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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Related Experiment Video

Updated: Apr 4, 2026

Pattern-based Search of Epigenomic Data Using GeNemo
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An Efficient Search Algorithm for Finding Genomic-Range Overlaps Based on the Maximum Range Length.

Ho-Sik Seok, Taemin Song, Sek Won Kong

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 11, 2015
    PubMed
    Summary
    This summary is machine-generated.

    We developed a faster algorithm for finding genomic-range overlaps, crucial for bioinformatics. Our method optimizes search by categorizing reference ranges, improving efficiency for applications like variant prioritization.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Efficient algorithms for genomic-range overlap detection are vital for bioinformatics.
    • Existing algorithms often have a time complexity of O(k + logN), where k depends on reference range characteristics.

    Purpose of the Study:

    • To present a novel, efficient algorithm that reduces the 'k' term in search complexity.
    • To improve the speed of genomic-range overlap searches.

    Main Methods:

    • The algorithm categorizes reference ranges into 'always', 'potentially', and 'never' overlapping subsets based on maximum reference range length.
    • This categorization allows for the exclusion of the 'never overlapping' subset, reducing search effort.

    Main Results:

    • The algorithm's running time is proportional to the size of the 'potentially overlapping' subset, which is influenced by the maximum reference range length.
    • Implementation showed a 13.8% to 30.0% speed improvement over existing fast range search methods on genomic datasets.

    Conclusions:

    • The proposed algorithm offers a significant speed-up for genomic-range overlap searches.
    • It has been integrated into a whole genome sequencing (WGS) variant prioritization pipeline.