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BGDMdocker: a Docker workflow for data mining and visualization of bacterial pan-genomes and biosynthetic gene clusters

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RT @fopsom: Docker workflow of pan-genomes https://t.co/BUATkjKtcZ via @thePeerJ
Docker workflow of pan-genomes https://t.co/BUATkjKtcZ via @thePeerJ
RT @thePeerJ: BGDMdocker: a Docker workflow for data mining and visualization of bacterial pan-genomes and biosynthetic gene clusters https…
BGDMdocker: a Docker workflow for data mining and visualization of bacterial pan-genomes and biosynthetic gene clusters https://t.co/VyQZEbrUNf #BioInformatics #Genomics https://t.co/SxYogdqVlw
PEER-REVIEWED
Note that a Preprint of this article also exists, first published June 22, 2017.

Introduction

Docker is an open source project and platform for building, shipping, and running any app, enabling the widespread distribution of applications. Docker allows users to package an application, along with all its dependencies, into a standardized unit for software development (https://docs.docker.com/). Docker includes three core structural compositions: Image, Container, and Repository. An image can start software as complex as a database, wait for you (or someone else) to add data, store the data for later use, and then wait for the next person. Containers afford similar resource isolation and allocation benefits as virtual machines; however, a different architectural approach allows the former to be much more portable and efficient. When an app is in Docker containers, setting up and maintaining different environments or tools for each language is not necessary (https://hub.docker.com/). The Docker Hub Registry allows users to find, manage, and pull Images from community, official, and private image libraries, and is free to use for public repositories (https://www.docker.com/whatisdocker). GitHub is a web-based source code version control repository and Internet hosting service that is mostly used for code. Compared with GitHub, Docker Hub (https://hub.docker.com/) is a cloud-based registry service of Docker, whose most notable advantages include workflow automation throughout the development pipeline based on Images and Counter of Docker.

Academic bioinformatics software programs generally suffer from limitations such as installation and configuration difficulties, large dependencies, and restrictions on the amount of data that may be uploaded to online servers. Therefore, several excellent software programs are of limited use to biologists. Bioinformatics tools may be merged with Docker technology to build reproducible and convenient types of workflows. Docker provides programmers, development teams, and bioinformaticians with a common toolbox that allows users to take full advantage of bioinformatics tools, thus helping to build, ship, and run any app, as well as distribute apps anywhere. Docker technology is suitable for use in the field of bioinformatics because of certain advantages and characteristics that allow applications to run in an isolated, self-contained package. This package may be efficiently distributed and executed in a portable manner across a wide range of computing platforms (Aranguren & Wilkinson, 2015; Belmann et al., 2015; Hosny et al., 2016). To date, numerous bioinformatics tools based on Docker have been developed and published in different programming languages such as Perl and BioPerl (https://hub.docker.com/_/perl/; https://hub.docker.com/r/bioperl/bioperl/), python and biopython (https://hub.docker.com/_/python/; https://hub.docker.com/r/biopython/biopython), and R and Bioconductor (https://hub.docker.com/_/r-base/; https://hub.docker.com/r/bioconductor/release_base/). These projects have contributed to official Docker Images. The famous Galaxy program has also contributed to Docker Galaxy (Grüning, 2017).

Here, we used Docker technology to rapidly construct a pan-genome analysis process that may be used in Linux, Windows, or Mac environments (64-bit). The present process may also be deployed as a cloud-based system such as with Amazon EC2 or other cloud providers. This workflow should provide a useful service to biologists in the field of bioinformatics. Docker Containers have only a minor impact on the performance of common genomic pipelines (Tommaso et al., 2015).

