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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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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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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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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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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
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Visual Genomics Analysis Studio as a Tool to Analyze Multiomic Data.

Rebecca J Hertzman1, Pooja Deshpande1, Shay Leary1

  • 1Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, WA, Australia.

Frontiers in Genetics
|July 5, 2021
PubMed
Summary
This summary is machine-generated.

Visual Genomics Analysis Studio (VGAS) simplifies complex single-cell multiomic data analysis for adverse drug reactions (ADRs). This user-friendly application enables researchers to identify immune cell signatures without coding, advancing ADR understanding.

Keywords:
bioinformaticsheterogeneityimmunogenomicssingle-cell CITE-seqsingle-cell RNA sequencingsingle-cell TCR sequencing

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Area of Science:

  • Immunology
  • Genomics
  • Bioinformatics

Background:

  • Type B adverse drug reactions (ADRs) are immune-mediated, but their cellular mechanisms are poorly understood.
  • Severe ADRs, like delayed hypersensitivity, involve T-cells, human leukocyte antigen (HLA) alleles, and T-cell receptors (TCRs).
  • Defining the specific immune cell populations mediating ADRs has been challenging.

Purpose of the Study:

  • To introduce Visual Genomics Analysis Studio (VGAS), a user-friendly application for analyzing multiomic single-cell data.
  • To enable researchers to interrogate single-cell RNA sequencing (scRNA-seq), single-cell TCR sequencing (scTCR-seq), and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data.
  • To facilitate the characterization of immune cell populations involved in ADRs.

Main Methods:

  • VGAS provides an integrated graphical user interface for analyzing scTCR-seq, scRNA-seq, and CITE-seq data.
  • The application performs analyses such as TCR pairing, gene rearrangement visualization, and transcriptome/proteome profiling.
  • It integrates various visualization tools like principal component analysis, t-distributed stochastic neighborhood embedding, uniform manifold approximation and projection, volcano plots, and heatmaps.

Main Results:

  • VGAS enables holistic cellular characterization by analyzing transcriptome and surface proteome data.
  • It allows identification of immunodominant TCR-expressing populations and visualization of clonality and dominance.
  • Users can compare data to reference atlases to reveal disease-specific immune subsets and activation states.

Conclusions:

  • VGAS democratizes multiomic single-cell data analysis for researchers without extensive bioinformatics expertise.
  • The application supports detailed characterization of immune cells involved in ADRs, aiding in understanding disease pathogenesis.
  • VGAS facilitates the generation of high-resolution graphics for publication, advancing ADR research.