<img width="500" height="245" src="config-files/logo.png"> </p> <a href="https://github.com/GunzIvan28/rMAP/blob/master/LICENSE"><img alt="GitHub license" src="https://img.shields.io/github/license/GunzIvan28/rMAP?color=blue"></a> [![Anaconda-Server Badge](https://anaconda.org/gunzivan28/rmap/badges/platforms.svg)](https://anaconda.org/gunzivan28/rmap) [![Anaconda-Server Badge](https://anaconda.org/gunzivan28/rmap/badges/version.svg)](https://anaconda.org/gunzivan28/rmap) ![Conda](https://img.shields.io/conda/v/gunzivan28/rmap) [![Anaconda-Server Badge](https://anaconda.org/gunzivan28/rmap/badges/latest_release_date.svg)](https://anaconda.org/gunzivan28/rmap) [![Anaconda-Server Badge](https://anaconda.org/gunzivan28/rmap/badges/downloads.svg)](https://anaconda.org/gunzivan28/rmap) Thorough easy-to-use resistome profiling bioinformatics pipeline for **ESKAPE** (_Enterococcus faecium_, _Staphylococcus aureus,_ _Klebsiella pneumoniae_, _Acinetobacter baumannii_, _Pseudomonas aeruginosa_, and _Enterobacter_ species) pathogens using Illumina Whole-genome sequencing (WGS) paired-end reads ## 🎬 Introduction The evolution of the genomics era has led to generation of sequencing data at an unprecedented rate. Many bioinformatics tools have been created to analyze this data; however, very few tools can be utilized by individuals without prior reasonable bioinformatics training rMAP(Rapid Microbial Analysis Pipeline) was designed using already pre-existing tools to automate analysis WGS Illumina paired-end data for the clinically significant ESKAPE group pathogens. It is able to exhaustively decode their resistomes whilst hiding the technical impediments faced by inexperienced users. Installation is fast and straight forward. A successful run generates a.htmlreport that can be easily interpreted by non-bioinformatics personnel to guide decision making ## 🏷️ Pipeline Features The rMAP pipeline toolbox is able to perform: 1. Download raw sequences from [NCBI-SRA](https://www.ncbi.nlm.nih.gov/sra/?term=) archive 2. Run quality control checks 3. Adapter and poor quality read trimming 4. De-novo assembly using [shovill](https://github.com/tseemann/shovill) or [megahit](https://github.com/voutcn/megahit) 5. Contig and scaffold annotation using [prokka](https://github.com/tseemann/prokka) 6. Variant calling using [freebayes](https://github.com/ekg/freebayes) and annotation using [snpEff](http://snpeff.sourceforge.net/) 7. SNP-based phylogeny inference using Maximum-Likelihood methods using [iqtree](https://github.com/Cibiv/IQ-TREE) 8. Antimicrobial resistance genes, plasmid, virulence factors and MLST profiling 9. [Insertion sequences](https://github.com/jhawkey/IS_mapper) detection 10. Pangenome analysis 11. Interactive visual.HTMLreport generation using [R](https://www.r-project.org/) packages and [Markdown](https://www.markdownguide.org/) language ## ⚙️ Installation An ultra-fast compilation of the pipeline has been developed using [Mamba](https://github.com/conda-forge/miniforge) from Miniforge. Install [Mamba](https://github.com/conda-forge/miniforge) by running the following commands: **For Linux Users:**cd && wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh`

bash Miniforge3-Linux-x86_64.sh

For MacOS Users: cd && wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-x86_64.sh
bash Miniforge3-MacOSX-x86_64.sh
export PATH="$CONDA_PREFIX":$PATH and source using source ~/.bashrc

Clone the main repository to get started:
git clone https://github.com/GunzIvan28/rMAP.git
cd rMAP
mamba update -n base -y -c defaults conda

Select the appropriate installer for your computer (either rMAP-1.0-Linux-installer.yml or rMAP-1.0-macOs-installer.yml)

