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Transgenic Plants02:50

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Recombinant DNA technology called transgenesis is often used to add a foreign gene or remove a detrimental gene from an organism. Such genetically modified organisms are called transgenic organisms.
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Related Experiment Video

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Co-localization of Cell Lineage Markers and the Tomato Signal
10:56

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Published on: December 28, 2016

Dynamic transcriptomic profiles between tomato and a wild relative reflect distinct developmental architectures.

Daniel H Chitwood1, Julin N Maloof, Neelima R Sinha

  • 1Department of Plant Biology, University of California, Davis, California 95616, USA.

Plant Physiology
|April 16, 2013
PubMed
Summary

Analyzing gene expression across tomato species reveals how tissue-specific gene regulation drives developmental differences. Self-organizing maps effectively visualize these interspecies expression patterns, highlighting evolutionary changes.

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Area of Science:

  • Genomics
  • Developmental Biology
  • Bioinformatics

Background:

  • Interspecies developmental differences often stem from altered tissue-specific gene expression.
  • Clustering algorithms are valuable for coexpression analysis within species but less commonly applied to multidimensional interspecies datasets.
  • Next-generation sequencing facilitates interspecies analyses, necessitating advanced visualization and exploration methods.

Purpose of the Study:

  • To analyze gene expression profiles across six tissue types in domesticated tomato (Solanum lycopersicum) and its wild relative (Solanum pennellii).
  • To evaluate self-organizing maps (SOMs) and other clustering approaches for exploring interspecies gene expression data.
  • To identify tissue-specific gene expression changes between species and link them to developmental architecture differences.

Main Methods:

  • Analysis of gene expression profiles across six tissues in S. lycopersicum and S. pennellii.
  • Application of self-organizing maps (SOMs) for interspecies data analysis, assigning orthologs to map levels.
  • Principal component analysis (PCA) to compare clustering approaches, visualizing orthologous pair expression.
  • Clustering based on interspecies expression differences to identify tissue-by-species interactions.

Main Results:

  • Self-organizing maps (SOMs) provide a useful framework for analyzing interspecies gene expression data.
  • Clustering based on expression differences, rather than absolute profiles, effectively identifies significant tissue-by-species interactions.
  • Observed gene expression profile changes correlate with differences in developmental architecture, such as meristematic activity, between the two tomato species.
  • The study presents a suite of data exploration methods for visualizing and interpreting tissue-by-species gene expression interactions.

Conclusions:

  • Self-organizing maps and expression-difference-based clustering are powerful tools for interspecies gene expression analysis.
  • These methods reveal how changes in tissue-specific gene expression contribute to developmental divergence between species.
  • The findings provide a framework for understanding evolutionary adaptations in gene regulation and developmental biology.