CATT

What is CATT

An ultra-sensitive and accurate tool for characterizing T cell receptor sequence in bulk and single cell TCR-Seq and RNA-Seq data. The tool can be found in:

Overview

CATT(CharActerzing TCR repertoires) is a tool for detecting CDR3 sequences from any TCR containing raw sequencing data (including TCR-seq, RNA-seq, scRNA-seq and any TCR contained sequencing data)

The tool has the following feature:

Overview of the core algorithm of CATT. (A) Candidate CDR3 detection. All reads are aligned to V and J reference genes to select out candidate (brown) reads for micro-assembly. Potential CDR3 sequences were reconstruct by k-1 overlapped k-mers using k-mer frequency based greedy-feasible-flow algorithm. (B) Error correction. The motif criteria from IMGT project were employed to identify putative CDR3 sequences in directly found and assembled CDR3 sequences. CATT eliminates the erroneous CDR3 sequences using a data-driven transition-probability learning algorithms, which retrieves the probability of erroneous CDR3s from the observed CDR3 distribution and merges erroneous sequences (red) according to transition rates based on frequency and Hamming distance between the root and leaf sequences in the same subgroup. (C) Annotation and confidence assessment. After error correction, CATT employs a Bayes classification algorithm to assess the reliability of CDR3 sequences (differ from other protein-coding genes).

Newly update

Current support profiling

Chain - RegionHomo spaniesMus musculusSus scrofa
TRA - CDR1
TRA - CDR2
TRA - CDR3
TRB - CDR1
TRB - CDR2
TRB - CDR3
IGH - CDR3

Installation

Docker Image (recommand)

CATT can be installed using Docker, Docker is a computer program that performs operating-system-level visualization. Using docker, users could easily install CATT and run CATT in virtual environment.

Steps:

  1. Download and install Docker, recommend from homepage (required ubuntu ≥ 14.04 )
Enterprise Container Platform | Docker
In the Forrester New Wave ™: Enterprise Container Platform Software Suites, Q4 2018 report, Docker was cited as a leader in enterprise container platform category with Docker and our Docker Enterprise Container platform receiving a "differentiated" rating in eight criteria including runtime and orchestration, security, image management, user experience, vision and more.
https://www.docker.com/
Docker homepage

2. Download latest CATT docker image

docker pull guobioinfolab/catt:latest

This command will pull down the CATT from the docker hub (about ~5min needed to download the image, depend on the network speed). When execution is done, CATT have been installed successfully.

Source code (Not well tested)

CATT is written in Julia and Python. To install CATT from source code, download the latest version of CATT and accompanying files from Github

git clone https://github.com/GuoBioinfoLab/CATT.git

Pre-requisites

To run CATT stand-alone, some packages and softwares are needed:

💡
The current version of BioSequence.jl seems have a bug which may report `UndefVarError: x not defined` during the runtime. The solution is modify the file `.julia/packages/BioSequences/k4j4J/src/composition.jl` Some users reported that install the package through GitHub could avoid it.

//around Line 80
for mer in iter
    counts[mer.fw] = get(counts, mer.fw, 0) + 1
end

Configure

  1. Set the parameters in reference.jl
    • ref_prefix: The path of resource folder (contain), like /home/XXX/catt/resource
    • bwa_path: The executive file path of bwa, like /usr/bin/bwa. If the bwa is in the $PATH, this can be simply set as bwa
    • Samtools_path: The executive file path of samtools
  1. Set the parameters in catt.jl
    • In Line 18-20, the absolute path to file reference.jl, Jtool.jl
  1. Set the parameters in catt
    • In Line 63, the absolute path to catt.jl
    • In Line 64, the absolute path to julia
  1. Make catt executable and add it to environment variable
chmod u+x catt
#add catt to ~/.bashrc
export PATH="/path/to/catt:$PATH"
#Change the python execution in catt Line 1 if not using the system python

Test sample

We prepared a sample file (testSample.fq, can be downloaded from Github) for user test their installation and illustrating the CATT usage. Enter the folder contain the sample file and then run command:

docker run -it --rm -v $PWD:/output -w /output -u $UID guobioinfolab/catt \
catt -f testSample.fq -o testSampleOutput -t 2

If all goes well, a CSV format file with name testSampleOutput.TRB.CDR3.CATT.csv should be created in current folder.

