Dec 28, 2022
Establishing processes to capture standardized contextual data
This protocol is a draft, published without a DOI.
- Paul Lorenzo A Gaite1,
- Dr Ritchie Mae T Gamot1,
- Prof Lyre Anni E Murao1,2
- 1PGC Mindanao;
- 2UP Mindanao
Protocol Citation: Paul Lorenzo A Gaite, Dr Ritchie Mae T Gamot, Prof Lyre Anni E Murao 2022. Establishing processes to capture standardized contextual data. protocols.io https://protocols.io/view/establishing-processes-to-capture-standardized-con-cghztt76
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: September 13, 2022
Last Modified: December 28, 2022
Protocol Integer ID: 69913
Abstract
One of the obstacles of biosurveillance is the non-standard recording and storage of contextual data. This is a problem especially when establishing a national biosurveillance program made up of different public health laboratories having differing data collection standards (e.g. each laboratory requiring or recording a different set of contextual data categories). As a result, timely submission to databases is hampered due to additional time to request additional pieces of metadata from sampling laboratories. To circumvent this problem, a need for standard contextual data collection and storage system should be in place. For this reason PHA4GE has developed a contextual data standard package.
Abstract/Introduction
One of the obstacles of biosurveillance is the non-standard recording and storage of contextual data. This is a problem especially when establishing a national biosurveillance program made up of different public health laboratories having differing data collection standards (e.g. each laboratory requiring or recording a different set of contextual data categories). As a result, timely submission to databases is hampered due to additional time to request additional pieces of metadata from sampling laboratories. To circumvent this problem, a need for standard contextual data collection and storage system should be in place. For this reason PHA4GE has developed a contextual data standard package.
The sections below show the process of establishing a contextual data standard for PGC Mindanao. The original PGC Mindanao workflow for the handling of contextual data is outlined (Section 2), particularly the collection of contextual data (Section 2.1), storage of contextual data (Section 2.2), stewardship of contextual data (Section 2.3), and preparation of contextual data for submission to GISAID (Section 2.4). The PHA4GE workflow for handling contextual data, as applied by PGC Mindanao is also outlined (Section 3).
PGC Mindanao workflow
PGC Mindanao did not have a defined workflow for handling contextual data. The subsections below outline the activities done in connection to the collection, storage, and stewardship of contextual data, as well as preparation of contextual data for submission to GISAID.
Collection of Contextual Data:
The contextual data associated with the patient samples (e.g. anonymised patient data) were collected by the Sub-National Laboratories (SNLs). The collected contextual data were then collated in template spreadsheets (these contain both required and optional metadata fields for a GISAID submission) by the SNLs and sent back through e-mail to PGC Mindanao (Figures 1 and 2).
Figure 1. Instructions to SNL for inputting contextual data from sequenced samples
Figure 2. Spreadsheet containing actual contextual data from sequenced samples as inputted by SNL
Storage of Contextual Data:
Contextual data are stored within workstations at PGC Mindanao. Part of the protocol from the previous project entailed upload of contextual data to the REDCap database.
Stewardship of Contextual Data:
As contextual data is sensitive information, at the beginning of the collaboration between the previous project and PGC Mindanao an agreement was created and a specific clause was created to outline how data will be shared. Specifically, the source SNL and the project should be informed and permissions should be acquired when sharing data to third-party projects including PHA4GE.
Preparation of Contextual Data for Submission to GISAID:
The previous project involved collection of all contextual data in a REDCap database. Various scripts were created and provided by the previous project to transfer data from the database to spreadsheets. The GISAID format was followed for uploading to GISAID database. Please refer to protocol "Submission of sequence and contextual data to GISAID, INSDC repositories, or other databases" for details on this process.
PHA4GE workflow
PHA4GE has developed and provided a contextual data specification package (e.g. submission template and scripts) that can facilitate collation of contextual data and eventual upload of consensus sequences and corresponding contextual data to databases.
This section describes the input of contextual data to the PHA4GE contextual data template spreadsheet (Section 3.1), and usage and some feedback on the PHA4GE contextual data template spreadsheet (Section 3.2).
Input of contextual data to the PHA4GE contextual data template spreadsheet:
Contextual data collected from the samples (please refer to Section 2.1 - "Collection of Contextual Data" of this protocol for details) were inputted into the PHA4GE contextual data template spreadsheet (Figure 3). Required fields were inputted.
Figure 3. Contextual data from a sequencing batch inputted into PHA4GE contextual data template spreadsheet
Use of PHA4GE contextual data template spreadsheet
Usage of the contextual data template has proven to be useful for our purposes since this can standardize data requirements, submission, and storage within the center, as well as possible sharing of data with other sequencing laboratories hence facilitating ease of collaboration. For example, the naming convention provided by the package is now being used by the center to name sequenced samples. The package is also easy to use since the instructions are clear and intuitive. The fields present in the package are also the same as those required in submission to databases such as GISAID and NCBI GenBank hence is useful for facilitating submission to these databases. It is noted that the fields contained in the template sufficiently cover information that are needed for public health activities.