  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.
