4. Genome assembly¶
In this section we will use our skill on the command-line interface to create a genome assembly from sequencing data.
You will encounter some To-do sections at times. Write the solutions and answers into a text-file.
4.3. Learning outcomes¶
After studying this tutorial you should be able to:
- Compute and interpret a whole genome assembly.
- Judge the quality of a genome assembly.
4.4. Before we start¶
Lets see how our directory structure looks so far:
cd ~/analysis ls -1F
data/ SolexaQA/ SolexaQA++ trimmed/ trimmed-fastqc/ trimmed-solexaqa/
4.5. Creating a genome assembly¶
We want to create a genome assembly for our ancestor. We are going to use the quality trimmed forward and backward DNA sequences and use a program called SPAdes to build a genome assembly.
- Discuss briefly why we are using the ancestral sequences to create a reference genome as opposed to the evolved line.
4.5.1. Installing the software¶
We are going to use a program called SPAdes fo assembling our genome. In a recent evaluation of assembly software, SPAdes was found to be a good choice for fungal genomes [ABBAS2014]. It is also simple to install and use.
conda activate ngs conda install spades
4.5.2. SPAdes usage¶
# change to your analysis root folder cd ~/analysis # first create a output directory for the assemblies mkdir assembly # to get a help for spades and an overview of the parameter type: spades.py -h
The two files we need to submit to SPAdes are two paired-end read files.
spades.py -o assembly/spades-default/ -1 trimmed/ancestor-R1.fastq.trimmed.gz -2 trimmed/ancestor-R2.fastq.trimmed.gz
- Run SPAdes with default parameters on the ancestor
- Read in the SPAdes manual about about assembling with 2x150bp reads
- Run SPAdes a second time but use the options suggested at the SPAdes manual section 3.4 for assembling 2x150bp paired-end reads (are fungi multicellular?). Use a different output directory
assembly/spades-150for this run.
Should you not get it right, try the commands in Code: SPAdes assembly (trimmed data).
4.6. Assembly quality assessment¶
4.6.1. Assembly statistics¶
- N50: length for which the collection of all contigs of that length or longer covers at least 50% of assembly length
- NG50: where length of the reference genome is being covered
- NA50 and NGA50: where aligned blocks instead of contigs are taken
- missassemblies: misassembled and unaligned contigs or contigs bases
- genes and operons covered
conda create -n quast python=2 quast conda activate quast
Run Quast with both assembly scaffolds.fasta files to compare the results.
quast -o assembly/quast assembly/spades-default/scaffolds.fasta assembly/spades-150/scaffolds.fasta
- Compare the results of Quast with regards to the two different assemblies.
- Which one do you prefer and why?
4.7. Compare the untrimmed data¶
- To see if our trimming procedure has an influence on our assembly, run the same command you used on the trimmed data on the original untrimmed data.
- Run Quast on the assembly and compare the statistics to the one derived for the trimmed data set. Write down your observations.
Should you not get it right, try the commands in Code: SPAdes assembly (original data).
Now that you know the basics for assembling a genome and judging their quality, play with the SPAdes parameters and the trimmed data to create the best assembly possible. We will compare the assemblies to find out who created the best one.
- Once you have your final assembly, rename your assembly directory int
mv assembly/spades-default assembly/spades-final.
- Write down in your notes the command used to create your final assembly.
- Write down in your notes the assembly statistics derived through Quast