Sequencing by Emergence (SEQE) is a new single-molecule nucleic acid (DNA/RNA) sequencing technology that estimates sequence as an emergent property of the binding and localization of a repertoire of short oligonucleotide probes. SEQE promises to deliver accurate, ultra-long, haplotype-phased reads at the whole genome-scale for very low cost within 10 minutes. The data SEQE generates requires entirely new inference techniques. In this paper we introduce a probabilistic model of the SEQE measurement process and an algorithm that estimates sequence by solving a convex relaxation of the corresponding maximum likelihood problem. We demonstrate the effectiveness of our algorithm on a variety of simulated datasets.
@article{arxiv.2103.10477,
title = {Sequencing by Emergence: Modeling and Estimation},
author = {Nicholas Boyd and Samuel Woodhouse and Kalim Mir},
journal= {arXiv preprint arXiv:2103.10477},
year = {2021}
}