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Related Concept Videos

Genomics02:02

Genomics

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...
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...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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.
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
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...

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Updated: May 7, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Bayesian methods for expression-based integration of various types of genomics data.

Elizabeth M Jennings1, Jeffrey S Morris, Raymond J Carroll

  • 1Department of Biostatistics, UT M,D, Anderson Cancer Center, Houston, TX 77030, USA. veera@mdanderson.org.

EURASIP Journal on Bioinformatics & Systems Biology
|September 24, 2013
PubMed
Summary

This study introduces a novel Bayesian framework to integrate genomic data for identifying cancer-related genes. The method enhances statistical power and reveals how gene expression impacts patient survival, with applications in Glioblastoma Multiforme.

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

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Last Updated: May 7, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Integrating multi-platform genomic data is challenging.
  • Identifying genes linked to clinical outcomes requires robust statistical methods.
  • Understanding gene-environment interactions in cancer is crucial.

Purpose of the Study:

  • To develop a hierarchical Bayesian framework for integrating genomic data from multiple platforms.
  • To identify genes associated with clinical outcomes in cancer, specifically patient survival.
  • To provide mechanistic insights into how gene expression influences cancer prognosis.

Main Methods:

  • Hierarchical Bayesian analysis framework.
  • Incorporation of biological relationships between genomic platforms.
  • Shrinkage estimation for improved statistical power.
  • Application to Glioblastoma Multiforme (GBM) dataset.

Main Results:

  • Successfully integrated data across multiple genomic platforms.
  • Identified 12 positive prognostic markers (9 genes) and 13 negative prognostic markers (9 genes) associated with patient survival in GBM.
  • Demonstrated increased statistical power for gene discovery through simulation.

Conclusions:

  • The proposed Bayesian framework effectively integrates multi-platform genomic data.
  • This approach enhances the identification of prognostic genes in cancer.
  • The method provides valuable mechanistic and prognostic information for cancer patients.