Bacillus amyloliquefaciens has been extensively studied as an important biological control agent owing to its ability to inhibit the growth of fungi and bacteria (Nam et al., 2016). Using Docker, we rapidly executed a container (on Ubuntu 16.04 and Win10 hosts) to analyze the pan-genome and reveal biosynthetic gene cluster features of 44 B. amyloliquefaciens strains, as well as to visualize the results. The analytical workflow consisted of three toolkits: Prokka v1.11 (Seemann, 2014), panX (Ding, Baumdicker & Neher, 2017), and antiSMASH3.0 (Weber et al., 2015), for prokaryotic genome annotation, pan-genome analysis and visualization, and analysis of biosynthetic gene clusters, respectively. We included all of these applications and their dependencies in a BGDMdocker (bacterial genome data mining Docker-based) to enable the workflow to be implemented online with a single run. We additionally wrote three standalone Dockerfiles for Prokka, panX, and antiSMASH in order to meet the various requirements of different users. We recommend setting up the workflow with three independent files, each with a specific purpose. This method is presented in the Supplementary Information. Here, we describe how to build the workflow and conduct the analysis in detail.

Materials and Methods

Installation of latest Docker on your host

1. Copy the following commands for quickly and easily installing the latest Docker-CE (https://docs.docker.com/engine/installation/) (Ubuntu, Debian, Raspbian, Fedora, Centos, Redhat, Suse, Oracle, Linux etc. are all applicable):


$ curl -fsSL get.docker.com -o get-docker.sh
$ sudo sh get-docker.sh

If user would like to use Docker as a non-root user, you should now consider adding your user to the “docker” group, e.g., using:


$ sudo usermod -aG docker <user name>

Type the following commands at your shell prompt. If this outputs the Docker version, your installation was successful.


$ docker version

2. Installing latest Docker on Windows 10 Enterprise (https://docs.docker.com/docker-for-windows/install/):

The current version of Docker for Windows runs on 64-bit Windows 10 Pro, Enterprise, and Education editions. Download “InstallDocker.msi” (https://download.docker.com/win/stable/InstallDocker.msi). Double-click “InstallDocker.msi” to run the installer. Follow the install wizard to accept the license, authorize the installer, and proceed with the installation.

Type the following commands at your shell prompt (cmd.exe or PowerShell). If this outputs the Docker version, your installation was successful.


$ docker version

Use Docker to build the BGDMdocker workflow

1. On your host (with Docker), type the following command lines to build a BGDMdocker workflow:


$ git clone https://github.com/cgwyx/BGDMdocker.git

or: download “BGDMdocker-master.zip(https://github.com/cgwyx/BGDMdocker/archive/master.zip) file


$ unzip BGDMdocker-master.zip

2. Build workflow Images:


$ cd ./BGDMdocker
$ docker build -t BGDMdocker:latest .

Or: pull Images of BGDMdocker from DockerHub (https://hub.docker.com/r/cgwyx/bgdmdocker/), such as:


$ docker pull cgwyx/bgdmdocker

3. Run a Container from the BGDMdocker Image:


$ docker run -it --rm -v home:home -p 8000:8000 BGDMdocker:latest /bin/bash

If use the “-v home:home” parameter, Docker will mount the local folder/home into the Container under /home, storing all of your data in one directory in the home folder of the host operating system; then, you may access the directories of home from inside the Container.

We analyzed the pan-genome and biosynthetic gene clusters of 44 B. amyloliquefaciens strains using the BGDMdocker workflow. For detailed commands, see the Supplementary Information.

Results

Fast and reproducible building of the BGDMdocker workflow across computing platforms using Docker

Using Docker technology, the Dockerfile script file can build Images and run a container in seconds or milliseconds on Linux and Windows. The file may also be deployed in Mac and cloud-based systems such as Amazon EC2 or other cloud providers. The Dockerfile is a small, plain-text file that may be easily stored and shared. Therefore, the user is not required to install and configure the programs.

Here, based on Debian 8.0 (Jessie) Image, we have established a novel Docker-based bioinformatics platform for the study of microbe genomes and pan-genomes (Fig. 1). The workflow, which offers the advantages of cross-platform and modular reuse, provides biologists with simple and standardized tools to extract biological information from their own experiments and from online sequence databases. Researchers may therefore focus solely on mining information from the obtained sequences rather than determining how to install the software package. We have uploaded this Dockerfile to GitHub for sharing with relevant scientific researchers.

