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    Big data in biology requires updated bioinformatics training. Curricula can be enhanced with new courses focusing on biological, computational, and statistical skills for data-intensive science.

    Keywords:
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    Area of Science:

    • Bioinformatics and computational biology
    • Data science in biological research

    Background:

    • Modern technologies generate vast biological data, necessitating systems-level analysis.
    • Computational biologists and bioinformaticians are increasingly tasked with interpreting big data.

    Purpose of the Study:

    • To review changes in bioinformatics curricula due to big data.
    • To identify essential competencies for scientists working with big data.
    • To propose new course structures to meet these needs.

    Main Methods:

    • Literature review of existing bioinformatics curricula.
    • Identification of key competencies across disciplines for big data analysis.
    • Proposal of modular course content adaptable to existing programs.

    Main Results:

    • Bioinformatics programs traditionally train students in data-intensive science.
    • Specific biological, computational, and statistical emphases are crucial for the big data era.
    • Existing curricula can be updated rather than completely redesigned.

    Conclusions:

    • Big data challenges present an opportunity to refine bioinformatics education.
    • Targeted course additions can equip scientists with necessary big data skills.
    • A complete overhaul of bioinformatics training is not anticipated.