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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Evolution of comparative transcriptomics: biological scales, phylogenetic spans, and modeling frameworks.

Matteo Zambon1, Federica Mantica2, Mafalda Dias1

  • 1Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain; Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain; Barcelona Collaboratorium for Modelling and Predictive Biology, Spain.

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Comparative transcriptomics uses advanced sequencing and broader species sampling to reveal molecular evolution. New machine learning models aid in understanding cell type evolution and predicting RNA coverage, advancing evolutionary biology and medicine.

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Area of Science:

  • Evolutionary Biology
  • Genomics
  • Molecular Biology

Background:

  • Comparative transcriptomics is crucial for understanding molecular evolution and phenotypic divergence.
  • Historically, studies focused on model organisms and human-related species.
  • Advances in sequencing and data analysis are transforming the field.

Purpose of the Study:

  • To outline the key evolving directions in comparative transcriptomics.
  • To highlight the impact of new technologies and analytical approaches.
  • To underscore the expanding scope and applications of the field.

Main Methods:

  • Discusses the evolution from bulk RNA sequencing to single-cell and spatial transcriptomics.
  • Highlights the shift towards broader phylogenetic sampling and deeper clade analysis.
  • Mentions the integration of machine learning approaches for data analysis and modeling.

Main Results:

  • Single-cell and spatial transcriptomics enable cell-type-centered evolutionary comparisons.
  • Broader phylogenetic coverage provides a more comprehensive evolutionary perspective.
  • Machine learning facilitates novel frameworks for reconstructing phylogenies and predicting RNA expression.

Conclusions:

  • Comparative transcriptomics is rapidly advancing due to technological and methodological innovations.
  • The field is expanding to include a wider range of species and a focus on cellular resolution.
  • New analytical tools are enhancing our understanding of evolutionary processes and their biomedical relevance.