Managing Data Flow Through the Barcoding Pipeline

1. Managing Data Flow Through the Barcoding Pipeline Amy Driskell Laboratories of Analytical Biology (LAB) National Museum of Natural History Smithsonian Institution

2. What is the “pipeline”? LIMS Specimen Data Deposition Data QC

3. Outline • BEFORE the LIMS • LIMS • Data recorded • Exploring laboratory success/failure • Tracking project completion • Data QC • Criteria and data requirements • Checking for contamination and validity

4. Critical data management BEFORE specimen enters the laboratory pipeline • Data elements (“metadata”) necessary for laboratory processing: • Taxonomy, collection information, etc. IMPORTANT! • Assess laboratory successes/failures in light of this information • Tailor/change lab protocols

5. Careful Metadata Collection at Specimen Collection or Harvest • Metadata can be formatted at the beginning of a project (e.g. at specimen collection) to guarantee a smooth information transfer into the LIMS • Multiple sources for metadata: • Spreadsheets • Field Information Management Systems (FIMS) • Museum databases • Fusion tables

6. Rockin’ It “Old School” -- Spreadsheets • Modified BOLD specimen spreadsheet for use in field/museum • Additional fields desired by PIs • Modified easily to interface with multiple kinds of databases • 96-well format – 2D barcoded tubes, extraction plates • NOT directly connected to other databases, including LIMS

7. An Elegant Solution:BiocodeMoorea FIMS Actively connected to their LIMS http://biocode.berkeley.edu/

8. bioValidator – cleaning up the collection of metadata • Many aspects of metadata require specific formats: digital lat/long, meters, names • bioValidator enforces adherence to formatting and other rules • Photo matcher http://biovalidator.sourceforge.net/

9. Museum Collection Databases • Sampling directly from existing collections? • Some museum databases cannot link directly to lab-based information systems (LIMS) • Requires output from collection database, input into lab database – no automatic updates

10. Why? 1. Downstream insertion of data into other databases simplified2. Because metadata has important uses in the lab • Determine possible causes of failure: taxonomy, collection event, specimen age • adjust extraction or amplification protocols • design new primers – e.g. smaller fragments

11. Specimens enter the labMetadata enters the LIMS LIMS Specimen & Metadata Data Deposition Data QC

12. What is a LIMS? • An electronic lab “notebook” (aka database) to replace our traditional paper lab notebooks. • Tracks a specimen through lab processes from extraction through to barcode sequence completion (data QC may use external software). • Records every lab procedure. • Provides information to guide further lab efforts – success rates, “redo” lists • Records the physical location of extracts, etc.

13. My requirements for a LIMS • I want a system that records every piece of information about each specimen/extract for which I produce a barcode sequence. • I want my procedures and protocols to be transparent enough so that anyone can reproduce my results. • This includes my QC procedures. • Currently no good place to publish these data.

14. Data to be recorded • Extraction: protocol, digestion time, etc. • PCR: recipes, DNA [ ], cycling parameters, clean-up method (PCR machine, brand of enzyme, lot #) • Gel photos • Sequencing: recipe, clean-up, machine, etc. • Bonus: success or failure can be mapped back to any of these recorded values. Maybe the Taq was bad? Or the PCR machine needs repair?

15. A LIMS can be homegrown (like LAB’s barcoding LIMS, or SI’s plant barcoding LIMS) – relatively simple relational databases • Sophisticated, commercially produced – Geneious plug-in MooreaBiocode LIMS (plug-in is free) • Software updated and maintained • Plugs into the Geneious data analysis software http://software.mooreabiocode.org

16. Workflow

17. Mapping workflow elements to success

18. Tracking project progress & identifying next steps • Which specimens have completed barcodes? • Which specimens need additional labwork? • Which specimens should be abandoned? • Where are the original DNA extracts or tissue samples?

19. Project Progress

20. Raw data enters the QC process LIMS Specimen Data Deposition Data QC

21. Data QC • OUTSIDE of LIMS database • “Clean up” raw data – trim, examine quality • Assemble passed traces (“contig”) for a specimen • Examine/edit contigs • Check validity of resulting sequences

22. My data QC ethos • All criteria for each step of data analysis is recorded • For raw trace processing: trimming criteria, length and quality requirements, binning criteria • For assembly: assembly parameters, product length, etc. • Hand editing is minimized* • It would be possible for anyone to recreate the barcode sequence

23. Any DNA sequence analysis software can be used for data QC • Sequencher (Genecodes) & Geneious (Biomatters) • Trim ends of raw sequences with adjustable criteria, explore effects of trim criteria • Discard short or poor sequences • Assemble trimmed reads with stringent, but adjustable criteria • Output completed sequences • Geneious LIMS is plugged into the data analysis software • direct communication • binning* • Sequencher data must be exported and imported into LIMS

24. Data analysis Here are the traces. You can see some FIMS data in the document fields (eg identified by, tissue id). You will also notice a binning column (see the following slide)

25. Binning Automatic categorization of reads and assemblies • Change binning parameters, examine effects • Trimming and assembly dialog boxes similar

26. Final Steps:Is it a contaminant? Is it identified correctly? • A number of procedures for identifying contamination or incorrect identification • BLASTingdatabase of known contaminants; Genbank; BOLD • Quick and dirty assembly tests • NJ trees • Geneious taxonomy verification tool

27. Verify Taxonomy • BLASTs your sequences • Gets the NCBI taxonomy for the best hit(s) • Compares to the taxonomy from the FIMS

28. Good, clean, barcode sequences • Feed back into LIMS* • Monitor progress • Connect sequences and traces to specimen data • Prepare for output to databases Genbank or BOLD upload packages LIMS & Data QC Specimen Data Deposition

29. Positive Information Flow from field or museum to final data deposition • Collect metadata to flow easily into LIMS and other databases • Record all aspects of all laboratory procedures (LIMS) • Use LIMS system for reporting and protocol investigation, monitoring of project progress • Input information and data from QC procedures into LIMS* • LIMS output upload packages for public databases