Connect with us

Software

How to create a pangenome of isolated genome sequences using Roary and Prokka?

Published

on

Roary is a pangenome genome pipeline, which calculates pangenome of a set of related prokaryotic isolates [1]. It takes annotated assemblies in the gff3 format generated by Prokka [2] and provides the pangenome. The working methodology has been explained in our previous article. In this article, we will learn how to create the pangenome of a few isolated genome sequences using Roary [1] and Prokka [2].

Input for Roary

  1. Genome sequences in the form of gff3 files.

Downloading the genome sequences

At first, you need to download genome sequences as per your need, which you can easily download yourself or by using the ncbi-genome-download package. It provides several scripts to download genome sequences from NCBI FTP servers. To install this package, open a terminal (Ctrl + T) and type the following commands:

$ pip install ncbi-genome-download

After downloading this package, you can download the genome assemblies as per your requirements such as fasta sequences of all bacteria, viral genome, RefSeq genome sequences in GenBank format, fungal genomes and so on. (Remember, while downloading gff3 files, you need to download Genbank files with the nucleotide sequence because gff3 files on the NCBI website contain annotation only). I will download all bacterial sequences in fasta format using the following command (showing this example with only a few sequences only):

$ ncbi-genome-download --format fasta bacteria

Annotating the genome sequences

Go into the directory of Roary, create a new folder, let’s name it as ‘example’, and save those downloaded sequences. After downloading, you will see many fasta files in the same folder. Now start annotating them to determine the attributes and location of the genes present in them, and also to obtain gff3 files which are used as an input in roary. This can be easily done with Prokka [2]. Open the terminal and type the following commands:

$ cd Downloads/Roary/example/

$ prokka --kingdom Bacteria --outdir prokka_GCA_000006285 --genus Salmonella --locustag GCA_000006285 GCA_000006285.2_ASM828v3_genomic.fna

You can further add other descriptions such as organism details (genus, species, etc.). Make sure you annotate all the genome sequences you are dealing with and remember to change the output directory name, locus tag, and assembly version accordingly. After running this command, a new directory will be created in the name of each sequence and will consist of 12 files with different extensions including the gff3 file.

Creating pangenome/Running Roary

We have got gff3 files of the genome sequences in the directories, now we need to copy the gff3 file from each directory into another directory (let’s say, gff_all). After that, open the terminal again and type the following command to run roary:

$ roary -f ./tutorial -e -n -v ./gff_all/*.gff

At this stage, Roary will get all the coding sequences, translate them into protein sequences, and generate pre-clusters. After that, roary will look for the paralogs using blastp [3] and create clusters using MCL [4]. Finally, it will take every isolate and order them according to the presence/absence of orthologs. This will take time depending upon the number of sequences (or gff3 files) you are using.

If you want to create a pangenome without the core alignment, then use the following command:

$ roary -f ./tutorial -v ./gff_all/*.gff

If you want to change the percentage identity of blastp (not advised to go below 90%), then use the following command:

$ roary -f ./tutorial -i 90 -v ./gff_all/*.gff

These commands will result in a new directory called tutorial (as given name in the command), where all result files will be found. You can see the summary statistics in the file named ‘summary_statistics.txt‘, it will look like this:

                  summary_statistics.txt

Core genes (99% <= strains <= 100%) 2031

Softcore genes (95% <= strains < 99%) 0

Shell genes (15% <= strains < 95%) 2497

Cloud genes (0% <= strains < 15%) 0

Total genes (0% <= strains <= 100%) 4528

Visualizing results

Similarly, you will find some other output files such as ‘gene_presence_absence.csv‘, ‘accessory_binary_genes.fa.newick‘. ‘roary_plots.py’ script (written by Marco Galardini) will be used to visualize the results, which is present inside the directory named contrib in the main roary directory. Open the terminal, go into the tutorial directory (where all the result files are present) and type the following:

$ cd tutorial
$ /home/user/Downloads/roary/contrib/roary_plots/roary_plots.py accessory_binary_genes.fa.newick gene_presence_absence.csv

You will see three png files that will be added in the same tutorial directory: pangenome_frequence.png (Fig. 1), pangenome_matrix.png (Fig. 2), and pangenome_pie.png (Fig. 3) as shown below.

Fig. 1 showing the number of genes present in each genome sequence.

Fig. 2 Gene clusters.

Fig. 3 represents a pie chart showing different genes present in the genome sequences.

Additionally, you can also visualize the Newick file in phylogeny software such as Mega for further analysis.

This article demonstrated the creation of a pangenome of isolated genome sequences using roary. In case of any queries, please write to us at [email protected] or [email protected].

