Introduction about the history and (changing) purpose of biological specimen collections
These need to be examples where specimens are linked to digital data resulting in (applied) research results. I might be able to track down some instances of this in TreeBASE, but other examples are very welcome.
Transcript of "4. Rutger Vos, Naturalis Biodiversity Center"
Natural History Museums in theNGS Era: Coping with Data DelugeRutger Vos
Naturalis, the natural history museum• NBC is the national natural history museum of the Netherlands• Collection holds an estimated 37 million physical specimens• The collection places in the global top 5 by size Natural history museums, which evolved from cabinets of curiosities, played an important role in the emergence of professional biological disciplines and research programs. Particularly in the 19th century, scientists began to use their natural history collections as teaching tools for advanced students and the basis for their own morphological research.
Example: the ancient tomato genomeRetrieving the Golden 2-like transcription factor that was described in Science29 June 2012: Vol. 336 no. 6089 pp. 1711-1715 DOI: 10.1126/science.1222218
Example: DNA Barcoding TCMOrchids long since used inChina and now alsoincreasingly popular inEuropeRequire identification toensure they do not contain:• legally protected wild species• other species than mentioned on label (=adulteration)• life threatening poisons in case of toxic substitutes
Example: snake venom and medicine • Naturalis researchers are mapping the King Cobra genome • Studying its evolution in broader comparative context • Many proteins in venom might have medical applications
Informatics-intensive researchData types Types of analyses• Molecular data – • Assembly, alignment NGS/Sanger, genomes and barcodes • Phylogenetic analysis• Phenotype descriptions – • Analysis of natural OCR floras selection• GIS data sets – niche • Image analysis modeling • Natural language• Images – digitised collection processing specimens • Distribution modeling• Metadata – collection • Data integration metadata, barcoding, speciali sed databases
The ledger Expenses Income • Personnel • Funding • Sequencing − Fundamental • HPC infrastructure − Applied ("topsectors") − National, European • Consultancy • Product development
Where we (all?) are now Pressures Solutions • Sequencing volume • Spend less grows faster than computing power • Earn more • Research funding is • Work smarter dwindling • Suitably specialised personnel is hard to find
On spending less• Economies of scale, e.g. in consortiums or with neighbours − Sharing costly resources − Sharing development cost of generic tools − Sharing personnel• Changes in workflow, e.g. using free, command-line software instead of costly GUI − Savings on licenses (CLC, Geneious) − Workstations used by multiple users − Flexible workplaces
On earning more• Coordinated efforts to seek out public/private projects − "topsectors" − product development• Consultancy − barcoding, identification − primer design• Renting out resources
On working smarterBe generic• Dont repeat yourself or reinvent ever so slightly different wheels every time• Use generic, free platforms for data, knowledge and resource sharing (git, virtualization/cloud, wikis, galaxy, wetransfer)Empower people• Enable the architecture for remote collaboration and resource sharing• Train staff to use the most productive tools