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Related Concept Videos

Genomics02:02

Genomics

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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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Evolutionary Relationships through Genome Comparisons02:54

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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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Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Proteomics01:33

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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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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Metagenomic Analysis of Silage
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Metagenomic Analysis of Silage

Published on: January 13, 2017

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[Meta-Mesh: metagenomic data analysis system].

Xiaoquan Su, Baoxing Song, Xuetao Wang

    Sheng Wu Gong Cheng Xue Bao = Chinese Journal of Biotechnology
    |May 14, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Meta-Mesh is a novel system for analyzing and comparing metagenomic samples. It features a large, annotated database and an analysis platform to efficiently find similar microbial communities.

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    Area of Science:

    • Microbiology
    • Bioinformatics
    • Computational Biology

    Background:

    • Metagenome data is rapidly accumulating, necessitating integrated platforms for analysis.
    • Existing metagenomic databases often lack comprehensive annotation and analytical tools.
    • Current methods for comparing metagenomic data are limited in scope and scalability.

    Purpose of the Study:

    • To develop a novel system, Meta-Mesh, for systematic and efficient analysis and comparison of metagenomic samples.
    • To create a searchable, well-annotated database of metagenomic samples.
    • To provide an analysis platform with tools for taxonomic annotation, sample comparison, and similarity searching.

    Main Methods:

    • Collected over 7,000 high-quality, annotated metagenomic samples.
    • Developed a fast indexing strategy and scoring function for similarity searching.
    • Integrated online tools for taxonomic annotation and multi-angle sample comparison.

    Main Results:

    • Meta-Mesh successfully hosts a large, annotated metagenomic sample database.
    • The analysis platform enables efficient searching for similar metagenomic samples.
    • Case studies demonstrated effective sample identification and clustering based on similarity.

    Conclusions:

    • Meta-Mesh provides a robust database and analysis system for metagenomic data.
    • The system facilitates rapid parsing and identification of similar microbial communities.
    • Meta-Mesh enhances the ability to explore relationships within large metagenomic datasets.