For Linux Users: mamba env create -n rMAP-1.0 -y --file rMAP-1.0-Linux-installer.yml
For MacOS Users: mamba env create -n rMAP-1.0 -y --file rMAP-1.0-macOs-installer.yml

mamba activate rMAP-1.0
bash setup.sh
rMAP -h

bash This is rMAP 1.0 Developed and maintained by Ivan Sserwadda & Gerald Mboowa SYPNOSIS: Bacterial analysis Toolbox for profiling the Resistome of ESKAPE pathogens using WGS paired-end reads USAGE: rMAP [options] --input <DIR> --output <OUTDIR> --reference <REF> GENERAL: -h/--help Show this help menu -v/--version Print version and exit -x/--citation Show citation and exit OBLIGATORY OPTIONS: -i/--input Location of the raw sequences to be analyzed by the pipeline [either .fastq or .fastq.gz] -o/--output Path and name of the output directory -r/--reference Path to reference genome(.gbk). Provide '.gbk' to get annotated vcf files and insertion sequences [default="REF.gbk"] -c/--config Install and configure full software dependencies OTHER OPTIONS: -d/--download Download sequences from NCBI-SRA. Requires 'list.txt' of sample ids saved at $HOME directory -f/--quality Generate .html reports with quality statistics for the samples -q/--trim Trims adapters off raw reads to a phred quality score[default=27] -a/--assembly Perform De novo assembly [default=megahit] Choose either 'shovill' or 'megahit' -vc/--varcall Generates SNPs for each sample and a merged 'all-sample ID' VCF file to be used to infer phylogeny in downstream analysis -t/--threads Number of threads to use <integer> [default=4] -m/--amr Profiles any existing antimicrobial resistance genes, virulence factors, mlsts and plasmids present within each sample id. -p/--phylogeny Infers phylogeny using merged all-sample ID VCF file to determine diversity and evolutionary relationships using Maximum Likelihood(ML) in 1000 Bootstraps -s/--pangenome Perform pangenome analysis. A minimum of 3 samples should be provided to run this option -g/--gen-ele Interrogates and profiles for mobile genomic elements(MGE) and insertion sequences(IS) that may exist in the sequences For further explanation please visit: https://github.com/GunzIvan28/rMAP

Before starting the pipeline, run the command below to install and enjoy the full functionality of the software. This is done only once
rMAP -t 8 --config or rMAP -t 8 -c

## 📀 Snippets of commandline arguments

Using a sample-ID 'list.txt' saved at $HOME, use rMAP to download sequences from NCBI-SRA

rMAP -t 8 --download

Perform a full run of rMAP using
rMAP -t 8 --reference full_genome.gbk --input dir_name --output dir_name --quality --assembly shovill --amr --varcall --trim --phylogeny --pangenome --gen-ele

The short notation for the code above can be run as follows:
rMAP -t 8 -r full_genome.gbk -i dir_name -o dir_name -f -a shovill -m -vc -q -p -s -g

## 🚀 Arguments

### ⚡ Mandatory

- -c | --config This installs R-packages and other dependancies required for downstream analysis. It is run only once, mandatory and the very first step performed before any analysis
- -i | --input Location of sequences to be analyzed either in .fastq or .fastq.gz formats. If reads are not qzipped, rMAP will compress them for the user for optimization
- -o | --output Name of directory to output results. rMAP creates the specified folder if it does not exist
- -r | --reference Provide the recommended reference genome in genbank format renamed with extension .gbk e.g reference_name.gbk required for variant calling. A reference in fasta format e.g reference_name.fasta or reference_name.fa can be used but will not produce annotated vcf files

