sequensing

Sequencing is a proposed framework that combines elements of sequencing and sensing to capture not only what components are present in a system, but also the order and timing in which they appear. It emphasizes the reconstruction of sequence information from continuous or discrete signals collected by sensors, with attention to temporal relationships and context. The concept is used across disciplines to describe efforts to monitor dynamic streams where the arrangement of events matters, such as genetic sequences as they are transcribed and translated, digital data streams, or environmental sensor networks.

Principles include time-resolved data capture, sequence alignment or ordering, error correction, and inference of missing steps. Techniques may involve time-stamped measurements, multiplexed sensing, single-molecule detection, and computational methods from sequence analysis and time-series modeling.

Applications are exploratory and include field-ready genomic sequencing in remote settings, live monitoring of industrial processes where the order of events influences outcomes, and studies of biological systems where temporal sequences underpin behavior, such as neural spike trains or signaling cascades. In data networks, sequensing concepts inform the design of systems that must preserve the order of packets or events while estimating sequences from incomplete observations.

Challenges include measurement noise, latency, scale, and the integration of heterogeneous data types. As a developing idea, sequensing lacks a single standard methodology, and its definitions vary by domain. Proponents emphasize transparent reporting of assumptions and uncertainty to support cross-disciplinary use. Related topics include sequencing, sensing, and time-series analysis.