通过对称删除查找实现闪电般快速的适应性免疫受体相似性搜索
Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookup
AI总结：
本文提出基于对称删除查找的快速算法，并实现SymScan和XTNeighbor软件，可在秒级完成百万序列的编辑距离搜索，显著加速免疫受体分析流程。
摘要：
个体的适应性免疫受体（AIR）库由于受体的抗原特异性而记录了免疫历史。读取这一记录需要计算方法从序列中推断受体功能，因为可能的受体-抗原对的多样性远超实验知识。识别具有相似序列从而可能具有相似功能的AIR是这些方法中常见的性能瓶颈。在此，我们基准测试了五种基于半径搜索Levenshtein邻居的算法方法的运行时间扩展性。我们展示了一种最初为拼写检查提出的对称删除查找方法特别具有可扩展性。我们介绍了SymScan和XTNeighbor，即实现该算法并行化变体的优化CPU和GPU软件。对于一百万个输入序列，这些工具能在几秒内识别出所有相差一个或两个编辑的序列对，比现有方法快数个数量级。我们演示了如何将对称删除查找作为T细胞受体元克隆发现和B细胞受体谱系鉴定中的预过滤步骤集成。我们的贡献有望极大加速现有分析流程，并支持大规模免疫测序数据的处理。
英文摘要：
An individual's adaptive immune receptor (AIR) repertoire records immune history due to the receptors' antigen specificity. 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 runtime scaling of five 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 introduce SymScan and XTNeighbor, optimized CPU and GPU software implementing a parallelized variant of the algorithm. For one million input sequences, these tools identify all sequence pairs that differ by one or two edits in seconds, orders of magnitude faster than existing approaches. We demonstrate how symmetric deletion lookup can be integrated as a pre-filtering step in T cell receptor metaclone discovery and B cell receptor lineage identification. Our contribution is poised to greatly accelerate existing analysis pipelines and enable processing of immunosequencing data at scale.
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