Abstract

Most sequencing data analyses start by aligning sequencing reads to a linear reference genome, but failure to account for genetic variation leads to reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the reference flow alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance but with 14% of the memory footprint and 5.5 times the speed.

Highlights

  • Sequencing data analysis often begins with aligning reads to a reference genome, with the reference represented as a linear string of bases

  • Simulations for major-allele reference flow We studied the efficacy of a strategy we call “MajorFlow,” which starts by aligning all reads to the global major reference

  • We first showed that a 2-pass method using superpopulation major-allele references (MajorFlow) outperformed both a standard linear reference and individual major-allele references

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Summary

Introduction

Sequencing data analysis often begins with aligning reads to a reference genome, with the reference represented as a linear string of bases. Linearity leads to reference bias: a tendency to miss alignments or report incorrect alignments for reads containing non-reference alleles This can lead to confounding of scientific results, especially for analyses concerned with hypervariable regions [2], allele-specific effects [3,4,5,6], ancient DNA analysis [7, 8], or epigenenomic signals [9]. Some studies suggest replacing the typical linear reference with a “major-allele” version, with each variant set to its most common allele This can increase alignment [16,17,18] and genotyping accuracy [19]. The majorallele reference is largely compatible with the standard reference (though indels can shift coordinates) and imposes little or no additional computational overhead

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