SeroBA is a k-mer based Pipeline to identify the Serotype from Illumina NGS reads for given references. You can use SeroBA to download references from (https://github.com/phe-bioinformatics/PneumoCaT) to do identify the capsular type of Streptococcus pneumoniae.
- Further Information
SeroBA can predict serotypes, by identifying the cps locus, directly from raw whole genome sequencing read data with 98% concordance using a k-mer based method, can process 10,000 samples in just over 1 day using a standard server and can call serotypes at a coverage as low as 10x. SeroBA is implemented in Python3 and is freely available under an open source GPLv3
SeroBA has the following dependencies:
- Python3 version >= 3.3.2
- KMC version >= 3.0
- MUMmer version >= 3.1
There are a number of ways to install SeroBA and details are provided below. If you encounter an issue when installing SeroBA please contact your local system administrator. If you encounter a bug please log it here.
Set up bioconda channel:
conda config --add channels bioconda
conda install -c bioconda seroba
Ensure you have a development environment setup (you may have done this already):
yum -y update yum -y groupinstall 'Development Tools' yum -y install https://centos7.iuscommunity.org/ius-release.rpm
Install SeroBA and its dependancies:
yum -y install python36u python36u-pip python36u-devel zlib-devel wget which python36u-tkinter ln -s $(which pip3.6) /usr/bin/pip3 bash <(curl -fsSL https://raw.githubusercontent.com/sanger-pathogens/seroba/master/install_dependencies.sh)
Make sure to add the PATHs outputted by this script to your .bashrc file (or equivalent). Finally install SeroBA:
pip3 install seroba
Debian Testing/ Ubuntu 17.10
Install the dependancies:
sudo apt-get update sudo apt-get install ariba python3-pip wget
Manually install KMC version 3 (version 2 is the latest in Debian but is incompatible). Add the binaries to your PATH (e.g. in your bash profile).
mkdir kmc && cd kmc wget https://github.com/refresh-bio/KMC/releases/download/v3.0.0/KMC3.linux.tar.gz tar xvfz KMC3.linux.tar.gz export PATH=$PWD:$PATH
Finally install SeroBA:
pip3 install seroba
Ubuntu 16.04 (Xenial)
Install the dependancies:
apt-get update apt-get install --no-install-recommends -y build-essential cd-hit curl git libbz2-dev liblzma-dev mummer python python3-dev python3-setuptools python3-pip python3-tk python3-matplotlib unzip wget zlib1g-dev wget -q https://raw.githubusercontent.com/sanger-pathogens/seroba/master/install_dependencies.sh && bash ./install_dependencies.sh
Once the dependencies are installed, install SeroBA using pip:
pip3 install seroba
Install Docker. We have a docker container which gets automatically built from the latest version of SeroBA. To install it:
docker pull sangerpathogens/seroba
To use it you would use a command such as this (substituting in your directories), where your files are assumed to be stored in /home/ubuntu/data:
docker run --rm -it -v /home/ubuntu/data:/data sangerpathogens/seroba seroba runSerotyping seroba/database /data/read_1.fastq.gz /data/read_2.fastq.gz /data/output_folder
Running the tests
The test can be run from the top level directory:
python setup.py test
Setting up the database
You can use the CTV of PneumoCaT by using seroba getPneumocat. It is also possible to add new serotypes by adding the references sequence to the “references.fasta” file in the database folder. Out of the information provided by this database a TSV file is created while using seroba createDBs. You can easily put in additional genetic information for any of these serotypes in the given format.
Since SeroBA v0.1.3 an updated variant of the CTV from PneumoCaT is provided in the SeroBA package. This includes the serotypes 6E, 6F, 11E, 10X, 39X and two NT references. It is not necessary to use SeroBA getPneumocat. For SeroBA version 0.1.3 and greater, download the database provided within this git repository:
For git users
Clone the git repository: git clone https://github.com/sanger-pathogens/seroba.git
Copy the database to a directory:
cp -r seroba/database my_directory
Delete the git repository to clean up your system:
rm -r seroba
For svn users
Install svn. Checkout the database directory:
svn checkout "https://github.com/sanger-pathogens/seroba/trunk/database"
Continue with Step 2.
For SeroBA version 0.1.2 and smaller:
usage: seroba getPneumocat <database dir> Downloads PneumoCat and build an tsv formatted meta data file out of it positional arguments: database dir directory to store the PneumoCats capsular type variant (CTV) database
usage: seroba createDBs <database dir> <kmer size> Creates a Database for kmc and ariba positional arguments: database dir output directory for kmc and ariba Database kmer size kmer_size you want to use for kmc , recommended = 71 usage: seroba runSerotyping [options] <databases directory> <read1> <read2> <prefix> Example : seroba createDBs my_database/ 71 Identify serotype of your input data positional arguments: database dir path to database directory read1 forward read file read2 reverse read file prefix unique prefix optional arguments: -h, --help show this help message and exit Other options: --noclean NOCLEAN Do not clean up intermediate files (assemblies, ariba report) --coverage COVERAGE threshold for k-mer coverage of the reference sequence (default = 20) Summaries the output in one tsv file usage: seroba summary <output folder> positional arguments: output folder directory where the output directories from seroba runSerotyping are stored
In the folder ‘prefix’ you will find a pred.tsv including your predicted serotype as well as a file called detailed_serogroup_info.txt including information about SNP, genes, and alleles that are found in your reads. After the use of “seroba summary” a tsv file called summary.tsv is created that consists of three columns (sample Id , serotype, comments). Serotypes that do not match any reference are marked as “untypable”(v0.1.3).
Predicted Serotype: 23F Serotype predicted by ariba: 23F assembly from ariba has an identity of: 99.77 with this serotype Serotype Genetic Variant 23F allele wchA
In the detailed information you can see the finally predicted serotype as well as the serotypes that had the closest reference in that specific serogroup according to ARIBA. Furthermore you can see the sequence identity between the sequence assembly and the reference sequence.
- Case 1:
- SeroBA predicts ‘untypable’. An ‘untypable’ prediction can either be a real ‘untypable’ strain or can be caused by different problems. Possible problems are: bad quality of your input data, submission of a wrong species or to low coverage of your sequenced reads. Please check your data again and run a quality control.
- Case 2:
- Low alignment identity in the ‘detailed_serogroup_info’ file. This can be a hint for a mosaic serotpye.
- Possible solution: perform a blast search on the whole genome assembly
- Case 3:
- The third column in the summary.tsv indicates “contamination”. This means that at least one heterozygous SNP was detected in the read data with at least 10% of the mapped reads at the specific position supporting the SNP.
- Possible solution: please check the quality of your data and have a look for contamination within your reads
SeroBA is free software, licensed under GPLv3
Please report any issues to the issues page.
SeroBA: rapid high-throughput serotyping of Streptococcus pneumoniae from whole genome sequence data
Epping L, van Tonder, AJ, Gladstone RA, GPS Consortium, Bentley SD, Page AJ, Keane JA, Microbial Genomics 2018, doi: 10.1099/mgen.0.000186
A tutorial for SeroBA can be found here: