Newly updateĪs we have developed another tools DeRR for Single-Cell sequencing data, it is no longer recommend using CATT to do such analysis. After error correction, CATT employs a Bayes classification algorithm to assess the reliability of CDR3 sequences (differ from other protein-coding genes). (C) Annotation and confidence assessment. 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. The motif criteria from IMGT project were employed to identify putative CDR3 sequences in directly found and assembled CDR3 sequences. Potential CDR3 sequences were reconstruct by k-1 overlapped k-mers using k-mer frequency based greedy-feasible-flow algorithm. All reads are aligned to V and J reference genes to select out candidate (brown) reads for micro-assembly. Easy installation: Using Docker, CATT can be installed in any platform in one command.Based on specially designed assembly, CATT could recover more CDR3 sequences than other tools even from short reads. Precisely and efficiently extract T cell CDR3 sequences from most types of TCR containing raw sequencing data.One-button: CATT employs a totally data-driven algorithm, which is self-adaption to input data without any additional parameters.
CATT is an ultra-sensitive and accurate tool for characterizing T cell receptor sequence in bulk and single cell TCR-Seq and RNA-Seq data.
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