PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
PHA4GE Quality Control Contextual Data Tags: Standardized Annotations for Sharing Public Health Sequence Datasets with Known Quality Issues to Facilitate Testing and Training
Version 1
: Received: 24 February 2023 / Approved: 2 March 2023 / Online: 2 March 2023 (07:50:05 CET)
How to cite:
Griffiths, E.; Mendes, C.I.; Maguire, F.; Guthrie, J.; Chindelevitch, L.; Karsch-Mizrachi, I.; Waheed, Z.; Cameron, R.; Holt, K.; Katz, L.; Petit III, R.; MacCannell, D.; Dave, M.; Oluniyi, P.; Nasar, M.I.; Raphenya, A.; Hsiao, W.; Timme, R. PHA4GE Quality Control Contextual Data Tags: Standardized Annotations for Sharing Public Health Sequence Datasets with Known Quality Issues to Facilitate Testing and Training. Preprints2023, 2023030037. https://doi.org/10.20944/preprints202303.0037.v1
Griffiths, E.; Mendes, C.I.; Maguire, F.; Guthrie, J.; Chindelevitch, L.; Karsch-Mizrachi, I.; Waheed, Z.; Cameron, R.; Holt, K.; Katz, L.; Petit III, R.; MacCannell, D.; Dave, M.; Oluniyi, P.; Nasar, M.I.; Raphenya, A.; Hsiao, W.; Timme, R. PHA4GE Quality Control Contextual Data Tags: Standardized Annotations for Sharing Public Health Sequence Datasets with Known Quality Issues to Facilitate Testing and Training. Preprints 2023, 2023030037. https://doi.org/10.20944/preprints202303.0037.v1
Griffiths, E.; Mendes, C.I.; Maguire, F.; Guthrie, J.; Chindelevitch, L.; Karsch-Mizrachi, I.; Waheed, Z.; Cameron, R.; Holt, K.; Katz, L.; Petit III, R.; MacCannell, D.; Dave, M.; Oluniyi, P.; Nasar, M.I.; Raphenya, A.; Hsiao, W.; Timme, R. PHA4GE Quality Control Contextual Data Tags: Standardized Annotations for Sharing Public Health Sequence Datasets with Known Quality Issues to Facilitate Testing and Training. Preprints2023, 2023030037. https://doi.org/10.20944/preprints202303.0037.v1
APA Style
Griffiths, E., Mendes, C.I., Maguire, F., Guthrie, J., Chindelevitch, L., Karsch-Mizrachi, I., Waheed, Z., Cameron, R., Holt, K., Katz, L., Petit III, R., MacCannell, D., Dave, M., Oluniyi, P., Nasar, M.I., Raphenya, A., Hsiao, W., & Timme, R. (2023). PHA4GE Quality Control Contextual Data Tags: Standardized Annotations for Sharing Public Health Sequence Datasets with Known Quality Issues to Facilitate Testing and Training. Preprints. https://doi.org/10.20944/preprints202303.0037.v1
Chicago/Turabian Style
Griffiths, E., William Hsiao and Ruth Timme. 2023 "PHA4GE Quality Control Contextual Data Tags: Standardized Annotations for Sharing Public Health Sequence Datasets with Known Quality Issues to Facilitate Testing and Training" Preprints. https://doi.org/10.20944/preprints202303.0037.v1
Abstract
As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower-quality datasets be made available for analysis and comparison alongside those of higher-quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse.The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies, community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA’s GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.
Keywords
quality control; reference datasets; public health; metadata; data sharing
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
