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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Genetic Screens02:46

Genetic Screens

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Gene Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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Related Experiment Video

Updated: Jun 2, 2026

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

Sequential patterns mining and gene sequence visualization to discover novelty from microarray data.

A Sallaberry1, N Pecheur, S Bringay

  • 1LaBRI, INRIA Bordeaux Sud-Ouest, Pikko, 351, cours de la Libération, 33405 Talence Cedex, France. arnaud.sallaberry@labri.fr

Journal of Biomedical Informatics
|April 30, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel visualization system for sequential pattern mining, making complex biological data more accessible. The system aids researchers in discovering novel patterns in gene data, supporting Alzheimer

More Related Videos

Novel Sequence Discovery by Subtractive Genomics
09:40

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Related Experiment Videos

Last Updated: Jun 2, 2026

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

Novel Sequence Discovery by Subtractive Genomics
09:40

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Area of Science:

  • Bioinformatics
  • Data Mining
  • Scientific Visualization

Background:

  • Frequent pattern mining generates numerous patterns, overwhelming end-users.
  • Analyzing large biological datasets for novel insights presents significant challenges.

Purpose of the Study:

  • To develop a novel visualization system for sequential pattern mining.
  • To enhance the analysis of extracted knowledge and highlight novelty in biological documents.

Main Methods:

  • The system employs three visualization techniques: clouds, solar systems, and treemaps.
  • Sequential pattern mining applied to gene data.

Main Results:

  • The visualization techniques effectively identify associations and hierarchical relationships between patterns.
  • The system successfully highlighted novel patterns in gene data.

Conclusions:

  • The proposed visualization system aids end-users in analyzing complex sequential patterns from biological data.
  • The system's utility was validated by biology laboratories researching Alzheimer's disease and cancer.