Influenza Research Database

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General information about the Influenza Research Database

Content

  • IRD is comprised of 3 features (found from their corresponding paper in NAR):
    • Influenza virus-related data (surveillance, clinical, phenotypic, genomic, and proteomic)
    • Analytical and visualization tools (e.g. sequence comparison and analysis tools, BLAST, protein structure visualization tools, etc.)
    • Personal workbench (data storage and sharing)
  • IRD contains avian and non-human mammalian influenza surveillance data, human clinical data associated with virus extracts, phenotypic characteristics of viruses isolated from extracts, and all genomic and proteomic data available in public repositories for influenza viruses (Mission).
  • It includes both primary and secondary data that appear to be both electronically curated and manually curated in-house. On the Data Sources page it mentions that it uses algorithms to generate different data types. In the NAR paper authors also mention that some of the data are integrated from IRD in-house curation and annotation pipelines.

Maintenance

  • IRD is maintained privately by a team of 29 individuals belonging to Northrop Grumman Health IT, Vecna Technologies, DMID/NIAID/NIH/DHHS, and the J. Craig Venter Institute.
  • It really encourages the public is encouraged to submit its own data using the Workbench and Submit Data options.

Funding

  • IRD is funded by the National Institute of Allergy and Infectious Diseases, National Institutes of Health, and Department of Health and Human Services.
  • It is a collaboration between Northrop Grumman Health and Human Services, J. Craig Venter Institute, and Vecna Technologies.

Scientific quality of the database

Content

  • The content does appear to completely cover its content domain: 
"IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support."
  • IRD has data coverage on both birds and mammals.

Usefulness

"The objective of the IRD resource is to provide a one-stop shop for influenza virus data and analysis tools to drive new discoveries about influenza virus transmission, virulence, host range and pathogenesis, and to develop novel strategies for diagnosis, prevention and therapeutic intervention."
  • This database can be used to answer questions about genetic sequences, animal surveillance, immune epitopes, 3D protein structures, phenotype, human clinical metadata, antiviral drugs, and much more.
  • It contains analysis tools that assist biologists in analyzing their own data.

Relevance

  • IRD content is very timely because there is a need in the scientific community for such a database; the influenza virus is a major global public threat with complex processes. IRD allows for an easily accessed compilation of all data corresponding to the influenza virus while at the same time assisting biologists in their own analysis of data.
  • IRD is not completely unique as NCBI covers similar content in their Influenza Virus Database.

Upkeep

  • It is unclear when the database was first released online. However, its most distant publication was in 2006 (IRD Resource Publications and Presentations).
  • While sequences are downloaded from GenBank, curated, and added to the IRD database daily, updates to generated and imported public data are made less often. The most recent updates for this information occurred between May 2017 and October 2017 (IRD Data Updates).

General utility of the database to the scientific community

Where does the data come from?

  1. Although most of the data is generated by the IRD team, the database also imports data from the following databases:

Searching the database

  1. IRD has multiple ways to browse and search the data within their database. They also have a number of tools that make it convenient to refine and analyze the search as well as save the data you are working on to your "workbench" so you can come back to it later.
    1. You can use their quick search tool, to "search for sequence records using any text terms in key text fields and public IDs (e.g. accession numbers) of nucleotide and protein sequence records, strain data, surveillance data, and human clinical metadata."
    2. Or, you search using any of the following, focused, search tools:
      • Sequences & Strains
      • Animal Surveillance
      • 3D Protein Structure Files
      • Human Clinical Metadata
      • Serology Experiments (Beta)
      • Host Factor Data
      • Antiviral Drugs
      • Immune Epitopes
      • Phenotypes
      • PCR Primer Probe Data
      • Sequence Feature Variant Types
      • Human Clinical Studies and Lab Experiments (Beta)

Exporting the Data

  1. With all of these tools, you have many options to access any single piece of data. This provides a lot of convenience when it comes too trying to locate anything within their database.
  2. Once you've used one of their various search tools to find the data point that you need, you can download the data into one of the following formats:
    1. GFF3
    2. Segment FASTA
    3. Gene FASTA
    4. CDS FASTA
    5. Protein FASTA
  3. All file formats that they provide are standard in bioinformatics.

