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Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

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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Filtering for Highly Variable Genes and High-Quality Spots Improves Phylogenetic Analysis of Cancer Spatial

Alexandra Sasha Gavryushkina^1,2, Holly R Pinkney³, Sarah D Diermeier³

¹School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|June 11, 2025

Summary

This summary is machine-generated.

Analyzing spatial transcriptomics data for cancer evolution. We found that focusing on highly variable genes and accounting for RNA capture variations improves phylogenetic reconstructions, aiding cancer research.

Keywords:

RNA-sequencing cell lineages expression data phylogeography of tumor single cell

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Area of Science:

Oncology
Computational Biology
Genomics

Background:

Understanding tumor evolution requires analyzing cell relationships over space and time.
Single-cell DNA sequencing has been used to reconstruct cancer phylogenies.
Spatially resolved near single-cell RNA sequencing offers a cost-efficient alternative for evolutionary analysis.

Purpose of the Study:

To explore data preprocessing and modeling approaches for phylogenetic analysis of Visium spatial transcriptomics data.
To address methodological challenges in analyzing spatially resolved transcriptomics for cancer evolution.

Main Methods:

Exploration of data preprocessing techniques for spatial transcriptomics.
Development and application of modeling approaches for evolutionary analyses.
Utilizing Visium spatial transcriptomics data for phylogenetic reconstruction.

Main Results:

Using only highly variable genes improves reconstructed topological relationships.
Accounting for heterogeneous RNA capture across tissue spots enhances phylogenetic accuracy.
These approaches influence the estimation of phylogenetic branch lengths.

Conclusions:

Optimized preprocessing and modeling enhance phylogenetic analysis of spatial transcriptomics data.
This work provides methodological insights for studying cancer evolution using spatial transcriptomics.
Improved phylogenetic reconstructions can better identify cancer drivers and evolutionary events.