This repository contains a complete rewrite of the web version of TBProfiler, described here. It allows the use of profiling through a command line interface and contains some additional functionality such as the ability to process minION data.
The pipeline aligns reads to the H37Rv reference using bowtie2, BWA or minimap2 and then calls variants using bcftools. These variants are then compared to a drug-resistance database. We also predict the number of reads supporting drug resistance variants as an insight into hetero-resistance (not applicable for minION data)
This page has all the info you need to get started, however additional (and more organised!) documentation is available here. I have also tried create a (very!) basic translation to other languages: 🇧🇷🇳🇱. Please contact me if you would like to improve a translation or add a new one!
TBProfiler is under constant rapid development. If you plan to use the program in your work please make sure you are using the most up to date version! Similarly, the database is not static and is continuously being improved so make sure you are using the most latest version. If you use TBProfiler in your work please state the version of both the tool and the database as they are deveoped independantly from each other.
Conda can function as a package manages are is available here. If you have conda make sure the bioconda and conda-forge channels are added:
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
Then you can install tb-profiler and all of its dependancies from the bioconda channel:
conda install -c bioconda tb-profiler
conda install -c bioconda tb-profiler samtools=1.9=h7c4ea83_11 ncurses=6.1=h0a44026_1002
It is possible to install manually. The following pre-requisites will be needed at runtime: trimmomatic (>=v0.38), bwa (>=v0.7.17), minimap2 (>=v2.16), bowtie2 (>=v2.3.5), samtools (>=v1.9), bcftools (>=v1.9), GATK (>=v4.1.4.0), freebayes (>=v1.3.2), tqdm (>=v4.32.2) and parallel (>=v20190522). The pipeline should work and has been tested on the program versions indicated in parentheses.
To install the library run the following code:
pip3 install git+https://github.com/jodyphelan/TBProfiler.git
mkdir `python -c "import sys; print(getattr(sys, 'base_prefix', getattr(sys, 'real_prefix', sys.prefix)));"`
tb-profiler update_tbdb
You should then be able to run using tb-profiler
The first argument indicates the analysis type to perform. At the moment we currently only support the calling of small variants.
Run whole pipeline:
tb-profiler profile -1 /path/to/reads_1.fastq.gz -2 /path/to/reads_2.fastq.gz -p prefix
The prefix is usefull when you need to run more that one sample. This will store BAM, VCF and result files in respective directories. Results are output in json and text format.
mkdir test_run; cd test_run
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR166/009/ERR1664619/ERR1664619_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR166/009/ERR1664619/ERR1664619_2.fastq.gz
tb-profiler profile -1 ERR1664619_1.fastq.gz -2 ERR1664619_2.fastq.gz -t 4 -p ERR1664619
cat results/ERR1664619.results.json
By using the -a option you can specify to use an existing BAM file instead of fastq files. Warning!!!: The BAM files must have been created using the version of the genome as the database which can be downloaded here. Confusingly, this genome has multiple accession numbers (ASM19595v2,NC_000962.3,GCF_000195955.2, etc...). If you believe your reference to be the exact same sequence (length should be 4411532) then you can create a database with the same sequence name as used in your BAM file. For example if your sequence name is "NC_000962.3" you can run
tb-profiler update_tbdb --seqname NC_000962.3
The results from numerous runs can be collated into one table using the following command:
tb-profiler collate
This will automatically create a number of colled result files from all the individual result files in the result directory. If you would like to generate this file for a subset of the runs you can provide a list with the run sames using the --samples flag. The prefix for the output files is tbprofiler by default but this can be changed with the --prefix flag.
The collate function extracts the drug-resistance mutations and lineage, however you may want to extract more features that are present in the individual json result files. I have created a little tutorial on how to do this here.
TBProfiler ships with a default database. The development of the mutation library is hosted on the tbdb repository. Please visity this repo if you would like to get involved in the database or would like to modify and create your own.
If you would like to use an altered database you can load the config file produced by parse_db.py as such:
tb-profiler load_library [config.json]
It is possible run TBProfiler on another reference genome. Although there is currently no helper tool to create the databases for other references automatically, checkout the tbdb repository to find out more about what you need.
The pipeline searches for small variants and big deletions associated with drug resistance. It will also report the lineage. By default it uses Trimmomatic to trim the reads, BWA (or minimap2 for nanopore) to align to the reference genome and bcftools (or GATKv4/freebayes) to call variants. Here is an example schematic for illumina paried end data:
Several files are produced by the tb-profile collate function. Among these are several config files that can be used with iTOL to annotate phylogenetic trees. A small tree and config files have been placed in the example_data directory. To use navigate to the iTOL website and upload the tbprofiler.tree file using the upload button on the navigation bar. Once this has been uploaded you will be taken to a visualisation of the tree. To add the annotation, click on the '+' button on the lower right hand corner and select the iTOL config files. You should now see a figure similar to the one below. The following annotations are included:
- Lineage
- Drug resistance classes (Sensitive, drug-resistant, MDR, XDR)
- Drug resistance calls for individual drugs, were filled circles represent resistance.
If you plan to use TB-Profiler in your work please cite the paper and include both the version of tb-profiler and the database.
Please raise them using the Issues page.
Will populate this once we get some frequently asked questions!
- Add in capability to perform basic phylogenetic functions
- Add in levels of resistance to mutations