MAG Generation

Author

Affiliation

Tatiana Gurbich

EMBL-EBI

MAG generation

• Generation of metagenome assembled genomes (MAGs) from assemblies
• Assessment of quality
• Taxonomic assignment

Prerequisites

For this tutorial, you will need to first start the docker container by running:

sudo docker run --rm -it -v /home/training/Binning:/opt/data microbiomeinformatics/mgnify-ebi-2020-binning

password: training

Generating metagenome assembled genomes

Note

Learning Objectives - in the following exercises, you will learn how to bin an assembly, assess the quality of this assembly with checkM, and then visualize a placement of these genomes within a reference tree.

Note

As with the assembly process, there are many software tools available for binning metagenome assemblies. Examples include, but are not limited to:

• MaxBin
• CONCOCT
• COCACOLA
• MetaBAT

There is no clear winner between these tools, so it is best to experiment and compare a few different ones to determine which works best for your dataset. For this exercise, we will be using MetaBAT (specifically, MetaBAT2). The way in which MetaBAT bins contigs together is summarized in Figure 1.

Figure 1. MetaBAT workflow (Kang, et al. PeerJ 2015).

Preparing to run MetaBAT

Step

Prior to running MetaBAT, we need to generate coverage statistics by mapping reads to the contigs. To do this, we can use bwa and then the samtools software to reformat the output. This can take some time, so we have run it in advance.

Step

Let’s browse the files that we have prepared:

cd /opt/data/assemblies/
ls

You should find the following files in this directory:

• contigs.fasta: a file containing the primary metagenome assembly produced by metaSPAdes (contigs that haven’t been binned)
• input.fastq.sam.bam: a pre-generated file that contains reads mapped back to contigs

Tip

If you wanted to generate the input.fastq.sam.bam file yourself, you would run the following commands:

# NOTE: you will not be able to run subsequent steps until this workflow is completed because you need
# the input.fastq.sam.bam file to calculate contig depth in the next step.

# If you would like to practice this now, back up the input.fastq.sam.bam file that we provided first,
# as these steps will take a while:

cd /opt/data/assemblies/
mv input.fastq.sam.bam input.fastq.sam.bam.bak

# index the contigs file that was produced by metaSPAdes:
bwa index contigs.fasta

# fetch the reads from ENA
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR011/ERR011322/ERR011322_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR011/ERR011322/ERR011322_2.fastq.gz

# map the original reads to the contigs:
bwa mem contigs.fasta ERR011322_1.fastq ERR011322_2.fastq > input.fastq.sam

# reformat the file with samtools:
samtools view -Sbu input.fastq.sam > junk 
samtools sort junk -o input.fastq.sam.bam

Running MetaBAT

Create a subdirectory where files will be saved to

cd /opt/data/assemblies/
mkdir contigs.fasta.metabat-bins2000

In this case, the directory might already be part of your VM, so do not worry if you get an error saying the directory already exists. You can move on to the next step.

Step

Run the following command to produce a contigs.fasta.depth.txt file, summarizing the output depth for use with MetaBAT:

jgi_summarize_bam_contig_depths --outputDepth contigs.fasta.depth.txt input.fastq.sam.bam

Step

Now let’s put together the metaBAT2 command. To see the available options, run:

metabat2 -h

Here is what we are trying to do:

• we want to bin the assembly file called contigs.fasta

• the resulting bins should be saved into the contigs.fasta.metabat-bins2000 folder

• we want the bin file names to start with the prefix bin

• we want to use the contig depth file we just generated (contigs.fasta.depth.txt)

• the minimum contig length should be 2000

Take a moment to put your command together but please check the answer below before running it to make sure everything is correct.

See the answer

metabat2 --inFile contigs.fasta --outFile contigs.fasta.metabat-bins2000/bin --abdFile contigs.fasta.depth.txt --minContig 2000

Note

Once the binning process is complete, each bin will be grouped into a multi-fasta file with a name structure of bin.[0-9].fa.

Step

Inspect the output of the binning process.

ls contigs.fasta.metabat-bins2000/bin*

Question

How many bins did the process produce?

Question

How many sequences are in each bin?