Schematic of BGDMdocker workflow based on Docker: building image and running container from Dockerfile, then login interaction patterns to run software.

Figure 1: Schematic of BGDMdocker workflow based on Docker: building image and running container from Dockerfile, then login interaction patterns to run software.

This enables the user to load and save Image.tar, run and commit Container, pull from and push to Docker repository.

Datamining and visualizing the pan-genomes of B. amyloliquefaciens

In order to explore the result data, a website (http://bapgd.hygenomics.com/pangenome/home) was built for the interactive exploration of the B. amyloliquefaciens pan-genome and biosynthetic gene clusters using the BGDMdocker workflow. Visualization allowed for the rapid filtering and searching of genes. For each gene cluster, panX displayed an alignment and a phylogenetic tree, mapped mutations within that cluster to the branches of the tree, and inferred gene losses and gains on the core-genome phylogeny. Here, we provide the summary statistics of the pan-genome (Table 1), the phylogenetic relationships of the 44 B. amyloliquefaciens strains (Fig. 2), and screenshots of the website (http://bapgd.hygenomics.com/pangenome/home) (Figs. 3 and 4). All data may be visualized and downloaded without registration.

Table 1:
Summary statistics of pan-genome of 44 B. amyloliquefaciens strains.
Accession Strains Gene numbers in pan-genome of B. amyloliquefaciens (total genes, 172,388; core gene clusters, 2,306) Gene of strain genomes
Total gene Core gene Acc gene Uni gene All gene All protein
CYHL01000001 JRS5 3,856 2,310 1,546 57 3,870 3,863
CYHP01000001 JRS8 3,994 2,311 1,683 118 4,016 4,006
NC_014551 DSM7 3,935 2,307 1,628 21 4,030 3,811
NC_017188 TA208 3,935 2,307 1,628 1 3,974 3,847
NC_017190 LL3 3,981 2,308 1,673 19 4,037 3,887
NC_017191 XH7 3,942 2,307 1,635 6 3,983 3,846
NC_017912 Y2 4,099 2,310 1,789 46 4,148 3,983
NC_020272 IT-45 3,803 2,310 1,493 4 3,832 3,678
NC_022653 CC178 3,754 2,310 1,444 19 3,795 3,641
NC_023073 LFB112 3,761 2,308 1,453 19 3,801 3,637
NZ_AUNG01000001 Lx-11 3,700 2,309 1,391 5 3,742 3,619
NZ_AUWK01000001 HB-26 3,797 2,311 1,486 30 3,842 3,714
NZ_AVQH01000001 EGD-AQ141 4,079 2,311 1,768 54 4,121 3,995
NZ_AWQY01000001 UASWS BA1 3,794 2,309 1,485 8 3,806 3,681
NZ_CP006058 UMAF6639 3,825 2,311 1,514 20 3,879 3,716
NZ_CP006960 UMAF6614 3,804 2,311 1,493 13 3,850 3,695
NZ_CP007242 KHG19 3,775 2,310 1,465 19 3,816 3,658
NZ_CP010556 L-H15 3,724 2,309 1,415 6 3,769 3,615
NZ_CP011278 L-S60 3,728 2,310 1,418 7 3,773 3,611
NZ_CP013727 MBE1283 3,794 2,314 1,480 24 3,856 3,681
NZ_CP014700 S499 3,776 2,310 1,466 5 3,819 3,671
NZ_CP014783 B15 3,820 2,315 1,505 13 3,875 3,704
NZ_CP016913 RD7-7 3,597 2,308 1,289 39 3,656 3,483
NZ_DF836091 CMW1 3,771 2,311 1,460 128 3,901 3,706
NZ_JCOC01000001 EBL11 3,733 2,308 1,425 20 3,773 3,682
NZ_JMEG01000001 B1895 3,824 2,306 1,518 167 4,026 3,623
NZ_JQNZ01000001 X1 3,724 2,309 1,415 3 3,766 3,619
NZ_JTJG01000001 JJC33M 3,888 2,309 1,579 121 3,952 3,796
NZ_JXAT01000001 LPL-K103 3,709 2,309 1,400 15 3,743 3,637
NZ_JZDI01000001 12B 8,166 2,354 5,812 4,040 8,194 7,985
NZ_KB206086 DC-12 3,910 2,311 1,599 50 3,984 3,842
NZ_KN723307 TF281 3,640 2,312 1,328 5 3,782 3,571
NZ_LGYP01000001 629 3,536 2,313 1,223 11 3,785 3,427
NZ_LJAU01000001 Bs006 4,042 2,312 1,730 46 4,074 3,969
NZ_LJDI01000020 XK-4-1 3,799 2,310 1,489 14 3,821 3,701
NZ_LMAG01000001 RHNK22 3,781 2,309 1,472 37 3,837 3,698
NZ_LMAT01000001 Jxnuwx-1 3,930 2,309 1,621 246 4,008 3,870
NZ_LMUC01000016 H57 3,816 2,310 1,506 42 3,859 3,732
NZ_LPUP01000011 11B91 3,790 2,311 1,479 49 3,892 3,702
NZ_LQQW01000001 M49 3,694 2,311 1,383 21 3,741 3,617
NZ_LQYO01000001 B4140 3,771 2,307 1,464 49 3,847 3,713
NZ_LQYP01000001 B425 3,921 2,310 1,611 39 4,034 3,844
NZ_LYUG01000001 SRCM101266 3,724 2,306 1,418 15 3,781 3,628
NZ_LZZO01000001 SRCM101294 3,946 2,308 1,638 175 3,982 3,850
DOI: 10.7717/peerj.3948/table-1