User-friendliness & Organization

  1. It is a pretty user-friendly database. They have done a good job making all of their search and download tools very obvious and easy to use. Obviously is someone who has no biological background would have a hard time searching data, but that doesn't make the site not user-friendly.
  2. For the most part everything is clearly labeled and organized well. They have five main sections right under their logo which help direct you to the part of the site that you are looking for. The dropdown menus under each of these sections has labels that aren't confusing, and therefore take you to exactly the part of the site that you think you are going to.

Help Sections

  1. One of the five main sections is labeled help (easily spotted and right at the top-center of the page) and has the following sub-sections:
    1. Help Manual
    2. Tutorials & Training Materials
    3. Frequently Asked Questions
    4. IRD Computational Protocols
    5. IRD Glossary
    6. Contact Us
    7. Cite IRD
  2. Their help manual is very extensive with detailed written instructions on how to access/use any part of the site. Their Tutorials and Training Materials page is also very helpful because it provides links to video instructions on how to do the most used tasks within IRD.

Summary judgment

  1. Influenza Research Database is good for any biologist that is either new to bioinformatics or has veteran status in the field
  2. It is a very well-organized site that congregates its own data with the data of most of the big name bio-databases
  3. The site is designed well and has clearly marked entrances and exits to every page and section which makes it good for any hobbyist or professional bioinformatician

Powerpoint presentation

File:EDDIE EMMA PWPT.pdf

Electronic notebook

For this assignment, we chose the Influenza Research Database after class and did individual research on it. We then met up in the library and began working on the database's wiki page. First, we titled the page "Influenza Research database" and linked both of our user pages to this page. We then made an outline with individual headings corresponding to each of the main sections (this allowed us to work on the wiki at the same time by just being able to edit individual sections). We read the corresponding paper in NAR to get a general background for usage of the database and then began exploring the database itself. Emma worked on the first two sections (general information and scientific quality) while Eddie focused on the final one (general utility of the database to the scientific community). We went down the list of questions navigating through the paper and database to find the answers. Information was gathered by clicking on different tabs in the database and randomly browsing/exploring. Once we finished our individual sections for the analysis of the database, we started constructing our powerpoint presentation on google slides. We used the same basic format and layout in our powerpoint as we did in our wiki page and followed the presentation and powerpoint guidelines that were gone over in class. Once we finished our powerpoint, we checked for typos and uploaded the presentation to the wiki page.

Acknowledgements

  1. Emma and Eddie thank each other for working on this assignment together. We met outside of class to complete the assignment and divide up the presentation. We also plan to meet up in the future to practice.
  2. We would like to thank Dr. Dahlquistand Dr. Dionisio for their guidance and support
  3. While we worked with the people noted above, this journal entry was completed by Emma and Eddie and not copied from another source.

Emmatyrnauer (talk) 16:48, 4 October 2017 (PDT) Ebachour (talk) 23:58, 4 October 2017 (PDT)

References

  1. LMU BioDB 2017. (2017). Week 5. Retrieved October 4, 2017, from https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Week_5.
  2. Influenza Research Database: update 2017. Retrieved October 4, 2017, from https://www.fludb.org/brc/home.spg?decorator=influenza.
  3. Yun Zhang, Brian D. Aevermann, Tavis K. Anderson, David F. Burke, Gwenaelle Dauphin, Zhiping Gu, Sherry He, Sanjeev Kumar, Christopher N. Larsen, Alexandra J. Lee, Xiaomei Li, Catherine Macken, Colin Mahaffey, Brett E. Pickett, Brian Reardon, Thomas Smith, Lucy Stewart, Christian Suloway, Guangyu Sun, Lei Tong, Amy L. Vincent, Bryan Walters, Sam Zaremba, Hongtao Zhao, Liwei Zhou, Christian Zmasek, Edward B. Klem, Richard H. Scheuermann; Influenza Research Database: An integrated bioinformatics resource for influenza virus research, Nucleic Acids Research, Volume 45, Issue D1, 4 January 2017, Pages D466–D474. Retrieved October 4, 2017 from https://doi.org/10.1093/nar/gkw857.
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