# 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.

## Key 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.