Over the past decades, experimental advances in sequencing technology has seen an increasing demand for computational methods that can effectively handle and analyze genomic datasets. For example, the advent of whole genome shotgun sequencing saw large bacterial genomes being sequenced in the 1990s, but this required the development of complex algorithms that could piece together short sequenced reads. The advent of next- and third-generation sequencing technology saw the rise of genome assembly tools which could perform de novo genome assembly in a cost and time-efficient manner, as well downstream software analysis tools that could be used to explore genetic variation between sequenced samples. Moving forward, further advances in bioinformatic and computational approaches are required to handle and interpret the wealth of data being generated by genomic and metagenomic studies, including microbial identification, traceability of microorganisms, and pathogenic gene detection.
As the cost of whole genome sequencing is decreasing, thousands of microbial genomes and metagenomes are now available and can be used for data mining to help further our understanding of microbial function and evolution. Such big datasets can be used as a basis to develop Artificial Intelligence (AI) techniques like Machine Learning (ML), Deep Neural Network (DNN), and Natural Language Processing (NLP). However, at present, AI techniques have not been widely used for developing microbial genomic analysis methods. Hence, in this topic, we aim to collect manuscripts focusing on developing AI methods to understand characteristics such as function, virulence, infection, immune escape and antimicrobial resistance of microorganisms of pathogens that pose a threat to human health. Of particular interest are food-borne and nosocomial bacterial and fungal pathogens.
To contribute to the research areas mentioned above, this Research Topic will cover methods and applications on (but not be limited to) the following topics:
- New models and computational methods including sequencing tools, algorithms and protocols
- Theoretical and practical advances in bioinformatics, including sequence analysis, alignment, phylogenetics and understanding the basis for phenotypic traits
- Functional genomics and metabolism
- Investigation of antibiotic resistance and virulence factors using genomic approaches
- Genomic microbial epidemiology
Over the past decades, experimental advances in sequencing technology has seen an increasing demand for computational methods that can effectively handle and analyze genomic datasets. For example, the advent of whole genome shotgun sequencing saw large bacterial genomes being sequenced in the 1990s, but this required the development of complex algorithms that could piece together short sequenced reads. The advent of next- and third-generation sequencing technology saw the rise of genome assembly tools which could perform de novo genome assembly in a cost and time-efficient manner, as well downstream software analysis tools that could be used to explore genetic variation between sequenced samples. Moving forward, further advances in bioinformatic and computational approaches are required to handle and interpret the wealth of data being generated by genomic and metagenomic studies, including microbial identification, traceability of microorganisms, and pathogenic gene detection.
As the cost of whole genome sequencing is decreasing, thousands of microbial genomes and metagenomes are now available and can be used for data mining to help further our understanding of microbial function and evolution. Such big datasets can be used as a basis to develop Artificial Intelligence (AI) techniques like Machine Learning (ML), Deep Neural Network (DNN), and Natural Language Processing (NLP). However, at present, AI techniques have not been widely used for developing microbial genomic analysis methods. Hence, in this topic, we aim to collect manuscripts focusing on developing AI methods to understand characteristics such as function, virulence, infection, immune escape and antimicrobial resistance of microorganisms of pathogens that pose a threat to human health. Of particular interest are food-borne and nosocomial bacterial and fungal pathogens.
To contribute to the research areas mentioned above, this Research Topic will cover methods and applications on (but not be limited to) the following topics:
- New models and computational methods including sequencing tools, algorithms and protocols
- Theoretical and practical advances in bioinformatics, including sequence analysis, alignment, phylogenetics and understanding the basis for phenotypic traits
- Functional genomics and metabolism
- Investigation of antibiotic resistance and virulence factors using genomic approaches
- Genomic microbial epidemiology
