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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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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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PATH: An interactive web platform for analysis of time-course high-dimensional genomic data.

Yuping Zhang1, Yang Chen2, Zhengqing Ouyang2

  • 1Department of Statistics, University of Connecticut, Storrs, CT, 06269, USA.

International Journal of Computational Biology and Drug Design
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

We developed a Platform for Analysis of Time-course High-dimensional data (PATH) for genomics research. PATH offers user-friendly tools for pattern discovery and feature selection in complex biological datasets.

Keywords:
dimension reductionfeature selectioninteractive analysisjoint analysislongitudinal datavisualisation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Understanding biological system dynamics requires analyzing time-course genomic data.
  • High-dimensional time-course data presents significant analytical challenges.

Purpose of the Study:

  • To introduce a novel web application, Platform for Analysis of Time-course High-dimensional data (PATH).
  • To provide a user-friendly platform for interactive analysis and visualization of time-course high-dimensional genomic data.

Main Methods:

  • Utilizes Principal Trend Analysis (PTA) for pattern discovery and feature selection.
  • Incorporates Joint PTA for interactive and integrative analysis of time-course data.
  • Features interactive data visualization and dimension reduction techniques.

Main Results:

  • Demonstrates the utility of PATH using simulated and real genomic datasets.
  • Compares PATH's performance against established methods like functional principal component analysis.
  • Highlights PATH's effectiveness in uncovering patterns in complex biological time-course data.

Conclusions:

  • PATH offers a powerful and accessible tool for genomics researchers.
  • The platform facilitates deeper insights into biological system dynamics from time-course data.
  • PATH is freely available, promoting broader application in health and disease research.