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 SAMtools. 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)
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, bwa, minimap2, bowtie2, samtools, bcftools, tqdm and parallel.
You should also install the pathogen-profiler library found here.
To install tbprofiler run the following code:
git clone firstname.lastname@example.org:jodyphelan/TBProfiler.git cd TBProfiler python setup.py install 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
The first argument indicates the analysis type to perform. At the moment we currently only support the calling of small variants.
Quick start example
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
Running with an existing BAM file
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 do this by either:
- Creating the new database files using the
--seqname NC_000962.3option from the
parse_db.pyscript in the tbdb repo. Then loading it using
tb-profiler load_library /path/to/lib.
- Or applying a quick fix to replace references to "Chromosome" in all existing database files e.g using
sed -i 's/Chromosome/NC_000962.3/' `python -c "import sys;print(getattr(sys, 'base_prefix', getattr(sys, 'real_prefix', sys.prefix)))"`/share/tbprofiler/tbdb* && samtools faidx `python -c "import sys;print(getattr(sys, 'base_prefix', getattr(sys, 'real_prefix', sys.prefix)))"`/share/tbprofiler/tbdb.fasta
The results from numerous runs can be collated into one table using the following command:
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
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.
Under the hood
The pipeline searches for small variants and big deletions associated with drug resistance. It will also report the lineage.
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:
- Drug resistance classes (Sensitive, drug-resistant, MDR, XDR)
- Drug resistance calls for individual drugs, were filled circles represent resistance.
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