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Related Concept Videos

Genomics02:02

Genomics

41.8K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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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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Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Genome Size and the Evolution of New Genes03:21

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No description available
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Evolution of Microbial Genome01:08

Evolution of Microbial Genome

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Microbial genome evolution is a highly dynamic process shaped by continual gene gain and loss across species and strains. This genomic flexibility allows microorganisms to adapt rapidly to environmental pressures and interactions with other organisms. Central to understanding this diversity is the distinction between the core and pan genomes.The core genome comprises the genes shared by all sampled strains of a species, representing essential functions needed for fundamental cellular processes.
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Metagenomic Analysis of Silage
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LEC-Codec: Learning-Based Genome Data Compression.

Zhenhao Sun, Meng Wang, Shiqi Wang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |October 3, 2024
    PubMed
    Summary
    This summary is machine-generated.

    We introduce a Learning-based Genome Codec (LEC) for efficient and flexible lossless data compression. This deep learning model optimizes compression performance and speed for diverse applications.

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    Area of Science:

    • Bioinformatics
    • Computer Science
    • Data Compression

    Background:

    • Genomic data requires efficient compression techniques.
    • Existing codecs face challenges in balancing compression ratio, speed, and flexibility.
    • Deep learning offers novel approaches for data-driven compression.

    Purpose of the Study:

    • To propose a novel Learning-based Genome Codec (LEC).
    • To enhance efficiency and flexibility in lossless genomic data compression.
    • To optimize the trade-off between coding complexity and compression performance.

    Main Methods:

    • Integration of Group of Bases (GoB) compression, multi-stride coding, and bidirectional prediction.
    • Application of a data-driven deep neural network model for symbol probability inference.
    • Development of a fully parallelizable encoding and decoding architecture.

    Main Results:

    • The LEC demonstrates high efficiency in compression performance.
    • Achieved optimized balance between coding complexity and compression performance.
    • Showcased improved flexibility for real-world genomic data applications.

    Conclusions:

    • The proposed LEC offers a significant advancement in genomic data compression.
    • The data-driven approach enables adaptable performance for various applications.
    • LEC provides an efficient and flexible solution for lossless compression of genomic sequences.