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Related Concept Videos

The Evidence for Evolution02:55

The Evidence for Evolution

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Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
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Convergent Evolution01:54

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The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the nucleus-first hypothesis, the ancestral prokaryote first evolved a membrane to enclose DNA and form the nucleus. Conversely, the mitochondria-first hypothesis suggests that the nucleus was formed after endosymbiosis of mitochondria.
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Synteny and Evolution02:31

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

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Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
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NUQA: Estimating Cancer Spatial and Temporal Heterogeneity and Evolution through Alignment-Free Methods.

Aideen C Roddy1, Anna Jurek-Loughrey2, Jose Souza1

  • 1Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom.

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This study introduces an alignment-free method for analyzing cancer evolution using genomic data. This approach, applied to longitudinal patient samples, offers a novel way to understand tumor heterogeneity and progression.

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Longitudinal next-generation sequencing (NGS) is crucial for understanding cancer evolution and heterogeneity.
  • Traditional alignment-based methods for analyzing cancer genomic data can lead to significant information loss.
  • Analyzing spatiotemporal tumor samples requires robust methods to assess evolutionary trajectories.

Purpose of the Study:

  • To propose and evaluate an alignment-free approach for analyzing longitudinal cancer patient samples.
  • To assess the utility of Jensen-Shannon divergence and Hellinger distance in characterizing tumor evolution.
  • To demonstrate the potential of alignment-free methods for unsupervised assessment of genomic profiles in cancer research.

Main Methods:

  • Developed and applied an alignment-free sequence comparison method using the NUQA software.
  • Utilized Jensen-Shannon divergence and Hellinger distance as metrics for sequence comparison.
  • Analyzed two longitudinal cancer patient cohorts: glioma and clear cell renal cell carcinoma.

Main Results:

  • The alignment-free approach was successfully applied to longitudinal cancer cohorts.
  • The study demonstrated the potential of Jensen-Shannon divergence and Hellinger distance in analyzing tumor evolution.
  • NUQA software facilitated unsupervised assessment of evolutionary trajectories in patient genomic profiles.

Conclusions:

  • Alignment-free methods offer a promising alternative for analyzing cancer evolution from longitudinal sequencing data.
  • This approach can reveal novel insights into tumor heterogeneity, progression, and early tumorigenesis events.
  • The methodology has the potential to uncover origins of metastases and recurrences, aiding clinical research.