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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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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 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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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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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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Updated: Dec 1, 2025

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Argonaut: A Web Platform for Collaborative Multi-omic Data Visualization and Exploration.

Dain R Brademan1,2, Ian J Miller3, Nicholas W Kwiecien1

  • 1Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.

Patterns (New York, N.Y.)
|November 6, 2020
PubMed
Summary
This summary is machine-generated.

Argonaut is a code-free platform for creating interactive websites to share and explore large multi-omic datasets. This tool simplifies data dissemination and analysis, accelerating biological discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • The rapid generation of large multi-omic datasets presents significant challenges in data dissemination, visualization, and exploration.
  • Developing custom web-based data portals for multi-omic experiments typically requires extensive programming expertise and web server knowledge, posing a barrier for many researchers.

Purpose of the Study:

  • To introduce Argonaut, a user-friendly, code-free platform designed for creating customizable, interactive websites to host and explore multi-omic data.
  • To streamline the process of data sharing and analysis for multi-omic studies, thereby facilitating biological insights.

Main Methods:

  • Argonaut provides a platform for organizing multi-omic data into shareable projects.
  • The platform performs real-time statistical analyses on the data.
  • It enables visualization of results through interactive plots that users can modify.

Main Results:

  • Argonaut successfully facilitates the creation of customizable, interactive data-hosting websites without requiring programming skills.
  • The platform enables real-time statistical analysis and visualization of multi-omic data, allowing for streamlined data interpretation.
  • A published multi-omics dataset from a mitochondrial protease deletion collection was used to demonstrate Argonaut's capabilities.

Conclusions:

  • Argonaut significantly lowers the barrier to entry for creating web-based data portals for multi-omic research.
  • The platform enhances the accessibility and interpretability of complex multi-omic datasets.
  • By increasing the pace and ease of data access, Argonaut aims to accelerate the discovery of new biological insights.