Note:

Genome sequences of 44 B. amyloliquefaciens (https://www.ncbi.nlm.nih.gov/genome/genomes/848) strains downloaded from GenBank RefSeq database: “Acc gene” refers to accessory gene (dispensable gene); “Uni gene” refers to unique gene (strain-specific gene); “All genes” refers to gene of *.gbff files recorder, including Pseudo Genes; “Total genes” refers to those used for pan-genome analysis gene of *.gbff files recorder, excluding Pseudo Genes.

Phylogenetic tree of 44 B. amyloliquefaciens strains.

Figure 2: Phylogenetic tree of 44 B. amyloliquefaciens strains.

The tree was constructed using all genes shared between the 44 strains (2,306 core genes). The scale bar represents genetic distance.
Screenshot of website (http://bapgd.hygenomics.com/pangenome/home) for visualization of gene cluster table (A) and sequence alignment (B).

Figure 3: Screenshot of website (http://bapgd.hygenomics.com/pangenome/home) for visualization of gene cluster table (A) and sequence alignment (B).

Screenshot of website (http://bapgd.hygenomics.com/pangenome/home) for visualization of Phylogenetic tree of 44 B. amyloliquefaciens strains (A) and genes (B).

Figure 4: Screenshot of website (http://bapgd.hygenomics.com/pangenome/home) for visualization of Phylogenetic tree of 44 B. amyloliquefaciens strains (A) and genes (B).

The scale bar represents genetic distance.

Datamining and visualizing of biosynthetic gene clusters of B. amyloliquefaciens

Results from the identification and analysis of the biosynthetic gene clusters of 44 B. amyloliquefaciens strain genomes, using the BGDMdocker workflow, have been uploaded to our website (http://bapgd.hygenomics.com/pangenome/home). All data may be downloaded without registration.

Here, we provide brief summary statistics for the biosynthetic gene clusters of all 44 strains (Table 2), as well as an example of the type and number of biosynthetic gene clusters in the Y2 (NC_017912) strain (Table 3) and representative screenshots of the website (http://bapgd.hygenomics.com/pangenome/home) (Figs. 5 and 6). There are a total of 31 gene clusters in the genome of Y2. Among these, 21 gene clusters show similarities to known clusters in MIBiG (http://mibig.secondarymetabolites.org/) such as surfactin, mersacidin, and fengycin; the remaining 10 gene clusters are unknown.