References

  1. Page, A. J., Cummins, C. A., Hunt, M., Wong, V. K., Reuter, S., Holden, M. T., … & Parkhill, J. (2015). Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics31(22), 3691-3693.
  2. Seemann, T. (2014). Prokka: rapid prokaryotic genome annotation. Bioinformatics30(14), 2068-2069.
  3. Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. (1990) “Basic local alignment search tool.” J. Mol. Biol. 215:403-410.
  4. Dongen S van. Graph Clustering by Flow Simulation. University of Utrecht; 2000.

Tariq is founder of Bioinformatics Review and CEO at IQL Technologies. His areas of expertise include algorithm design, phylogenetics, MicroArray, Plant Systematics, and genome data analysis. If you have questions, reach out to him via his homepage.

Advertisement
Click to comment

You must be logged in to post a comment Login

Leave a Reply

Bioinformatics Programming

Free_Energy_Landscape-MD: Python package to create Free Energy Landscape using PCA from GROMACS.

Dr. Muniba Faiza

Published

on

In molecular dynamics (MD) simulations, a free energy landscape (FEL) serves as a crucial tool for understanding the behavior of molecules and biomolecules over time. It is difficult to understand and plot a meaningful FEL and then extract the time frames at which the plot shows minima. In this article, we introduce a new Python package (Free_Energy_Landscape-MD) to generate an FEL based on principal component analysis (PCA) from MD simulation done by GROMACS [1].

(more…)

Continue Reading

Bioinformatics News

VS_Analysis: A Python package to perform post-virtual screening analysis

Dr. Muniba Faiza

Published

on

VS_Analysis: A Python package to perform post-virtual screening analysis

Virtual screening (VS) is a crucial aspect of bioinformatics. As you may already know, there are various tools available for this purpose, including both paid and freely accessible options such as Autodock Vina. Conducting virtual screening with Autodock Vina requires less effort than analyzing its results. However, the analysis process can be challenging due to the large number of output files generated. To address this, we offer a comprehensive Python package designed to automate the analysis of virtual screening results.

(more…)

Continue Reading

Bioinformatics Programming

vs_interaction_analysis.py: Python script to perform post-virtual screening analysis

Dr. Muniba Faiza

Published

on

vs_interaction_analysis.py: Python script to perform post-virtual screening analysis

Analyzing the results of virtual screening (VS) performed with Autodock Vina [1] can be challenging when done manually. In earlier instances, we supplied two scripts, namely vs_analysis.py [2,3] and vs_analysis_compounds.py [4]. This time, we have developed a new Python script to simplify the analysis of VS results.

(more…)

Continue Reading

Software

How to install Interactive Genome Viewer (IGV) & tools on Ubuntu?

Dr. Muniba Faiza

Published

on

How to install Interactive Genome Viewer (IGV) & tools on Ubuntu?

Interactive Genome Viewer (IGV) is an interactive tool to visualize genomic data [1]. In this article, we are installing IGV and tools on Ubuntu desktop.

(more…)

Continue Reading

MD Simulation

[Tutorial] Installing VIAMD on Ubuntu (Linux).

Dr. Muniba Faiza

Published

on

[Tutorial] Installing VIAMD on Ubuntu (Linux).

Visual Interactive Analysis of Molecular Dynamics (VIAMD) is a tool that allows the interactive analysis of molecular dynamics simulations [1]. In this article, we are installing it on Ubuntu (Linux).

(more…)

Continue Reading

Docking

[Tutorial] Performing docking using DockingPie plugin in PyMOL.

Dr. Muniba Faiza

Published

on

[Tutorial] Performing docking using DockingPie plugin in PyMOL.

DockingPie [1] is a PyMOL plugin to perform computational docking within PyMOL [2]. In this article, we will perform simple docking using DockingPie1.2.

(more…)

Continue Reading

Docking

How to install the DockingPie plugin on PyMOL?

Dr. Muniba Faiza

Published

on

How to install DockingPie plugin on PyMOL?

DockingPie [1] is a plugin of PyMOL [2] made to fulfill the purpose of docking within the PyMOL interface. This plugin will allow you to dock using four different algorithms, namely, Vina, RxDock, SMINA, and ADFR. It will also allow you to perform flexible docking. Though the installation procedure is the same for all OSs, in this article, we are installing this plugin on Ubuntu (Linux).

(more…)

Continue Reading

Software

Video Tutorial: Calculating binding pocket volume using PyVol plugin.

Dr. Muniba Faiza

Published

on

Calculate Binding Pocket Volume in Pymol (using PyVol plugin).

This is a video tutorial for calculating binding pocket volume using the PyVol plugin [1] in Pymol [2].

