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Related Concept Videos

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...
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...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
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.
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...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

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Related Experiment Video

Updated: May 23, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Pathway analysis of genomic data: concepts, methods, and prospects for future development.

Vijay K Ramanan1, Li Shen, Jason H Moore

  • 1Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.

Trends in Genetics : TIG
|April 7, 2012
PubMed
Summary

Pathway-based analysis of genomic data enhances complex disease discovery. This approach integrates diverse -omics data for deeper biological insights and improved treatment understanding.

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Area of Science:

  • Genomics
  • Systems Biology
  • Computational Biology

Background:

  • Genome-wide data analysis is crucial for understanding complex diseases.
  • Functional pathway sets offer enhanced discovery power and biological relevance.
  • Next-generation sequencing data necessitates updated analytical strategies.

Purpose of the Study:

  • To synthesize concepts and methodologies for pathway-based genomic data analysis.
  • To guide researchers in study design and execution for complex diseases.
  • To highlight challenges and propose solutions in genomic data interpretation.

Main Methods:

  • Review and synthesis of existing pathway-based analytical strategies.
  • Integration of concepts from genomics, systems biology, and bioinformatics.
  • Discussion of challenges and potential solutions for data integration.

Main Results:

  • Pathway-centric analysis provides a framework for interpreting complex genomic data.
  • The approach facilitates the connection between genomic variations and biological mechanisms.
  • Identified strategies to overcome challenges in current genomic data analysis.

Conclusions:

  • Pathway and network analysis are essential for harnessing multi-omics data.
  • This integrated approach promises deeper insights into disease and treatment mechanisms.
  • Future directions involve refining analytical strategies for complex biological systems.