Table 2:
Summary statistics of biosynthetic gene clusters of 44 B. amyloliquefaciens strains.
Accession Strains Biosynthetic gene clusters Genome of strains
Total Known Unknown Type Size (Mb) Gene Protein
CYHL01000001 JRS5 38 27 11 12 4.03148 3,870 3,863
CYHP01000001 JRS8 42 26 16 11 4.0909 4,016 4,006
NC_014551 DSM7 31 18 13 9 3.9802 4,030 3,811
NC_017188 TA208 29 17 12 10 3.93751 3,974 3,847
NC_017190 LL3 29 17 12 9 4.00199 4,037 3,887
NC_017191 XH7 29 17 12 10 3.9392 3,983 3,846
NC_017912 Y2 31 21 10 11 4.23862 4,148 3,983
NC_020272 IT-45 32 19 13 11 3.93687 3,832 3,678
NC_022653 CC178 35 19 16 9 3.91683 3,795 3,641
NC_023073 LFB112 35 20 15 10 3.94275 3,801 3,637
NZ_AUNG01000001 Lx-11 37 25 12 11 3.88689 3,742 3,619
NZ_AUWK01000001 HB-26 45 30 15 9 3.98936 3,842 3,714
NZ_AVQH01000001 EGD-AQ141 36 26 10 12 4.22259 4,121 3,995
NZ_AWQY01000001 UASWS BA1 37 25 12 11 3.94409 3,806 3,681
NZ_CP006058 UMAF6639 35 21 14 10 4.03464 3,879 3,716
NZ_CP006960 UMAF6614 32 20 12 10 4.00514 3,850 3,695
NZ_CP007242 KHG19 32 20 12 10 3.95336 3,816 3,658
NZ_CP010556 L-H15 32 19 13 10 3.90597 3,769 3,615
NZ_CP011278 L-S60 32 19 13 10 3.90302 3,773 3,611
NZ_CP013727 MBE1283 35 22 13 12 3.97993 3,856 3,681
NZ_CP014700 S499 33 19 14 11 3.93593 3,819 3,671
NZ_CP014783 B15 29 19 10 10 4.00675 3,875 3,704
NZ_CP016913 RD7-7 31 17 14 8 3.68821 3,656 3,483
NZ_DF836091 CMW1 30 20 10 11 3.90857 3,901 3,706
NZ_JCOC01000001 EBL11 35 23 12 11 3.92932 3,773 3,682
NZ_JMEG01000001 B1895 38 24 14 12 4.10728 4,026 3,623
NZ_JQNZ01000001 X1 40 28 12 10 3.9211 3,766 3,619
NZ_JTJG01000001 JJC33M 36 25 11 12 3.96166 3,952 3,796
NZ_JXAT01000001 LPL-K103 36 23 13 9 3.87327 3,743 3,637
NZ_JZDI01000001 12B 69 49 20 11 7.59676 8,194 7,985
NZ_KB206086 DC-12 28 19 9 11 4.01656 3,984 3,842
NZ_KN723307 TF281 31 20 11 11 3.98764 3,782 3,571
NZ_LGYP01000001 629 31 18 13 10 3.90337 3,785 3,427
NZ_LJAU01000001 Bs006 45 30 15 10 4.17309 4,074 3,969
NZ_LJDI01000020 XK-4-1 37 24 13 12 3.94181 3,821 3,701
NZ_LMAG01000001 RHNK22 38 27 11 12 3.97818 3,837 3,698
NZ_LMAT01000001 Jxnuwx-1 40 27 13 10 4.08932 4,008 3,870
NZ_LMUC01000016 H57 34 23 11 11 3.95883 3,859 3,732
NZ_LPUP01000011 11B91 33 20 13 10 4.02366 3,892 3,702
NZ_LQQW01000001 M49 41 30 11 11 3.88665 3,741 3,617
NZ_LQYO01000001 B4140 39 25 14 11 4.01425 3,847 3,713
NZ_LQYP01000001 B425 29 20 9 9 3.9682 4,034 3,844
NZ_LYUG01000001 SRCM101266 31 19 12 11 3.76536 3,781 3,628
NZ_LZZO01000001 SRCM101294 32 20 12 10 3.96275 3,982 3,850
DOI: 10.7717/peerj.3948/table-2

