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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-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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Related Experiment Video

Updated: Nov 21, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies.

Mario Zanfardino1, Rossana Castaldo2, Katia Pane1

  • 1IRCCS SDN, Via E. Gianturco, 113, 80143, Naples, Italy.

Scientific Reports
|January 16, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces MuSA, a user-friendly tool for analyzing radiogenomic data. MuSA integrates multi-omics and imaging data, aiding in cancer diagnosis and prognosis for personalized medicine.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Large-scale omics data and biomedical imaging analysis are crucial for personalized medicine.
  • Multi-omics data integration aids in cancer diagnosis, prognosis, and treatment.
  • Existing multi-omics data structures do not incorporate radiomic data.

Purpose of the Study:

  • To propose MultiAssayExperiment (MAE) as an integrated data structure for multi-omics and radiomic data.
  • To develop the Multi-omics Statistical Approaches (MuSA) tool for simplified radiogenomic data analysis.
  • To demonstrate MuSA's utility with public breast cancer datasets.

Main Methods:

  • Utilized MultiAssayExperiment (MAE) for heterogeneous data integration.
  • Developed MuSA, a Shiny GUI-based tool for managing and analyzing radiogenomic datasets.
  • Implemented modular pre-processing (filtering, normalization) and downstream analysis (correlation, clustering, feature selection) within MuSA.

Main Results:

  • MuSA successfully integrates multi-omics and radiomic data.
  • The tool provides dynamic visualization of analysis results.
  • Demonstrated capabilities using TCGA-TCIA breast cancer datasets.

Conclusions:

  • MuSA offers an accessible platform for radiogenomic data analysis, guiding non-programmers.
  • The modular, open-source architecture of MuSA allows for future expansion.
  • MuSA facilitates the exploration of complex radiogenomic datasets for improved cancer research.