利用重/轻链配对信息推断B细胞克隆家族
Inference of B cell clonal families using heavy/light chain pairing information
摘要：
下一代B细胞受体（BCR）库测序已成为了解抗体介导的免疫应答的普遍工具：现在通常有大量编码BCR重链和轻链亚基的序列数据。然而，直到最近开发出保留重链/轻链配对信息的高通量方法，这些样品才含有关于哪些重链序列与哪些轻链序列配对的明确信息。分析这些BCR库样本的第一步是将序列分组为克隆相关的家族，其中每个家族源于单个重排事件。已经开发了许多实现这一点的方法，然而，到目前为止还没有一种方法充分利用新的配对信息。该信息可以显著提高聚类性能，特别是对于轻链。轻链传统上对于克隆家族推断具有挑战性，因为其低多样性和随之而来的具有不可区分的天然重排的非克隆家族的丰度。在这里，我们提出了一种方法，将这种配对信息的聚类过程中，以达到一个更准确的分区的数据克隆相关的家庭。我们还展示了两种方法，固定不完美的配对信息，这可能允许简化样品制备和增加测序深度。最后，我们描述了partis软件包（https：//github.com/psathyrella/partis）的其他几个改进。
英文摘要：
Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the light chain. The light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package (https://github.com/psathyrella/partis).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