(more…)

Continue Reading

Software

How to generate topology from SMILES for MD Simulation?

Dr. Muniba Faiza

Published

on

How to generate topology from SMILES for MD Simulation?

If you need to generate the topology of molecules using their SMILES, a simple Python script is available.

(more…)

Continue Reading

Software

[Tutorial] Installing jdock on Ubuntu (Linux).

Dr. Muniba Faiza

Published

on

[Tutorial] Installing jdock on Ubuntu (Linux).

jdock is an extended version of idock [1]. It has the same features as the idock along with some bug fixes. However, the binary name and the GitHub repository names are changed. We are installing jdock on Ubuntu (Linux).

(more…)

Continue Reading

Software

How to upgrade cmake on Ubuntu (Linux)?

Dr. Muniba Faiza

Published

on

How to upgrade cmake on Ubuntu/Linux?

In bioinformatics, cmake is used to install multiple software including GROMACS, jdock, and so on. Here is a short tutorial on how to upgrade cmake on Ubuntu and get rid of the previous version. (more…)

Continue Reading

Software

How to install GMXPBSA on Ubuntu (Linux)?

Dr. Muniba Faiza

Published

on

How to install GMXPBSA on Ubuntu (Linux)?

GMXPBSA is a tool to calculate binding free energy [1]. It is compatible with Gromacs version 4.5 and later. In this article, we will install GMXPBSA version 2.1.2 on Ubuntu (Linux).

(more…)

Continue Reading

Docking

[Tutorial] Installing Pyrx on Windows.

Dr. Muniba Faiza

Published

on

[Tutorial] Installing Pyrx on Windows.

Pyrx [1] is another virtual screening software that also offers to perform docking using Autodock Vina. In this article, we will install Pyrx on Windows. (more…)

Continue Reading

MD Simulation

How to solve ‘Could NOT find CUDA: Found unsuitable version “10.1”‘ error during GROMACS installation?

Dr. Muniba Faiza

Published

on

How to solve ‘Could NOT find CUDA: Found unsuitable version “10.1”‘ error during GROMACS installation?

Compiling GROMACS [1] with GPU can be trivial. Previously, we have provided a few articles on the same. In this article, we will solve an error frequently occurring during GROMACS installation.

(more…)

Continue Reading

Software

Installing Autodock4 on MacOS.

Dr. Muniba Faiza

Published

on

Installing Autodock4 on MacOS

Previously, we installed the Autodock suite [1] on Ubuntu. Visit this article for details. Now, let’s install it on MacOS.

(more…)

Continue Reading

Docking

How to install Autodock4 on Ubuntu?

Dr. Muniba Faiza

Published

on

How to install Autodock4 on Ubuntu?

Autodock suite is used for docking small molecules [1]. Recently, Autodock-GPU [2] is developed to accelerate the docking process. Its installation is described in this article. In this tutorial, we will install Autodock 4.2.6 on Ubuntu.

(more…)

Continue Reading

Software

DS Visualizer: Uses & Applications

Dr. Muniba Faiza

Published

on

DS Visualizer: Uses & Applications

Discovery Studio (DS) Visualizer (from BIOVIA) is a visualization tool for viewing, sharing, and analyzing proteins [1]. Here are some uses and applications of DS Visualizer.

(more…)

Continue Reading

Software

Protein structure & folding information exploited from remote homologs.

Dr. Muniba Faiza

Published

on

protein structure & folding prediction using remote homologs

Remote homologs are similar protein structures that share similar functions, but there is no easily detectable sequence similarity in them. A new study has revealed that the protein folding information can be exploited from remote homologous structures. A new tool is developed to recognize such proteins and predict their structure and folding pathway. (more…)

Continue Reading

RNA-seq analysis

Pathonoia- A new tool to detect pathogens in RNA-seq data.

Dr. Muniba Faiza

Published

on

Pathonoia- A new tool to detect pathogens in RNA-seq data.

Detecting viruses and bacteria in RNA-seq data with less false positive rate is a difficult task. A new tool is introduced to detect pathogens in RNA-seq data with high precision and recall known as Pathonoia [1].

(more…)

Continue Reading

Software

AlphaFill- New algorithm to fill ligands in AlphaFold models.

Dr. Muniba Faiza

Published

on

AlphaFill- New algorithm to fill ligands in AlphaFold models.

AlphaFold is a popular artificial intelligence based protein prediction tool [1]. Though it predicts good protein structures, it lacks the capability to predict the small molecules present in the structure such as ligands. For this purpose, AlphaFill is introduced by Hekkelman et al.,[2]. (more…)

Continue Reading

LATEST ISSUE

ADVERT