Sequencing by Emergence: Modeling and Estimation

TL;DR

Sequencing by Emergence (SEQE) is a novel DNA/RNA sequencing technology that uses probe binding to infer sequences rapidly and cost-effectively, requiring new inference methods for data analysis.

Contribution

This paper introduces a probabilistic model and a convex relaxation algorithm for sequence estimation in the SEQE technology, enabling effective analysis of its data.

Findings

Algorithm performs well on simulated datasets

Provides accurate sequence estimation

Enables ultra-long, haplotype-phased reads

Abstract

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.