  An individual's adaptive immune receptor (AIR) repertoire records immune
history due to the exquisite antigen specificity of AIRs. Reading this record
requires computational approaches for inferring receptor function from
sequence, as the diversity of possible receptor-antigen pairs vastly outstrips
experimental knowledge. Identification of AIRs with similar sequence and thus
putatively similar function is a common performance bottleneck in these
approaches. Here, we benchmark the time complexity of five different
algorithmic approaches to radius-based search for Levenshtein neighbors. We
show that a symmetric deletion lookup approach, originally proposed for
spell-checking, is particularly scalable. We then introduce XTNeighbor, a
variant of this algorithm that can be massively parallelized on GPUs. For one
million input sequences, XTNeighbor identifies all sequence neighbors that
differ by up to two edits in seconds on commodity hardware, orders of magnitude
faster than existing approaches. We also demonstrate how symmetric deletion
lookup can speed up search with more complex sequence-similarity metrics such
as TCRdist. Our contribution is poised to greatly speed up existing analysis
pipelines and enable processing of large-scale immunosequencing data without
downsampling.
