type of assembly”:
Genome
- “Use a reference genome?”:
Yes
- “Reference genome”:
wildtype.fna
- “Type of organism”:
Prokaryotes
- “Lower Threshold”:
500
- “Comma-separated list of contig length thresholds”:
0,1000
This tool generates 5 output files, but we will focus on the HTML report and the Icarus viewer.
Question
- What is represented in the Icarus viewer?
The HTML report reports many statistics computed by QUAST to assess the quality of the assembly:
- Statistics about the quality of the assembly when compared to the reference (fraction of the genome, duplication ratio, etc)
-
Misassembly statistics, including the number of misassemblies
A misassembly is a position in the contigs (breakpoints) that satisfy one of the following criteria:
- the left flanking sequence aligns over 1 kbp away from the right flanking sequence on the reference;
- flanking sequences overlap on more than 1 kbp
- flanking sequences align to different strands or different chromosomes
- Unaligned regions in the assembly
- Mismatches compared to the reference genomes
- Statistics about the assembly per se, such as the number of contigs and the length of the largest contig
Question
- How many contigs have been constructed?
- Which proportion of the reference genome do they represent?
- How many misassemblies have been found?
- Has the assembly introduced mismatches and indels?
- What are N50 and L50?
- Is there a bias in GC percentage induced by the assembly?
Discussion
Hands-on: (Optional) Rerun for values k ranging from 31 to 101
- velveth tool with the same parameters as before except
- “Hash Length”: a value between 31 and 101
- velvetg tool with the same parameters as before
- Quast tool with the same parameters as before
We have completed an assembly on this data set for a number of k values ranging from 29 to 101. A few of the assembly metrics appear below.




Question
- Are there any distinct features in the charts?
- Does it look like one assembly might be better than some of the others?
The reasons for these patterns will be discussed in detail in the De Bruijn graph assembly slides and tutorial.
Key points
We assembled some Illumina fastq reads into contigs using a short read assembler called Velvet
We showed what effect one of the key assembly parameters, the k-mer size, has on the assembly
It looks as though there are some exploitable patterns in the metric data vs the k-mer size.
Frequently Asked Questions
Have questions about this tutorial? Check out the tutorial FAQ page or the FAQ page for the Assembly topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help ForumFeedback
Did you use this material as an instructor? Feel free to give us feedback on how it went.
Did you use this material as a learner or student? Click the form below to leave feedback.
Citing this Tutorial
- Simon Gladman, An Introduction to Genome Assembly (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/assembly/tutorials/general-introduction/tutorial.html Online; accessed Thu Jul 24 2025
- Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012
Congratulations on successfully completing this tutorial!