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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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Dimension reduction techniques for the integrative analysis of multi-omics data.

Chen Meng, Oana A Zeleznik, Gerhard G Thallinger

    Briefings in Bioinformatics
    |March 13, 2016
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    This review explores dimension reduction techniques for integrating multiple

    Keywords:
    dimension reductionexploratory data analysisintegrative genomicsmulti-assaymulti-omics data integrationmultivariate analysis

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    Area of Science:

    • Bioinformatics and Computational Biology
    • Genomics and Systems Biology

    Background:

    • High-throughput 'omics' technologies generate large experimental datasets.
    • These datasets offer insights into cellular molecular pathways and disease.
    • Dimension reduction is crucial for analyzing complex biological data.

    Purpose of the Study:

    • To review dimension reduction approaches for simultaneous analysis of multiple 'omics' datasets.
    • To highlight the role of these methods in biological data integration.
    • To demonstrate their application in understanding biological systems and disease.

    Main Methods:

    • Focus on dimension reduction techniques for multi-dataset analysis.
    • Exploration of methods extracting linear relationships across datasets.
    • Identification of techniques for analyzing variability within and between variables.

    Main Results:

    • Dimension reduction facilitates exploratory analysis of integrated 'omics' data.
    • These methods can reveal correlated structures and variability across datasets.
    • Techniques can identify data issues like batch effects and outliers.

    Conclusions:

    • Dimension reduction is a key emerging approach for multi-omics data integration.
    • These methods enhance understanding of biological systems in health and disease.
    • Application of these techniques advances biological discovery.