Obviously, not all bins will have the same level of accuracy since some might represent a very small fraction of a potential species present in your dataset. To further assess the quality of the bins, we will use CheckM.

Running CheckM

Note

CheckM has its own reference database of single-copy marker genes. Essentially, based on the proportion of these markers detected in the bin, the number of copies of each, and how different they are, it will determine the level of completeness, contamination, and strain heterogeneity of the predicted genome.

Step

Before we start, we need to configure CheckM.

cd /opt/data
mkdir /opt/data/checkm_data
tar -xf checkm_data_2015_01_16.tar.gz -C /opt/data/checkm_data
checkm data setRoot /opt/data/checkm_data

This program has some handy tools not only for quality control but also for taxonomic classification, assessing coverage, building a phylogenetic tree, etc. The most relevant ones for this exercise are wrapped into the lineage_wf workflow.

Step

STOP!!!! The following command fails. Please use backups instead, inspect the command visually only:

cd /opt/data/assemblies
# INSPECT BUT DO NOT RUN!
checkm lineage_wf -x fa contigs.fasta.metabat-bins2000 checkm_output --tab_table -f MAGs_checkm.tab --reduced_tree -t 4

Due to memory constraints (< 40 GB), we have added the option --reduced_tree to build the phylogeny with a reduced number of reference genomes.

Once the lineage_wf analysis is done, the reference tree can be found in checkm_output/storage/tree/concatenated.tre.

Additionally, you will have the taxonomic assignment and quality assessment of each bin in the file MAGs_checkm.tab with the corresponding level of completeness, contamination, and strain heterogeneity (Fig. 2). A quick way to infer the overall quality of the bin is to calculate the level of **(completeness - 5*contamination). You should be aiming for an overall score of at least 70-80%**.

You can inspect the CheckM output with:

cat MAGs_checkm.tab

Example output of CheckM

Before we can visualize and plot the tree, we will need to convert the reference ID names used by CheckM to taxon names. We have already prepared a mapping file for renaming the tree (rename_list.tab). We can then do this easily with the newick utilities.

Step

To do this, run the following command:

cd /opt/data/
nw_rename assemblies/checkm_output/storage/tree/concatenated.tre assemblies/rename_list.tab > renamed.tree

Visualizing the phylogenetic tree

We will now plot and visualize the tree we have produced. A quick and user-friendly way to do this is to use the web-based interactive Tree of Life iTOL

iTOL only takes in newick formatted trees, so we need to quickly reformat the tree with FigTree.

Step

In order to open FigTree, open a new terminal window (without docker) and type figtree

Step

Open the renamed.tree file with FigTree (File -> Open) (file is in /home/training/Binning) and then select from the toolbar File -> Export Trees. In the Tree file format select Newick and export the file as renamed.nwk (or choose a name you will recognise if you plan to use the shared account described below).

Step

To use iTOL you will need a user account. For the purpose of this tutorial we have already created one for you with an example tree.

Go to the iTOL website (open the link in a new window).

The login is as follows:

User: EBI_training

Password: EBI_training

After you login, just click on My Trees in the toolbar at the top and select

IBD_checkm.nwk from the Imported trees workspace.

Alternatively, if you want to create your own account and plot the tree yourself follow these steps:

1. After you have created and logged in to your account go to My Trees
2. From there select Upload tree files and upload the tree you exported from FigTree
3. Once uploaded, click the tree name to visualize the plot
4. To colour the clades and the outside circle according to the phylum of each strain, drag and drop the files iTOL_clades.txt and iTOL_ocircles.txt present in /home/training/Data/Binning/iTOL_Files/ into the browser window

Once that is done, all the reference genomes used by CheckM will be coloured according to their phylum name, while all the other ones left blank correspond to the target genomes we placed in the tree. Highlighting each tip of the phylogeny will let you see the whole taxon/sample name. Feel free to play around with the plot.

To find the bins you generated, click on the search icon in the left-hand side menu (magnifying glass with the letters “Aa” in it). In the search field type bin.. Click on each bin name to see it in the tree.

Question

Does the CheckM taxonomic classification make sense? Were you able to find all of the bins in the tree? If not, why do you think that is?

Reuse