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Related Concept Videos

Genomics02:02

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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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Modern Molecular Taxonomy01:29

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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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Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
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Genomic DNA in Prokaryotes00:46

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The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
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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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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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Related Experiment Video

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Pattern-based Search of Epigenomic Data Using GeNemo
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Ontology-Based Search of Genomic Metadata.

Javier D Fernandez, Maurizio Lenzerini, Marco Masseroli

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
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    Searching the Encyclopedia of DNA Elements (ENCODE) for biological insights is enhanced by S.O.S. GeM. This system uses semantic search to find more relevant genomic datasets than traditional methods.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Data Science

    Background:

    • The Encyclopedia of DNA Elements (ENCODE) offers vast genomic, transcriptomic, and epigenomic data.
    • Current ENCODE dataset search is limited by simple, incomplete metadata lacking a coherent ontology.
    • Extracting novel biological knowledge from ENCODE data is challenging due to search limitations.

    Purpose of the Study:

    • To develop an improved method for searching and retrieving ENCODE datasets.
    • To overcome limitations in ENCODE metadata by incorporating ontological knowledge and advanced indexing.
    • To enable more effective data-driven genomic discoveries from the ENCODE repository.

    Main Methods:

    • Developed S.O.S. GeM, a system for semantic search and retrieval of ENCODE datasets.
    • Constructed a Semantic Knowledge Base by mapping ENCODE metadata to biomedical ontologies within the Unified Medical Language System.
    • Utilized the Semantic Knowledge Base to perform semantic searches based on biologists' queries.

    Main Results:

    • The S.O.S. GeM system enables effective semantic search and retrieval of ENCODE datasets.
    • The semantic search approach successfully identifies more relevant datasets compared to purely syntactic search methods.
    • Empirical evidence demonstrates the high relevance of datasets found through S.O.S. GeM to biological research queries.

    Conclusions:

    • Semantic search, powered by ontological knowledge, significantly enhances the discoverability of ENCODE datasets.
    • S.O.S. GeM provides a robust solution for navigating and utilizing the vast ENCODE data repository.
    • This approach facilitates deeper data-driven insights and discoveries in genomics, transcriptomics, and epigenomics.