Literature DB >> 32108313

User guides for biologists to learn computational methods.

Dokyun Na1.   

Abstract

System-wide studies of a given molecular type are referred to as "omics." These include genomics, proteomics, and metabolomics, among others. Recent biotechnological advances allow for high-throughput measurement of cellular components, and thus it becomes possible to take a snapshot of all molecules inside cells, a form of omics study. Advances in computational modeling methods also make it possible to predict cellular mechanisms from the snapshots. These technologies have opened an era of computation-based biology. Component snapshots allow the discovery of gene-phenotype relationships in diseases, microorganisms in the human body, etc. Computational models allow us to predict new outcomes, which are useful in strain design in metabolic engineering and drug discovery from protein-ligand interactions. However, as the quantity of data increases or the model becomes complicated, the process becomes less accessible to biologists. In this special issue, six protocol articles are presented as user guides in the field of computational biology.

Entities:  

Keywords:  Ribo-seq; computational biology; drug discovery; machine learning; microbiome

Year:  2020        PMID: 32108313     DOI: 10.1007/s12275-020-9723-1

Source DB:  PubMed          Journal:  J Microbiol        ISSN: 1225-8873            Impact factor:   3.422


  3 in total

Review 1.  Trans-acting regulators of ribonuclease activity.

Authors:  Jaejin Lee; Minho Lee; Kangseok Lee
Journal:  J Microbiol       Date:  2021-02-10       Impact factor: 3.422

2.  Regulator of ribonuclease activity modulates the pathogenicity of Vibrio vulnificus.

Authors:  Jaejin Lee; Eunkyoung Shin; Jaeyeong Park; Minho Lee; Kangseok Lee
Journal:  J Microbiol       Date:  2021-11-09       Impact factor: 3.422

3.  Omics-based microbiome analysis in microbial ecology: from sequences to information.

Authors:  Jang-Cheon Cho
Journal:  J Microbiol       Date:  2021-03       Impact factor: 3.422

  3 in total

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