CASCADE: A Custom-Made Archiving System for the Conservation of Ancient DNA Experimental Data - Dolle, Dirk; Fages, Antoine; Mata, Xavier; Schiavinato, Stéphanie; Tonasso-Calvière, Laure; Chauvey, Lorelei; Wagner, Stefanie; Der Sarkissian, Clio; Fromentier, Aurore; Seguin-Orlando, Andaine; Orlando, Ludovic (Frontiers in Ecology and Evolution, 2020)
The field of ancient genomics has undergone a true revolution during the last decade. Input material, time requirements and processing costs have first limited the number of specimens amenable to genome sequencing. However, the discovery that archaeological material such as petrosal bones can show increased ancient DNA preservation rates, combined with advances in sequencing technologies, molecular methods for the recovery of degraded DNA fragments and bioinformatics, has vastly expanded the range of samples compatible with genome-wide investigation. Experimental procedures for DNA extraction, genomic library preparation and target enrichment have become more streamlined, and now also include automation. These procedures have considerably reduced the amount of work necessary for data generation, effectively adapting the processing capacity of individual laboratories to the increasing numbers of analyzable samples. Handling vast amounts of samples, however, comes with logistical challenges. Laboratory capacities, equipment, and people need to be efficiently coordinated, and the progress of each sample through the different experimental stages needs to be fully traceable, especially as archaeological remains of animals or plants are often provided and/or handled by many different collaborators. Here we present CASCADE, a laboratory information management system (LIMS) dealing with the specificities of ancient DNA sample processing and tracking, applicable by large and small laboratories alike, and scalable to large projects involving the analysis of thousands of samples and more. By giving an account of the specimen’s progress at any given analytical step, CASCADE not only optimizes the collaborative experience, including real-time information sharing with third parties, but also improves the efficacy of data generation and traceability in-house.