OLTA: Optimizing bait seLection for TArgeted sequencing

TL;DR

This paper introduces OLTA, a new algorithm that optimizes bait selection for targeted sequencing, reducing the number of baits needed while improving efficiency.

Contribution

The novel heuristic algorithm, OLTA, leverages problem similarities to minimize bait numbers with high utilization and low redundancy.

Findings

OLTA produces 6% and 11% fewer baits than existing methods on two major datasets.

The algorithm achieves the highest bait utilization and minimum redundancy across experimental settings.

Abstract

Targeted enrichment via capture probes, also known as baits, is a promising complementary procedure for next-generation sequencing methods. This technique uses short biotinylated oligonucleotide probes that hybridize with complementary genetic material in a sample. Following hybridization, the target fragments can be easily isolated and processed with minimal contamination from irrelevant material. Designing an efficient set of baits for a set of target sequences, however, is an NP-hard problem.

We develop a novel heuristic algorithm that leverages the similarities between the characteristics of the Minimum Bait Cover and the Closest String problems to reduce the number of baits to cover a given target sequence. Our results on real and synthetic datasets demonstrate that our algorithm, OLTA produces fewest baits for nearly all experimental settings and datasets. On average, it produces 6% and 11% fewer baits than the next best state-of-the-art methods for two major real datasets, AIV and MEGARES. Also, its bait set has the highest utilization and the minimum redundancy.

Our algorithm is available at github.com/FuelTheBurn/OLTA-Optimizing-bait-seLection-for-TArgeted-sequencing. Test data and other software are archived at doi.org/10.5281/zenodo.15086636.

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