### 🎨 Other options

- -o | --download This option downloads sequences from NCBI-Sequence Read Archive. Create a text file 'list.txt' containing the IDs of the samples to be downloaded and save it at $HOME directory. The downloaded samples will be saved at $HOME/SRA_READS
- -f | --quality Generates quality metrics for the input sequences visualized as .html reports
- -q | --trim Identifies and trims illumina library adapters off the raw reads and poor quality reads below a phred quality score of 27 with minimum length of 80bp set as the default for the software
- -a | --assembly Performs De-novo assembly for the trimmed reads. Two assemblers are available for this step: shovill or megahit. Selecting "shovill" will perform genome mapping and several polishing rounds with removal of ‘inter-contig’ gaps to produce good quality contigs and scaffolds but is SLOW. Selecting "megahit" produces contigs with relatively lower quality assembly metrics but is much FASTER
- -vc | --varcall Maps reads to the reference genome and calls SNPs saved in vcf format. A merged ‘all-sample ID’ VCF file to be used to infer phylogeny in downstream analysis is also generated at this stage
- -t | --threads Specifies the number of cores to use as an integer. Default cores are set to 4
- -m | --amr Provides a snapshot of the existing resistome (antimicrobial resistance genes, virulence factors, mlsts and plasmids) present in each sample id
- -p | phylogeny Uses the vcf file containing SNPs for all of the samples combined as an input, transposes it into a multiple alignment fasta file and infers phylogenetic analysis using Maximum-Likelihood method. The trees generated are in 1000 Bootstrap values
- -s | --pangenome Performs pangenome analysis for the samples using Roary. A minimum of 3 samples is required for this step
- -g | --gene-ele This interrogates for any Insertion Sequences that may have been inserted anywhere within the genomes of the samples. These sequences are compared against a database of the commonly reported insertion Sequences found in organisms originating from the ESKAPE fraternity
- -h | --help Shows the main menu
- -v | version Prints software version and exits
- -x | citation Shows citation and exits

## 📗 Report visualization

A sample of the interractive HTML report generated from the pipeline can be viewed at this link.
The pipeline also retains the intermediate files and respective folders within the reports directory to be interrogated further by experienced users for any particular genes that may be of interest.

## 📝 Information

### How to cite

When using rMAP, please cite as:

Sserwadda, I., & Mboowa, G. (2021). rMAP: the Rapid Microbial Analysis Pipeline for ESKAPE bacterial group whole-genome sequence data. Microbial genomics, 7(6), 10.1099/mgen.0.000583. https://doi.org/10.1099/mgen.0.000583. PMID: 34110280

### 🎞️ Credits

This pipeline was written by Ivan Sserwadda GunzIvan28 and Gerald Mboowa gmboowa. If you want to contribute, please open an issue or a pull request and ask to be added to the project - everyone is welcome to contribute

### ✍️ Authors

- Ivan Sserwadda
- Gerald Mboowa

### 💡 Tutorial
rMAP was built on the philosophy of universal userbility. Compilation and successful usage of the pipeline can turn out to be a nightmare for individuals without commandline experience. The authors created this short basic tutorial to be used as a reference for mainstream analysis and troubleshooting purposes

1. Installation

Follow the installation procedures using the Miniconda installation instructions above by copying and pasting line-by-line in your terminal.

2. Downloading sample datasets

A dataset comprised of 3 paired-end Acinetobacter Whole-genome sequences and a reference genome can be downloaded using rMAP_datasets link.

3. Preparing files for rMAP run

Open your command line terminal and run the following commands

- Change to your home directory and create a folder named “rMAPdatasets”
cd && mkdir rMAP_datasets
- Unzip the downloaded datasets from the link. Copy and paste the fastq.gz sequences of ERR1989084, ERR1989100,ERR1989115 into rMAP_datasets. Copy and paste the reference genome acinetobacter.gbk to the $HOME directory
- Activate rMAP environment using conda activate rMAP-1.0 and install the pipeline’s additional full packages using rMAP -t 8 -c.

4. Running rMAP

- After confirming that you have folder rMAP_datasets containing sequences ERR1989084, ERR1989100,ERR1989115, a reference genome acinetobacter.gbk saved at the home directory, and the rMAP dependencies fully installed, run the following command:
rMAP -t 8 --reference acinetobacter.gbk --input rMAP_datasets --output Acinetobacter_output --quality --assembly shovill --amr --varcall --trim --phylogeny --pangenome --gen-ele
WHERE:

- -t 8 specifies usage of 8 threads by default. User can specify more if available
- --reference specifies the path of reference genome
- --input contains our whole genome sequence datasets i.e rMAP_datasets
- --output specifies an output path called Acinetobacter_output for the intermediate files and results
- --quality, --assembly shovill, --amr, --varcall, --trim, --phylogeny ,--pangenome, --gen-ele activate the options for quality control, genome assembly using shovill assembler, antimicrobial resistance gene profiling, sequence trimming, phylogenetic analysis, pangenome and insertion sequence characterization respectively in the rMAP run.
- A successful run should generate a HTML report similar to the one in this link. Submit any queries or bugs to the Issue Tracker platform and the developers will see to it that it is rectified.

