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From time-series transcriptomics to gene regulatory networks: A review on inference methods

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  • Malvina Marku
  • Vera Pancaldi

Abstract

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.

Suggested Citation

  • Malvina Marku & Vera Pancaldi, 2023. "From time-series transcriptomics to gene regulatory networks: A review on inference methods," PLOS Computational Biology, Public Library of Science, vol. 19(8), pages 1-26, August.
    • Handle: RePEc:plo:pcbi00:1011254
    DOI: 10.1371/journal.pcbi.1011254
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    References listed on IDEAS

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    1. Misbah Razzaq & Loïc Paulevé & Anne Siegel & Julio Saez-Rodriguez & Jérémie Bourdon & Carito Guziolowski, 2018. "Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-23, October.
    2. Atte Aalto & Lauri Viitasaari & Pauliina Ilmonen & Laurent Mombaerts & Jorge Gonçalves, 2020. "Gene regulatory network inference from sparsely sampled noisy data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. Zhana Duren & Wenhui Sophia Lu & Joseph G. Arthur & Preyas Shah & Jingxue Xin & Francesca Meschi & Miranda Lin Li & Corey M. Nemec & Yifeng Yin & Wing Hung Wong, 2021. "Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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