User-Friendly Tools Applied to Genetics or Systems Biology

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Editorial

09 September 2020

Editorial: User-Friendly Tools Applied to Genetics or Systems Biology

Helder I. Nakaya

,

Juilee Thakar

and

Vinicius Maracaja-Coutinho

2,630 views

1 citations

Editors

3

University of São Paulo

University of Rochester

Vinicius Maracaja-Coutinho

University of Chile

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An important problem in Systems biology is to determine ways for combining high-throughput data, mathematical modeling, and computational approaches to help us understand and predict the behavior of a biological system under a specific perturbation.

However, according to Sydney Brenner “we are drowning in a sea of data and starving for knowledge. Today, biology is more about gathering data than hunting down new ideas”. This is partly due to the fact that a substantial number of researchers who are capable of thinking about new insights, are not able to deal with the vast amounts of data generated by modern technologies. This Research Topic aims to help those researchers interested in analyzing high-throughput data, but lacking knowledge on programming languages or bioinformatics skills. Thus, we welcome manuscripts describing computational methods and online resources that facilitate the analysis of Big Data in Genetics or Systems Biology. All user-friendly tools must have an intuitive interface allowing non-bioinformaticians to perform complex analyses and guiding them to answer their own biological question.

Specifically, we are looking for novel and robust methods that can be applied to:

- Preprocessing, normalization, annotation or filtering of datasets generated by proteomics, metabolomics, genomics or transcriptomics experiments;
- Assessing the heterogeneity and complexity of high-throughput data and individual variation;
- Analyzing single-cell omics datasets;
- Performing functional and statistical analyses;
- Visualizing and creating graphs related to systems biology;
- Machine-learning and predictive analyses;
- Crowd-sourcing analyses;
- Data modeling and Network Medicine.

An important problem in Systems biology is to determine ways for combining high-throughput data, mathematical modeling, and computational approaches to help us understand and predict the behavior of a biological system under a specific perturbation.

However, according to Sydney Brenner “we are drowning in a sea of data and starving for knowledge. Today, biology is more about gathering data than hunting down new ideas”. This is partly due to the fact that a substantial number of researchers who are capable of thinking about new insights, are not able to deal with the vast amounts of data generated by modern technologies. This Research Topic aims to help those researchers interested in analyzing high-throughput data, but lacking knowledge on programming languages or bioinformatics skills. Thus, we welcome manuscripts describing computational methods and online resources that facilitate the analysis of Big Data in Genetics or Systems Biology. All user-friendly tools must have an intuitive interface allowing non-bioinformaticians to perform complex analyses and guiding them to answer their own biological question.

Specifically, we are looking for novel and robust methods that can be applied to:

- Preprocessing, normalization, annotation or filtering of datasets generated by proteomics, metabolomics, genomics or transcriptomics experiments;
- Assessing the heterogeneity and complexity of high-throughput data and individual variation;
- Analyzing single-cell omics datasets;
- Performing functional and statistical analyses;
- Visualizing and creating graphs related to systems biology;
- Machine-learning and predictive analyses;
- Crowd-sourcing analyses;
- Data modeling and Network Medicine.

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Editors

University of São Paulo

University of Rochester

Vinicius Maracaja-Coutinho

University of Chile

Impact

137,143 Total views

110,213 Article views

23,663 Article downloads

3,267 Topic views

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