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Related Concept Videos

Evolutionary Processes in Microbes01:26

Evolutionary Processes in Microbes

Microbial evolution occurs rapidly due to short generation times and a variety of genetic processes, including horizontal gene transfer, mutation, recombination, and genetic drift. These mechanisms collectively enable microbes to adapt swiftly to changing environments.Horizontal gene transfer (HGT) allows genes to move between different species and occurs through three main mechanisms: conjugation, transformation, and transduction. Conjugation involves direct cell-to-cell contact for DNA...
Evolutionary Relationships through Genome Comparisons02:54

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...
Evolution of New Traits in Microbes01:24

Evolution of New Traits in Microbes

Microorganisms evolve rapidly due to their large population sizes and short generation times, often exhibiting measurable changes within days under laboratory conditions. Natural selection acts on standing genetic variation, enabling the retention and amplification of beneficial traits that confer fitness advantages in changing environments.Adaptive Pigment Regulation in RhodobacterIn Rhodobacter, a genus of purple non-sulfur bacteria, light-harvesting pigments such as bacteriochlorophyll and...
Synteny and Evolution02:31

Synteny and Evolution

John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Gene Evolution - Fast or Slow?02:05

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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Updated: May 9, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Published on: February 3, 2023

EssentCell: Discovering Essential Evolutionary Relations in Noisy Single-Cell Data.

Adiesha Liyanage, Robyn Burger, Allison Shi

    IEEE Transactions on Computational Biology and Bioinformatics
    |May 7, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces EssentCell, an algorithm to identify essential relationships among cells in noisy single-cell sequencing data. It reconstructs tumor evolution by finding conserved patterns in phylogenetic trees, even with errors.

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    Area of Science:

    • Computational Biology
    • Genomics
    • Bioinformatics

    Background:

    • Single-cell sequencing (SCS) provides high-resolution data for studying tumor evolution.
    • SCS data is often noisy, requiring methods to correct errors for accurate phylogenetic analysis.
    • Perfect phylogeny models assume conflict-free data, which is rarely met in practice.

    Purpose of the Study:

    • To develop an efficient algorithm for identifying essential relationships among cells in noisy SCS data.
    • To address the minimum-flip problem in SCS data, considering false positive rates.
    • To find conserved relations across all optimal phylogenetic solutions, not just a single tree.

    Main Methods:

    • Integer linear programming (ILP) to determine essential cell relations.
    • Algorithm parameterized by the number of false positives in the data.
    • Development and testing of the EssentCell tool on various datasets.

    Main Results:

    • An efficient ILP-based algorithm, EssentCell, was developed.
    • The algorithm identifies essential relations among cells in SCS data.
    • Demonstrated applicability on multiple real-world datasets.

    Conclusions:

    • EssentCell provides an efficient method for analyzing noisy SCS data.
    • The tool helps in understanding tumor evolution by identifying conserved phylogenetic relationships.
    • Essential relations offer robust insights into cellular relationships despite data imperfections.