Note:

“Total” of Biosynthesis gene clusters includes “Known” and “Unknown.” “Known” of Biosynthesis gene clusters is inferred from the MIBiG (Minimum Information about a Biosynthetic Gene cluster, http://mibig.secondarymetabolites.org). “Unknown” of Biosynthesis gene clusters is detected by Cluster Finder and further categorized into putative (“Cf_putative”) biosynthetic types. A full integration of the recently published Cluster Finder algorithm now allows the use of this probabilistic algorithm to detect putative gene clusters of unknown types; “–” of host is unrecorded.

Table 3:
Biosynthetic gene clusters of Y2(NC_017912) strain.
Cluster Type Most similar known cluster MIBiG BGC-ID
Cluster 1 Nrps Surfactin_biosynthetic_gene_cluster (43% of genes show similarity) BGC0000433_c1
Cluster 2 Cf_putative
Cluster 3 Cf_putative
Cluster 4 Cf_fatty_acid
Cluster 5 Phosphonate Pactamycin_biosynthetic_gene_cluster (3% of genes show similarity) BGC0000119_c1
Cluster 6 Cf_saccharide Plantathiazolicin/plantazolicin_biosynthetic_gene_cluster (33% of genes show similarity) BGC0000569_c1
Cluster 7 Cf_putative
Cluster 8 Otherks
Cluster 9 Cf_fatty_acid
Cluster 10 Cf_putative
Cluster 11 Terpene
Cluster 12 Cf_fatty_acid
Cluster 13 Cf_putative
Cluster 14 Cf_putative
Cluster 15 Transatpks Macrolactin_biosynthetic_gene_cluster (90% of genes show similarity) BGC0000181_c1
Cluster 16 Nrps-Transatpks Bacillaene_biosynthetic_gene_cluster (85% of genes show similarity) BGC0001089_c1
Cluster 17 Nrps-Transatpks Fengycin_biosynthetic_gene_cluster (93% of genes show similarity) BGC0001095_c1
Cluster 18 Terpene
Cluster 19 Cf_saccharide-T3pks
Cluster 20 Transatpks Difficidin_biosynthetic_gene_cluster (100% of genes show similarity) BGC0000176_c1
Cluster 21 Cf_putative
Cluster 22 Nrps-Bacteriocin Bacillibactin_biosynthetic_gene_cluster (100% of genes show similarity) BGC0000309_c1
Cluster 23 Cf_saccharide
Cluster 24 Nrps
Cluster 25 Cf_saccharide Teichuronic_acid_biosynthetic_gene_cluster (100% of genes show similarity) BGC0000868_c1
Cluster 26 Cf_putative
Cluster 27 Cf_saccharide Bacilysin_biosynthetic_gene_cluster (100% of genes show similarity) BGC0001184_c1
Cluster 28 Cf_putative
Cluster 29 Lantipeptide Mersacidin_biosynthetic_gene_cluster (90% of genes show similarity) BGC0000527_c1
Cluster 30 Cf_saccharide
Cluster 31 Cf_putative
DOI: 10.7717/peerj.3948/table-3

Note:

“Cf putative” refers to putative biosynthetic types (unknown types) detected by Cluster Finder and further categorized, known types are from the MIBiG (Minimum Information about a Biosynthetic Gene cluster, http://mibig.secondarymetabolites.org).

Screenshot of visualization of the total number of biosynthetic gene clusters of the Y2 strain.

Figure 5: Screenshot of visualization of the total number of biosynthetic gene clusters of the Y2 strain.

Screenshot of website (http://bapgd.hygenomics.com/pangenome/home) for the visualization of Transatpks type cluster of Y2 (most similar known cluster to the Difficidin_biosynthetic_gene_cluster).

