Your domain: Human pathogen genomics

Introduction

The human pathogen genomics domain focuses on studying the genetic code of organisms that cause disease in humans. Studies to identify and understand pathogens are conducted across different types of organisations ranging from research institutes to regional public health authorities. The aims can include urgent outbreak response, prevention measures, and developing remedies such as treatments and vaccines.

Data management challenges in this domain include the potential urgency of data sharing and secondary use of data across initiatives emerging from research, public health and policymakers. While pathogenic organisms are the object of interest, there are many considerations to take into account when dealing with samples collected from patients, pathogen surveillance, and human research subjects.

The genomic data can represent anything from the genetic sequence of a single pathogen isolate to various fragments of genetic materials from a flora of pathogens in a larger population. Other data can represent a wide range of contextual information about the human host, the disease, and various environmental factors.

Planning a study with pathogen genomic data

Description

While the objects of interest in this domain are pathogens, the data is usually derived from samples originating from human research subjects. This means that you must plan to either remove or handle human data during your study.

Considerations

• What legal and ethical aspects do you need to consider?
  • Can you separate pathogen and human host material and data?
  • What data protection measures should be implemented in contracts and procedures dealing with suppliers and collaborators?
  • What is the appropriate scope for the legal and ethical agreements necessary for the study?
  • How should statements related to data processing be phrased to allow timely and efficient data sharing?
  • How much time would be required to negotiate access to the samples and data for the study?
• What public health and research initiatives should you consider aligning with?
  • What data could be shared with or reused from other initiatives during the project?
  • How will you align your practices with these initiatives to maximise the impact of the data and insight generated by the project?
  • How will you share data with your collaborators and other initiatives?
• What conventions will you adopt when planning your study?
  • What existing protocols should you consider adopting for sample preparation, sequencing, variant calling and other operations?
  • What conventions should you adopt for documenting your research?

Solutions

Working with human data

• Ensure that the project’s procedures conform with good practices for handling human data. In particular, the following sections of the RDMkit:
  • Planning for projects with human data
  • Processing and analysing human data

Isolate pathogen from host information

• Depending on the pathogen and how it interacts with the host or the methods applied, it can be possible to generate clean isolates that do not contain host-related material. Data produced from a clean isolate could potentially be handled with fewer restrictions, while other data will be considered to be sensitive and will need protection.

Public health initiatives

• National and international recommendations from public health authorities, epidemic surveillance programs and research data communities should be considered when planning a new study or surveillance programme. In particular, you could consult conventions for relevant surveillance programs while considering widely adopted guidelines for research documentation, and instructions from the data-sharing platforms.
  • European Centre for Disease Prevention and Control (ECDC) coordinates Disease and laboratory networks and also issues Surveillance and reporting protocols and other Technical guidance on sequencing.
  • WHO issued genomic surveillance strategy and guidance on implementation for maximum impact on public health and there are published reports that advise on Implementing Quality Management Systems in Public Health Laboratories.
  • The US Centers for Disease Control and Prevention (CDC) offers guidance on Pathogen genomics for its work in monitoring, investigating, and controlling infectious diseases.
  • Refer to National resources for information on regional authorities and national considerations.

Sequencing experiments

• Good practices for genome experiments suggest that the documentation, at a minimum, should describe the design of the study or surveillance program, the collected specimens and how the samples were prepared, the experimental setup and protocols, and the analysis workflow.
  • Adopt specific genomics and pathogen genomics recommendations such as those outlined in Stevens20201.
  • Refer to the general guidance on providing documentation and metadata during your project.
• Adopt standards, conventions and robust protocols to maximise the reuse potential of the data in parallel initiatives and your future projects.
  • The Genomic Standards Consortium (GSC) develops and maintains the Minimum Information about any (x) Sequence (MIxS) and the Minimum Information about a (Meta)Genome Sequence (MIxS - MIGS/MIMS) set of core and extended descriptors for genomes and metagenomes with associated samples and their environment to guide scientists on how to capture the metadata essential for high-quality research.
  • The GenEpiO Consortium develops and maintains the Genomic Epidemiology Ontology (GenEpiO) to support data sharing and integration specifically for foodborne infectious disease surveillance and outbreak investigations.
  • The Public Health Alliance for Genomic Epidemiology (PHA4GE) supports openness and interoperability in public health bioinformatics. The Data Structures working group develops, adapts and standardises data models for microbial sequence data, contextual metadata, results and workflow metrics, such as the SARS-CoV-2 contextual data specification.
  • The International Organization for Standardization (ISO) has issued standards that can be referenced when designing or commissioning genomic sequencing and informatics services, such as
    • ISO 20397-1:2022 Biotechnology — Massively parallel sequencing — Part 1: Nucleic acid and library preparation
    • ISO 20397-2:2021 Biotechnology — Massively parallel sequencing — Part 2: Quality evaluation of sequencing data
    • ISO/TS 20428:2017 Health informatics — Data elements and their metadata for describing structured clinical genomic sequence information in electronic health records
    • ISO/TS 22692:2020 Genomics informatics — Quality control metrics for DNA sequencing
    • ISO 23418:2022 Microbiology of the food chain — Whole genome sequencing for typing and genomic characterization of bacteria — General requirements and guidance

Collecting and processing pathogen genomic data

Considerations

• What information should you consider recording when collecting data?
  • What should you note when collecting, storing, and preparing the samples?
  • How will you capture information about the configuration and quality of the sequencing results?
  • How will you ensure that the captured information is complete and correct?
• What data and file formats should you consider for your project?
  • What are the de-facto standards used for the experiment type and downstream analysis pipelines?
  • Where are the instrument-specific aspects of the data and file formats documented?
• What existing data will you integrate or use as a reference in your project?
  • What reference genome(s) will you need access to?
  • What is the recommended citation for the data and their versions?

