dereplication

Dereplication is a data processing approach aimed at identifying and removing redundant data within a set, thereby reducing duplication and speeding up analysis. It is used across disciplines, notably in natural products discovery and high-throughput sequencing, to avoid repeated effort on already known signals or highly similar data.

In natural products chemistry, spectral dereplication identifies known compounds early by comparing experimental spectra (typically MS and MS/MS, or NMR) against reference libraries. Common workflows involve obtaining spectra from extracts, matching them to databases such as GNPS, DNP, or AntiBase, and flagging matches that indicate previously described compounds. This helps prioritize novel molecules for isolation and characterization. Limitations include incomplete libraries, spectral isomerism, and potential misassignments when spectra are noisy or when closely related compounds yield similar signals.

In genomics and metagenomics, dereplication refers to reducing redundancy in sequence data or genome sets. Clustering methods and tools (for example CD-HIT, UCLUST, DNACLUST, or dRep) group near-identical sequences or genomes at defined identity thresholds to produce a non-redundant representative set. This minimizes computational load, improves comparative analyses, and clarifies downstream interpretation. Dereplication in this context must balance removing redundancy with preserving genetic diversity and rare variants.

Overall, dereplication is a foundational step in data curation, enabling efficient analysis while safeguarding important signals. Its implementation relies on appropriate reference resources, suitable similarity thresholds, and an awareness of the trade-offs between redundancy reduction and potential loss of distinct information.