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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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.
In contrast, regions which code...

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Related Experiment Video

Updated: Jul 19, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Phylogenetic inference from single-cell RNA-seq data.

Xuan Liu¹, Jason I Griffiths², Isaac Bishara²

¹Department of Integrative Biology & Pharmacology, University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 4.218, Houston, TX, 77030, USA.

Scientific Reports

|August 8, 2023

Summary

This summary is machine-generated.

PhylinSic reconstructs cancer cell evolution from single-cell RNA sequencing data. This method links cell genotypes and phenotypes, revealing how tumors develop resistance to therapies like chemotherapy.

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Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

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Last Updated: Jul 19, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

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Published on: July 18, 2019

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

Area of Science:

Oncology
Computational Biology
Genomics

Background:

Tumors contain diverse cancer cell subpopulations with evolving genetic profiles.
Understanding cancer evolution is key to identifying malignant properties driving tumor progression.
Reconstructing cell lineage and linking it to phenotype from single-cell data is challenging.

Purpose of the Study:

To develop a computational method for reconstructing phylogenetic relationships among cells.
To link these evolutionary relationships with gene expression profiles from single-cell RNA sequencing (scRNA-Seq) data.
To enable the study of cancer evolution and the acquisition of malignant phenotypes.

Main Methods:

Developed PhylinSic, a novel method for phylogenetic reconstruction from scRNA-Seq data.
Employs a probabilistic smoothing approach for nucleotide base calling.
Utilizes a Bayesian modeling algorithm to estimate phylogenetic trees.

Main Results:

PhylinSic successfully identified evolutionary relationships associated with drug selection and metastasis.
The method demonstrated sensitivity in detecting subclones arising from genetic drift.
Analysis of chemotherapy-resistant breast cancers revealed multiple lineages with independent K-Ras and β-catenin acquisition.

Conclusions:

PhylinSic enables the reconstruction of tumor evolution and links cell genotypes to phenotypes.
The findings suggest that durable therapeutic strategies may require targeting multiple emergent cancer lineages.
This approach advances the understanding of intra-tumor heterogeneity and treatment resistance.