Leveraging NCBI Genomic Metadata for Epidemiological Insights: Example of Enterobacterales
This study shows how researchers can use data from NCBI to better understand patterns of infectious diseases like E. coli and Salmonella.
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- 1 Unlike NORS, any individual or institution can submit data to NCBI through NCBI's submission portal. Submissions are processed or rejected in accordance with NLM's GenBank and SRA Data Processing document .
- 2 While some articles exist that compare and contrast NCBI data to other outbreak sources , to the best of our knowledge, no research article has contrasted NORS and NCBI for epidemiological studies.
- 3 While data submitted to NCBI may be corrected in the instance of clerical errors of a typographical nature, several errors of type have been observed.
- 4 Hence, the present study demonstrates the capabilities of the developed Python package to uncover trends, seasonality, and patterns present in NCBI data that may be overlooked in NORS data.
Introduction
The National Outbreak Reporting System (NORS) is a system governed by the Center for Disease Control (CDC) as a modern-day means for tracking and reporting of enteric disease outbreaks. The NORS Dashboard was launched in 2009 as a publicly accessible platform for standardized outbreak reporting by public health departments .
NORS collects data on outbreaks driven by transmissions classi ed as foodborne, waterborne, person-to-person or via contact with animals.
Indeterminate/Unknown tranmission vector data are also reported to NORS.
The “V1” tag shows this is the rst assembled version of the code, and the authors plan to expand its functionality in future work.
A small sample of the data after pivoting is shown in the fourth compartment in Figure 1 .
Methodology
The data analysis pipeline begins with the selection of a genomic pathogen by the user, as shown in compartment one of Figure 1 . We also lter by submission date, to include only samples submitted to NCBI before 2024 to align our analysis with NORS, which only includes outbreak data up to the end of 2023.
Study Design
Because .jsonl les contain nested .json lines, they are not compatible with many data analysis programs.
Dataformat is a tool designed to convert the .jsonl format data to either a .tsv le, or an .xlsx le for analysis in data spreadsheet programs .
Additionally, categorizing the isolation source and host columns analysis was determined using a limited number of keyword searches.
Results & Findings
This includes characterizing the relative contributions of di erent transmission modes (e.g., person-toperson, foodborne) and etiologies such as norovirus in enteric outbreaks (person-to-person: 62%, foodborne: 24%, norovirus: 59%) , estimating transmissibility through reproduction number modeling in norovirus outbreaks (e.g., median R 0 of 2.75, with variation by setting and season) , and to estimate attribution of various pathogens in outbreaks sourced by various leafy greens as vectors of foodborne illness.
- This includes characterizing the relative contributions of di erent transmission modes (e.g., person-toperson, foodborne) and etiologies such as norovirus in enteric outbreaks (person-to-person: 62%, foodborne: 24%.
- Unlike NORS, any individual or institution can submit data to NCBI through NCBI’s submission portal. Submissions are processed or rejected in accordance with NLM’s GenBank and.
- While some articles exist that compare and contrast NCBI data to other outbreak sources , to the best of our knowledge, no research article has contrasted.
- The present study aims to ll this gap.
- While data submitted to NCBI may be corrected in the instance of clerical errors of a typographical nature, several errors of type have been observed.
Unlike NORS, any individual or institution can submit data to NCBI through NCBI’s submission portal. Submissions are processed or rejected in accordance with NLM’s GenBank and SRA Data Processing document .
While some articles exist that compare and contrast NCBI data to other outbreak sources , to the best of our knowledge, no research article has contrasted NORS and NCBI for epidemiological studies.
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Practical Applications
Although packages such as ShinyR could potentially be used for di erent outbreak database comparisons , there seem to be no Python packages for the purposes of comparing NCBI submission data with NORS data. For example, an entry in geographic location may read “river in jackson mississippi” of which we can extract the state name “Mississippi” and an environment of contamination “Water Source”.
40 60 80 100 US Average Temperature (°F) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 100 200 300 400 500 Salmonella Submissions and Outbreaks NORS Outbreaks NCBI Submissions (scaled by 1/100) US Average Temperature (b) 40 60 80 100 US Average Temperature (°F) Jan Feb Mar Apr May Jun.
(NCBI and NORS) Figures a) and b ) provide a follow up to the claim of having higher numbers of outbreaks during Summer, where it can be seen that for both pathogens the numbers are much higher during June with a possible delay in reporting the outbreaks which are pushed to July Maine Vermont Rhode.
Python Tool Development
Describes the development of the EpiNCBI_V1 Python Tool, including its functionality for selecting genomic pathogens and downloading relevant data from NCBI.
New Data Acquisition System
Introduces the EpiNCBI_V1 tool as a solution for accessing formatted genomic assembly data from NCBI, emphasizing its ease of use and availability on GitHub.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1: Pipeline for obtaining NCBI genomic data using the developed Python tool ‘EpiNCBI_V1’.. Illustrates the data processing steps involved in leveraging NCBI genomic data for epidemiological studies.
Limitations and Cautions
A useful limitation and caution is that this article summarizes the available paper text and extracted evidence; readers should consult the source paper before treating any interpretation as definitive.
The paper’s conclusions may depend on its source selection, definitions, assumptions, and the scope of its analysis, so follow-up reading is important.
Frequently Asked Questions
Reports are submitted on a voluntary basis by local, state, and territorial health agencies, as well as CDC o cials. Researchers have used NORS data extensively to understand the epidemiology of di erent infectious diseases. Similar to NORS, there are many applications.
Dataformat is a tool designed to convert the .jsonl format data to either a .tsv le, or an .xlsx le for analysis in data spreadsheet programs . To further optimize analysis, we want to have individual rows in our .tsv le correspond.
Unlike NORS, any individual or institution can submit data to NCBI through NCBI’s submission portal. Submissions are processed or rejected in accordance with NLM’s GenBank and SRA Data Processing document . While some articles exist that compare and contrast NCBI data to.
Although packages such as ShinyR could potentially be used for di erent outbreak database comparisons , there seem to be no Python packages for the purposes of comparing NCBI submission data with NORS data. (NCBI and NORS) Figures a) and b ).
The “V1” tag shows this is the rst assembled version of the code, and the authors plan to expand its functionality in future work. Additionally, categorizing the isolation source and host columns analysis was determined using a limited number of keyword searches.
This study shows how researchers can use data from NCBI to better understand patterns of infectious diseases like E. coli and Salmonella.