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baRcodeR with PyTrackDat: Open-source labelling and tracking of biological samples for repeatable science

Yihan Wu, David R. Lougheed, Stephen C. Lougheed, Kristy Moniz, Virginia K. Walker, View ORCID ProfileRobert I. Colautti
doi: https://doi.org/10.1101/457051
Yihan Wu
Biology Department, Queen’s University, 116 Barrie St., Kingston, ON K7L 3N6 Canada
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David R. Lougheed
Biology Department, Queen’s University, 116 Barrie St., Kingston, ON K7L 3N6 Canada
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Stephen C. Lougheed
Biology Department, Queen’s University, 116 Barrie St., Kingston, ON K7L 3N6 Canada
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Kristy Moniz
Biology Department, Queen’s University, 116 Barrie St., Kingston, ON K7L 3N6 Canada
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Virginia K. Walker
Biology Department, Queen’s University, 116 Barrie St., Kingston, ON K7L 3N6 Canada
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Robert I. Colautti
Biology Department, Queen’s University, 116 Barrie St., Kingston, ON K7L 3N6 Canada
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  • ORCID record for Robert I. Colautti
  • For correspondence: robert.colautti@queensu.ca
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Abstract

Repeatable experiments with accurate data collection and reproducible analyses are fundamental to the scientific method but may be difficult to achieve in practice. Several flexible, open-source tools developed for the R and Python coding environments aid the reproducibility of data wrangling and analysis in scientific research. In contrast, analogous tools are generally lacking for earlier stages, such as systematic labelling and processing of field samples with hierarchical structure (e.g. time points of individuals from multiple lines or populations) or curating heterogenous data collected by different researchers over several years. Such tools are critical for modern research given trends toward globally distributed collaborators using higher-throughput technologies. As a step toward improving repeatability of methods for the collection of biological samples, and curation of biological data, we introduce the R package baRcodeR and the PyTrackDat pipeline in Python. The baRcodeR package provides tools for generating biologically informative, hierarchical labels with digitally encoded 2D barcodes that can be printed and scanned using low-cost commercial hardware. The PyTrackDat pipeline integrates with baRcodeR output to build a web interface for sample management and tracking along with data collection and curation. We briefly describe the application of principles from baRcodeR and PyTrackDat in three large research projects, which demonstrate their value to (i) help document sampling methods, (ii) facilitate collaboration and (iii) reduce opportunities for human errors and omissions that could otherwise propagate through downstream data analysis to compromise biological inference.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 30, 2018.
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baRcodeR with PyTrackDat: Open-source labelling and tracking of biological samples for repeatable science
Yihan Wu, David R. Lougheed, Stephen C. Lougheed, Kristy Moniz, Virginia K. Walker, Robert I. Colautti
bioRxiv 457051; doi: https://doi.org/10.1101/457051
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baRcodeR with PyTrackDat: Open-source labelling and tracking of biological samples for repeatable science
Yihan Wu, David R. Lougheed, Stephen C. Lougheed, Kristy Moniz, Virginia K. Walker, Robert I. Colautti
bioRxiv 457051; doi: https://doi.org/10.1101/457051

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