💡
Docker command explain: —it and --rm flag are used to set docker container attribute, which are not important here. -v will mounts the specified directory on the host inside the container at the specified path. In this case, we mounting the $PWD (current directory, for linux user only) to /output directory inside the CATT image. We then output the results to this directory as -o /output/testSampleOutput. As /output is same directory as $PWD, you can find the result in the $PWD directory outside the CATT image

Usage

To integrate usage, users who install the CATT from Docker should always add following settings before command:

docker run -it --rm -v $PWD:/output -w /output -u $UID guobioinfolab/catt

#for example, from
catt -f testSample.fq -o testSampleOutput -t 2
#to
docker run -it --rm -v $PWD:/output -w /output guobioinfolab/catt \
catt -f testSample.fq -o testSampleOutput -t 2

Where $PWD is the path of folder contain your input data (absolute path, or just $PWD if input file is in current folder)

Basic

# For single-end input:
catt [option] -f inputFile -o outputName

# For paired-end input:
catt [option] --f1 inputFile1 --f2 inputFile2 -o outputName

# For bam input
catt [option] --bam -f inputFile -outputName

option:

Advance

Multiple input files

Parameter -f (for paired-end input is --f1 and --f2) and -o can accept multiple input files like:

catt [option] -f inputFile1 inputFile2 inputFile3 ... inputFileN \
-o outputName1 outputName2 outputName3 ... outputNameN

The input and output should be one-to-one correspondence.

10X format data

As 10X sequencing becoming popular nowadays, we add the support for processing 10X scTCR-Seq data (In our evaluation, current 10X scRNA-seq is not suitable for TCR profiling, the reads number and length is under the minimum requirements). CATT will automatically read data, trim UMI, and do TCR profiling. (only support for the current version scTCR toolkit, 150bp paired-end, the first 16bp of Read1 is UMI and barcode sequence). CATT will output TCR for every cell (every barcode), in which some might be empty cell or derived from barcode error. User need to filter out such cells themself. 

catt [option] --tenX --f1 R1 --f2 R2 -o outputName

Output explain

The output file of CATT is a CSV format file named like {prefix}_{chain}_{region}.CATT.csv . The file contain 7 columns:

FAQ

Term of use

CATT is an sensitive and accurate tool for characterizing T cell receptor sequence in bulk and single cell TCR-Seq and RNA-Seq data, maintained by An-Yuan Guo Bioinformatics Lab (Guo Lab). Guo Lab may, from time to time, update the content on http://bioinfo.life.hust.edu.cn/CATT and https://github.com/GuoBioinfoLab/CATT. Guo Lab makes no warranties or representations, express or implied, with respect to any of the Content, including as to the present accuracy, completeness, timeliness, adequacy, or usefulness of any of the Content. By using this website, you agree that Guo Lab will not be liable for any losses or damages arising from your use of or reliance on the Content, or other websites or information to which this website may be linked.

CATT is freely accessible for research use in an academic setting. You may view the Content solely for your own personal reference or use for research in an academic setting. All academic research use of the Content must credit CATT as the source of the Content and reference these Terms of Use; outside of scientific publication, you may not otherwise redistribute or share the Content with any third party, in part or in whole, for any purpose, without the express permission of Guo Lab.

Unless you have signed a license agreement with Guo Lab, you may not use any part of the Content for any other purpose, including:

use or incorporation into a commercial product or towards performance of a commercial service; research use in a commercial setting; use for patient services; or generation of reports in a hospital or other patient care setting. You may not copy, transfer, reproduce, modify or create derivative works of CATT for any commercial purpose without the express permission of Guo Lab. If you seek to use CATT for such purposes, please request the license which best describes your anticipated use of CATT below:

Credit

Please cite our paper when using CATT

Si-Yi Chen, Chun-Jie Liu, Qiong Zhang, An-Yuan Guo, An ultrasensitive T-cell receptor detection method for TCR-Seq and RNA-Seq data, Bioinformatics, , btaa432, https://doi.org/10.1093/bioinformatics/btaa432

Copyright Guo Lab , Biomedical Big Data Center , West China Hospital ,Sichuan University , China