Skip to main content
eScholarship
Open Access Publications from the University of California

Computational Tools for Immune Repertoire Characterization and Primer Set Design

  • Author(s): Yu, Jane
  • Advisor(s): Song, Yun S
  • et al.
Abstract

The enormous decrease in the cost of genomic sequencing over the past two decades has enabled researchers to revisit previously unaddressable questions in sequence analysis. However, this boom of genomic information has introduced new sets of problems that often demand computationally efficient methods. In this work, we describe computational tools for two such settings involving large-scale genomic data: 1) estimating copy number and allelic variation in two highly complex gene families, and 2) selective sequencing of a target genome in a complex DNA sample.

We first describe a method that takes short reads from high-throughput sequencing and characterizes both copy number and allelic variation in the IGHV and TRBV loci. These two loci can vary extensively between individuals in copy number and contain genes that are highly similar, making their analysis technically challenging. Additionally, we have conducted the first study of a globally diverse sample of hundreds of individuals in these two loci from over a hundred populations. In addition to providing insight into the different evolutionary paths of the IGHV and TRBV loci, our results are also important to the adaptive immune repertoire sequencing community, where the lack of frequencies of common alleles and copy number variants is hampering existing analytical pipelines.

In our second problem setting, we describe SOAPswga, an optimized and parallelized pipeline for primer design in the context of selective amplification. Unlike previous heuristic-based methods, SOAPswga uses machine learning methods to evaluate both individual primers and primer sets. Additionally, rather than brute force search for primer sets, such as in predecessor methods, SOAPswga uses branch-and-bound principles to pursue only the most promising sets. These optimizations, including the parallelization of each step, allow for a huge decrease in runtime from the order of weeks to minutes. We also discuss the results of our pipeline applied to the selective amplification of Mycobacterium tuberculosis in a sample of human blood. Lastly, we expand on the importance of this work, and in general, its potential usefulness to any setting consisting of targeted sequencing.

Main Content
Current View