Solutions

Filtering genomic reads corresponding to human DNA fragments

• Data files with reads produced by sequencing experiments sometimes contain fragments of the host organism’s DNA. When the host is a human research subject, these fragments can be masked or removed to produce files that could potentially be handled with fewer restrictions. The approach chosen to mask the host-associated reads leads to different trade-offs. Make sure to include this as a factor in your risk assessment.
  • Mapping to (human) host reference genomes can inadvertently leave some host-associated reads unmasked 2.
  • Mapping to pathogens reference genomes can inadvertently mask some pathogen-associated reads and still leave some host-associated reads unmasked
  • Removal of human reads from SARS-CoV-2 sequencing data | Galaxy training

Contextual information about the sample

• Information about the host phenotype, context, and disease is often necessary to answer questions in a research study or policy perspective. Other contextual information can include non-host-related environmental factors, such as interactions with other pathogens, drugs and geographic proliferation. It can also include information about the sampled material and how it was processed for sequencing.
• Adopt common reporting checklists, data dictionaries, terms and vocabularies to simplify data sharing across initiatives.
  • European Nucleotide Archive (ENA) hosts a selection of sample checklists that can be used to annotate sequencing experiments, including checklists derived from Minimum Information about any (x) Sequence (MIxS). The ENA virus pathogen reporting standard checklist has been widely used for SARS-CoV-2 genomic studies.
  • Reuse terms and definitions from existing vocabularies, such as the Phenotypic QualiTy Ontology (PATO), NCBI Taxonomy, Disease Ontology (DOID), ChEBI, and UBER anatomy ONtology (UBERON).
  • The PHA4GE SARS-CoV-2 contextual data specification is a comprehensive example including a reporting checklist, related protocols, and mappings to relevant vocabularies and data sharing platforms.

Generating genomic data

• Establish protocols and document the steps taken in the lab to process the sample and in the computational workflow to prepare the resulting data. Make sure to keep information from quality assurance procedures and strive to make your labwork and computational process as reproducible as possible.
  • High-Throughput Sequencing | LifeScienceRDMLookUp.
  • The Beyond 1 Million genomes project provides guidelines that cover the minimum quality requirements 3 for the generation of genome sequencing data.
  • Data repositories generally have information about recommended data file formats and metadata.
  • The FAIR Cookbook provides instructions on validation of file formats.
  • A good place to look for scientific and technical information about data quality validation software tools for pathogenomics is bio.tools.
  • The Infectious Diseases Toolkit (IDTk) has a showcase on An automated SARS-CoV-2 genome surveillance system built around Galaxy.
  • The Galaxy Training Network provides free online training materials on quality control.

Sharing and preserving pathogen genomic data

Considerations

• What data needs to be preserved by the project and for how long?
• What is preserved by others and how would someone find and access the data?
• What databases should I use to share human pathogen genomics data?
• What other research information (such as protocols, computational tools, and samples) can the project share?

Solutions

Sharing host-related and other contextual information

• Some host-related information can be personal and/or sensitive and care should be taken when storing and sharing it. Apply data masking and aggregation techniques to pseudonymise or anonymise the contextual information and take measures to separate personal and sensitive information from the pathogen data when possible.
• Adopt solutions for federated analysis to support distributed analyses on information that could otherwise not be shared, such as establishing contractual agreements with suitable regional or international data infrastructures.
• GA4GH is a global organisation that frames policy and builds standards to meet the real-world needs of the genomics and health community. Its GDPR & International Health Data Sharing Forum shares GDPR Briefs that represent a consensus position among its Forum Members (not legal advice) regarding the current understanding of the GDPR and its implications for genomic and health-related research, such as
  • GDPR Brief: data protection implications of publishing metadata to enable discovery;
  • GDPR Brief: federated analysis for responsible data sharing under the GDPR.

Sharing pathogen genomic data

• You should adopt good practices for data sharing and identify which data sharing platforms to use to reach the relevant stakeholders. You can use more than one platform but care should be taken to make sure that data is interconnected where possible to enable deduplication in downstream analyses.
  • European healthcare surveillance systems are administered and used by public health authorities such as ECDC’s TESSy/EpiPulse.
  • International research data exchanges such as European Nucleotide Archive (ENA) for non-sensitive genomic data.
  • There are also pathogen specific initiatives, such as Pathogens Portal and Pathogen Detection. And initiatives focusing specifically on viruses, certain pathogens or certain data types, such as Global Initiative on Sharing All Influenza Data (GISAID) for observations and assembled consensus sequences on a selection of pathogens.
• Investigate if there are national resources or a data brokering organisation available to facilitate data sharing.
  • Pathogens Portal Data Hubs network for sensitive data.
  • COVID-19 Data Portal.

Bibliography

1. Stevens, I. et al. Ten Simple Rules for Annotating Sequencing Experiments. PLOS Computational Biology 16, (2020).
2. Bush, S. J., Connor, T. R., Peto, T. E. A., Crook, D. W. & Walker, A. S. Evaluation of Methods for Detecting Human Reads in Microbial Sequencing Datasets. Microbial Genomics 6, (2020).
3. Gut, I. et al. B1MG D3.1 - Quality Metrics for Sequencing. (2021) doi:10.5281/ZENODO.5018495.

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