### 🙏 Acknowledgements

rMAP was inspired and adapted from the TORMES pipeline, developed by Quijada et al. (2019) and reachable at https://github.com/nmquijada/tormes. The reporting format for rMAP was mainly adapted and modified from the Tormes pipeline. Other alternative tools similar to rMAP that you could consider depending on the type analysis to be computed:

AQUAMIS
Deneke C, Brendebach H, Uelze L, Borowiak M, Malorny B, Tausch SH. Species-Specific Quality Control, Assembly and Contamination Detection in Microbial Isolate Sequences with AQUAMIS. Genes. 2021;12. doi:10.3390/genes12050644

ASA³P
Schwengers O, Hoek A, Fritzenwanker M, Falgenhauer L, Hain T, Chakraborty T, Goesmann A. ASA³P: An automatic and scalable pipeline for the assembly, annotation and higher-level analysis of closely related bacterial isolates. PLoS Comput Biol 2020;16:e1007134. https://doi.org/10.1371/journal.pcbi.1007134.

MicroPIPE
Murigneux V, Roberts LW, Forde BM, Phan M-D, Nhu NTK, Irwin AD, Harris PNA, Paterson DL, Schembri MA, Whiley DM, Beatson SA MicroPIPE: validating an end-to-end workflow for high-quality complete bacterial genome construction.l, 22(1), 474. (2021) https://doi.org/10.1186/s12864-021-07767-z

Nullarbor
Seemann T, Goncalves da Silva A, Bulach DM, Schultz MB, Kwong JC, Howden BP. Nullarbor Github https://github.com/tseemann/nullarbor

ProkEvo
Pavlovikj N, Gomes-Neto JC, Deogun JS, Benson AK ProkEvo: an automated, reproducible, and scalable framework for high-throughput bacterial population genomics analyses. PeerJ, e11376 (2021) https://doi.org/10.7717/peerj.11376

Public Health Bacterial Genomics
Libuit K, Ambrosio F, Kapsak C Public Health Bacterial Genomics* _GitHub https://github.com/theiagen/public_health_bacterial_genomics

### 🔌 Third Party Plugins

This softwares’ foundation is built using pre-existing tools. When using it, please don’t forget to cite the following:

- ABRicate=1.0.1
- FastQC=0.11.9
- MultiQC=1.9
- Snippy=4.3.6
- SnpEff=4.5covid19
- AMRFinderPlus=3.8.4
- Prokka=1.14.6
- Prodigal=2.6.3
- Freebayes=1.3.2
- Unicycler=0.4.8
- Mlst=2.19.0
- Assembly-stats=1.0.1
- SRA-Tools=2.10.8
- BWA=0.7.17
- Kleborate=1.0.0
- Mafft=7.471
- Quast=5.0.2
- R-base=4.0.2
- Trimmomatic=0.39
- Megahit=1.2.9
- Parallel=20200722
- Shovill=1.0.9
- Vt=2015.11.10
- Fasttree=2.1.10
- Samclip=0.4.0
- Nextflow=20.07.1
- Any2fasta=0.4.2
- Biopython.convert=1.0.3
- Iqtree=2.0.3
- Bmge=1.12
- Samtools=1.9
- Roary=3.13.0
- ISmapper=2.0.1
- Cairosvg=2.4.2

### 🐛 To report bugs, ask questions or seek help

The software developing team works round the clock to ensure the bugs within the program are captured and fixed.
For support or any inquiry:
You can submit your query using the Issue Tracker