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Related Concept Videos

Gene Duplication and Divergence02:37

Gene Duplication and Divergence

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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...
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Gene Families01:57

Gene Families

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Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
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Genome Copying Errors02:46

Genome Copying Errors

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DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Replication in Prokaryotes01:32

Replication in Prokaryotes

28.6K
DNA replication has three main steps: initiation, elongation, and termination. Replication in prokaryotes begins when initiator proteins bind to the single origin of replication (ori) on the cell's circular chromosome. Replication then proceeds around the entire circle of the chromosome in each direction from the two replication forks, resulting in two DNA molecules.
Many Proteins Work Together to Replicate the Chromosome
Replication is coordinated and carried out by a host of specialized...
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Replication in Prokaryotes02:35

Replication in Prokaryotes

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Overview
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Related Experiment Video

Updated: Mar 1, 2026

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

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Efficient Algorithms for Genomic Duplication Models.

Jaroslaw Paszek, Pawel Gorecki

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |May 26, 2017
    PubMed
    Summary

    This study introduces efficient algorithms for identifying genomic duplication events and their evolutionary history. The research provides new tools and comparative analyses to improve the accuracy of inferring gene duplication events.

    Area of Science:

    • Evolutionary molecular biology
    • Genomics
    • Bioinformatics

    Background:

    • Identifying genomic duplication events is crucial for understanding evolutionary history.
    • Current methods differ in modeling evolutionary scenarios and clustering gene duplications.
    • Minimum episodes clustering is a key method for inferring duplication events.

    Purpose of the Study:

    • To develop efficient algorithms for genomic duplication discovery.
    • To analyze and compare different models of evolutionary scenarios for gene duplications.
    • To provide mathematical foundations for genomic duplication problems.

    Main Methods:

    • Focus on minimum episodes clustering for interval models of evolutionary scenarios.
    • Development of linear time and space algorithms for minimum episodes clustering.

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  • Comparative analysis using simulated and empirical genomic datasets.

    • Main Results:

    • First linear time and space algorithm for minimum episodes clustering across interval models.
    • Algorithm for the most general model allowing any evolutionary scenario.
    • Comparative study highlighting the most suitable models for inferring genomic duplications.

    Conclusions:

    • Developed efficient algorithms and tools for minimum episodes clustering.
    • Comparative study aids in selecting appropriate models for genomic duplication inference.
    • Advances understanding of evolutionary molecular biology through improved duplication event identification.