Estimating the diversity of immune repertoires¶
This tutorial covers processing and post-analysis of T-cell receptor (TCR) repertoire sequencing (RepSeq) data. General strategy for such data would be to de-multiplex samples and then map Variable (V), Diversity (D) and Joining (J) segments that are rearranged to form a mature TCR sequence. The complementarity determining region 3 (CDR3), a hypervariable TCR part that defines antigen specificity containing V-(D)-J junction, is then extracted. V, J and CDR3 regions are further assembled into clonotypes.
Datasets that are discussed here are amplicon libraries of TCR beta chain performed using 5’RACE that introduces unique molecular identifiers (UMI), short 12-bp tags of random nucleotides. Those are introduced at cDNA synthesis step and allow to trace reads back to their original cDNA molecules thus correcting for sequencing errors and allowing to count the number of starting molecules.
The following software tools are used for data processing and analysis:
- MIGEC for de-multiplexing data UMI-based error correction, mapping V(D)J segments
- MITCR for V(D)J mapping and frequency-based error correction
- VDJtools for computing diversity estimates and rarefaction analysis
Those tools are developed specifically for analysis of RepSeq, although some modules of MIGEC can be applied to broad range of UMI-tagged data.
The `repseq-tutorial <https://github.com/mikessh/repseq-tutorial>`__repository contains software binaries, datasets and shell scripts listing all commands used in this tutorial. Some plotting and statistics is done in R.
Some additional introduction slides can be accessed here.
Table of Contents¶
- Part I: Processing, error correction and diversity estimation
- Part II: Comparing diversity estimation methods
- Part III: Diversity and similarity of repertoires