Figure 6: Screenshot of website (http://bapgd.hygenomics.com/pangenome/home) for the visualization of Transatpks type cluster of Y2 (most similar known cluster to the Difficidin_biosynthetic_gene_cluster).

Discussion

The Dockerfiles of BGDMdocker scripts are convenient for deployment and sharing, and it is easy for other users to customize the Images by editing the Dockerfile directly. This is in contrast to Makefiles and other installations, for which the resulting builds differ across different machines (Boettiger, 2015). Dockerfiles can maintain and update related adjustments, rapidly recover from system failure events, control versions, and build application environments with the optimal flexibility. BGDMdocker Images enable portability and modular reuse. Bioinformatics tools are written in a variety of languages and require different operating environment configurations across platforms. Docker technology is capable of executing the same functions and services in different environments without additional configurations (Folarin, Dobson & Newhouse, 2015), thus creating reproducible tools with high efficiency. By constructing pipelines with different tools, bioinformaticians may automatically and effectively analyze biological problems of interest. The BGDMdocker Container enables application isolation with high efficiency and flexibility. Applications may run Container independently with Docker technology, and each management command (start, stop, boot, etc.) may be executed in seconds or milliseconds. Hundreds or thousands of Containers may be run on a single host at same time (Ali, El-Kalioby & Abouelhoda, 2016), thus ensuring that the failure of one task does not cause disruption of the entire process: new Containers may be initialized rapidly to continue the task until the completion of the entire process, thus improving overall efficiency.

In recent years, several online tools and software suites have been developed for pan-genome analysis, including Roary, PGPA, SplitMEM, PanGP, and PanTools. However, generally, the installation of these pipelines with many dependencies, but a single function, is complex and challenging. Therefore, limiting researchers’ ability directly focus on their analyses of interest (Table 4). Although the BGDMdocker workflow includes several tools, installing and running the software is quite simple. Biologists may automatically install, configure, and test the scripts, making these processes faster and the results repeatable.

Table 4:
Function of BGDMdocker workflow compared with other pan-genome tools.
Tools Automatic installation Cross platform Result visualization Genome annotation Gene cluster mining
Roary × × × ×
PGPA × × × × ×
SplitMEM × × × × ×
PanGP × × × × ×
PanTools × × × ×
BGDMdocker
DOI: 10.7717/peerj.3948/table-4

Note:

√ is provided with the function, × is not provided with the function.

Conclusion

Here, we present a BGDMdocker workflow to achieve bacterial and viral genome annotation, pan-genome analysis, mining of biosynthetic gene clusters, and visualization of results on a local host or online. This allows researchers to browse information for every gene, including duplication, diversity, indel events, and sequence alignments, as well as for biosynthetic gene clusters, including structure, type, description, detailed annotation, and predicted core structure of the target compounds. These tools and their installation commands and dependencies were all written in a Dockerfile. We used this Dockerfile to build a Docker Image and run Container for analyzing the pan-genome of 44 B. amyloliquefaciens strains retrieved from a public database. The pan-genome included a total of 172,388 genes and 2,306 core gene clusters. The visualization of the pan-genomic data included alignments, phylogenetic trees with mutations within each cluster mapped to the branches of the tree, and inference of gene losses and gains on the core-genome phylogeny for each gene cluster. In addition, 997 known (MIBiG: http://mibig.secondarymetabolites.org database) and 553 unknown (antiSMASH-predicted clusters and Pfam database) genes in biosynthetic gene clusters and orthologous groups were identified in all strains. The BGDMdocker workflow for the analysis and visualization of pan-genomes and biosynthetic gene clusters may be fully reused immediately across different computing platforms (Linux, Windows, Mac, and cloud-based systems), with flexible and rapid deployment of integrated software packages across various platforms. This workflow may also be used for other pan-genome analyses and visualization of other species. Additionally, the visual display of data provided in this study may be completely duplicated. All resulting data and relevant tools and files may be downloaded from our website (http://bapgd.hygenomics.com/pangenome/home) with no registration required.

Supplemental Information

The command line and tip of BGDMdocker.

DOI: 10.7717/peerj.3948/supp-1