This repository contains a complete rewrite of the web version of TB-Profiler, 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)

Documentation

This page has all the info you need to get started, however additional (and more organised!) documentation is available here. We have also have some basic translations listed below. Please contact us if you would like to improve a translation or add a new one!

Short readme

• EN
• FR (@justicengom)

In depth documentation

• EN
• PT (@emilyncosta)
• FR (@justicengom)

Keeping up to date

TB-Profiler 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 TB-Profiler in your work please state the version of both the tool and the database as they are developed independantly from each other.

Installation

Conda

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:

Linux

conda install -c bioconda tb-profiler

macOS

conda install -c bioconda tb-profiler 

If you have a new mac (M1/M2) you will need to open your terminal using Rosetta. Most bioinformatics tools are built to run on intel chips (x86) and won’t work on M1/M2 chips (ARM). Rosetta will emulate an x86 environment so you can still run your favourite bioinformatics tools. Do set up your new computer you should

1. Open the Finder application and go to “Applications”
2. Right click on your terminal application
3. Select “Open using Rosetta”
4. Set up conda and install tb-profiler as shown above

Manually

It is possible to install manually, although I would highly recommend installing through conda. The following dependancies will be needed at runtime: trimmomatic (>=v0.38), bwa (>=v0.7.17), minimap2 (>=v2.16), samtools (>=v1.12), bcftools (>=v1.12), freebayes (>=v1.3.5), tqdm (>=v4.32.2), parallel (>=v20190522), samclip (>=v0.4.0) and snpEff (>=v5.0.0)_. 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
pip3 install git+https://github.com/jodyphelan/pathogen-profiler.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

Usage

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.

Example run

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 --txt
cat results/ERR1664619.results.txt

Output format

Text and CSV

A .json formatted file will be produced by default for each sample you run and will be placed in a folder named results. This format is easy to load for software but is not very human-readable. There are also a number of optional formats which are intended to be interpreted by humans. Add --txt or --csv to your command to produce a tab-separated or csv-separated file containing most of the same information as in the .json file. You can also produce your own custom format report by supplying a template file written using the jinja templating language. You can do this by supplying your template using --output_template /path/to/template.

Word (docx) output

You can also create a docx output which is a versitile approach to create professional-looking reports that can include tables, images and anything you can do in Word. It works in a similar way by supplying a docx template with jinja variables that will be filled in using the sample data. To create the output run --docx /path/to/template.docx. An example template can be found here.

Whole genome analysis

By default, tb-profiler only analyses drug resistance candidate genes. It is also possible to perform variant calling across the whole genome using the --call_whole_genome argument. When this is enabled it is also possible to compare your sample to previous runs to find those which are close to your new sample. Close samples are found using a SNP distance cutoff which is enabled using the --snp_dist argument. Close samples are stored in the json output and are also found in the text report. Please note that the --snp_dist function is experimental and could produce unexpected results.

Spoligotyping

Experimental spoligotyping can be performed by adding --spoligotype to the command. This is enabled for bam and fastq input.

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 run the following command with your reference fasta file:

tb-profiler update_tbdb --match_ref /path/to/your/reference/fasta

Summarising runs

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.

If tb-profiler has been run using the --snp_dist argument, the collate function will also generate a transmission graph in json format that can be visualised by dragging and dropping the file into this site

Writing your own summary scripts

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.

Mutation database

TB-Profiler 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 download the tbdb repo, make the required changes and run the following code from within the tbdb repo directory:

tb-profiler create_db --prefix <new_library_name>
tb-profiler load_library <new_library_name>

Non-H37Rv databases

It is possible run TB-Profiler 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. 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:

ITOL files

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, RR-TB, HR-TB, MDR-TB, Pre-XDR-TB, XDR)
• Drug resistance calls for individual drugs, were filled circles represent resistance.

If you would like the ITOL config files to be generated add the itol argument to the collate function.

tb-profiler collate --itol

Citation

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.

Issues

Please raise them using the Issues page. Please see the contributing guidelines and the code of conduct.

FAQ

Q. How can I use tb-profiler to produce a phylogenetic tree?

A. At the moment TB-Profiler does not provide any functionality to actually create the tree. The config files for itol are generated, however the tree must be created with other software. It is planned for future releases, however it might take some time to be fully developed. I would suggest alternative software for the phylogenetic reconstruciton step (such as snippy + iqtree)

Future plans

Please see the roadmap to view future plans as well as features currently in development