Reconstructing Antibody Repertoires from Error-Prone Immunosequencing Reads

Abstract

Transforming error-prone immunosequencing datasets into antibody repertoires is a fundamental problem in immunogenomics, and a prerequisite for studies of immune responses. Although various repertoire reconstruction algorithms were released in the last three years, it remains unclear how to benchmark them and how to assess the accuracy of the reconstructed repertoires. We describe an accurate IgReC algorithm for constructing antibody repertoires from high-throughput immunosequencing datasets and a new framework for assessing the quality of reconstructed repertoires. Surprisingly, antibody repertoires constructed by IgReC from barcoded immunosequencing datasets in the blind mode (without using information about unique molecular identifiers) improved upon the repertoires constructed by the state-of-the-art tools that use barcoding. This finding suggests that IgReC may alleviate the need to generate repertoires using the barcoding technology (the workhorse of current immunogenomics efforts) because our computational approach to error correction of immunosequencing data is nearly as powerful as the experimental approach based on barcoding.

T‐cell receptor and B‐cell receptor repertoire profiling in adaptive immunity

“…Many computational approaches have been developed in the last decade to process and analyze HTS immune repertoire data , including software tools that can be used to extract TCR or BCR repertoires from raw sequencing reads (e.g., MiXCR, Vidjil, IMSEQ, IgReC ). This typically includes creating a list of clonotypes with determined CDR3, V, D, and J segments and their borders, as well as each clonotype's count and basic error correction, based on the collapsing of similar sequences.…”

A comparison of unamplified and massively multiplexed PCR amplification for murine antibody repertoire sequencing

“…Replication of the entire repertoire using 5’RACE would still require the use of multiple constant region primers, leading to the same multiplexing issues. Barcoding can have errors and chimeric reads making repertoires difficult to reconstruct . This latter issue is not a problem with our RNASeq data.…”

“…Barcoding can have errors and chimeric reads making repertoires difficult to reconstruct. 42 This latter issue is not a problem with our RNASeq data.…”

Immunoglobulin gene analysis as a tool for investigating human immune responses

“…Methods using paired end Illumina sequencing have advanced, however, allowing the capture of longer reads and sequencing of the full variable region and subclass isotyping with certain 2x 300 bp paired end sequencing methods 74. While Illumina offers unprecedented read counts, reconstructing libraries of antibody sequences, which can be in excess of 900 bp if determining subclass, becomes a bioinformatics conundrum, although there are now a large range of tools to facilitate this 75, 76, 77, 78. Paired end data can also be limited in ability to distinguish some somatic variants 79.…”

“…Transferring these more in‐depth analyses to high throughput methods is dependent on the accuracy of sequence information, and there is a sense of reluctance in the field to take clear biological inferences from what may not be the most precise data. HTS methods that incorporate UMIs and that provide multiple reads of the same unique sequence may be able to provide data which would overcome this reluctance and it may even be possible to correct sequencing data without the aid of UIDs with the appropriate algorithm such as IgReC 78. In addition, there are computational methods available for the construction of lineage trees 115, 116.…”