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OLTA: Optimizing bait seLection for TArgeted sequencing
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OA
AI
DOI：10.1093/bioinformatics/btaf146.png)
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Abstract
En 中文
Motivation 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.Results 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.Availability and implementation 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.
Keywords:
PATHOGEN DETECTION
SOFTWARE PACKAGE
OLIGONUCLEOTIDES
ENRICHMENT
DESIGN
DNA