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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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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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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Genomic data integration tutorial, a plant case study.

Emile Mardoc1, Mamadou Dia Sow1, Sébastien Déjean2

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Summary

This tutorial outlines best practices for integrating genomic data from Next Generation Sequencing (NGS) technologies. It provides a six-step guide to address the complexities of analyzing large-scale genomic datasets, aiding biological discovery.

Keywords:
BiologyIntegrationOmicsSystem

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next Generation Sequencing (NGS) technologies generate massive genomic datasets.
  • Integrating and analyzing these large-scale datasets presents significant conceptual and analytical challenges in biology.

Purpose of the Study:

  • To provide a practical, six-step tutorial for best practices in genomic data integration.
  • To address the complexities associated with analyzing large-scale genomic data.

Main Methods:

  • Designing a data matrix.
  • Formulating specific biological questions for data analysis.
  • Selecting appropriate analytical tools.
  • Data preprocessing and preliminary analysis.
  • Executing the genomic data integration process.

Main Results:

  • A six-step tutorial for best practices in genomic data integration is described.
  • The tutorial was successfully demonstrated using publicly available genomic data from poplar (Populus L.).

Conclusions:

  • A new graphical output, cimDiablo_v2, was developed for unsupervised multi-block analysis.
  • This tool facilitates the identification of key drivers in genomic data variation and interplay.