With the explosion of sequence data acquisition and submission to databases such as Genbank; research datasets have been getting larger and larger. To address the need for big data management and analytical skill sets, programs at several universities worldwide have emerged offering certificates, Master’s degrees and even Ph.D. degrees in the field of bioinformatics. The most recent guidance on bioinformatics core competencies was published last year in PLOS Computational Biology highlighting the importance of developing informatics skill sets early in the undergraduate curriculum. However, few courses at the undergraduate level strive to introduce big data analytics and bioinformatics within the curriculum design and many students graduate without a full understanding of what bioinformatics is nor how it can be used to solve biological problems.
Several bioinformatic disciplines: ie. metagenomics, genome construction/annotation, pathogen discovery, phylogenetics, metabolomics, transcriptomics have well-known workflows that teach valuable skills in data management, analytics, interpretation, and troubleshooting, but have yet to be translated to the classroom. Additionally, while new instructors and aspiring professors of microbiology recognize the importance of integrating more research, real-world datasets and informatics into the classroom, they know very little about, are not confident in, or are reticent to put together curriculum modules addressing big data analytics in microbiology.
This Research Topic focuses on bringing both research and educational communities together; to encourage researchers to translate their studies and pipelines into teaching tools and curriculum and encourage educators to dive into messy real-world datasets when teaching microbiology. The Topic will provide a platform for research applications focusing on software development and usage to answer microbiological hypotheses and their translation to the classroom.
We highly encourage article submissions that address or include; perspectives of bioinformatics education/training in microbiology, workshop and classroom module descriptions, informatics curriculum or workshop evaluations/efficacy results for both graphical and command-line based analysis workflows, best practices and considerations in experimental design and data analysis, database descriptions and updates, and software/pipeline submissions that include tutorials ready to be tested in the classroom using real-world and/or mock datasets.
With the explosion of sequence data acquisition and submission to databases such as Genbank; research datasets have been getting larger and larger. To address the need for big data management and analytical skill sets, programs at several universities worldwide have emerged offering certificates, Master’s degrees and even Ph.D. degrees in the field of bioinformatics. The most recent guidance on bioinformatics core competencies was published last year in PLOS Computational Biology highlighting the importance of developing informatics skill sets early in the undergraduate curriculum. However, few courses at the undergraduate level strive to introduce big data analytics and bioinformatics within the curriculum design and many students graduate without a full understanding of what bioinformatics is nor how it can be used to solve biological problems.
Several bioinformatic disciplines: ie. metagenomics, genome construction/annotation, pathogen discovery, phylogenetics, metabolomics, transcriptomics have well-known workflows that teach valuable skills in data management, analytics, interpretation, and troubleshooting, but have yet to be translated to the classroom. Additionally, while new instructors and aspiring professors of microbiology recognize the importance of integrating more research, real-world datasets and informatics into the classroom, they know very little about, are not confident in, or are reticent to put together curriculum modules addressing big data analytics in microbiology.
This Research Topic focuses on bringing both research and educational communities together; to encourage researchers to translate their studies and pipelines into teaching tools and curriculum and encourage educators to dive into messy real-world datasets when teaching microbiology. The Topic will provide a platform for research applications focusing on software development and usage to answer microbiological hypotheses and their translation to the classroom.
We highly encourage article submissions that address or include; perspectives of bioinformatics education/training in microbiology, workshop and classroom module descriptions, informatics curriculum or workshop evaluations/efficacy results for both graphical and command-line based analysis workflows, best practices and considerations in experimental design and data analysis, database descriptions and updates, and software/pipeline submissions that include tutorials ready to be tested in the classroom using real-world and/or mock datasets.
