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Organization of Genes

Organization of Genes

Organization of Genes

Organization of Genes

Genome-wide Association Studies-GWAS

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 Assembly

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.

Gene Families

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...

Gene Families

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...

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Related Experiment Video

Updated: Jul 18, 2026

Development of Compendium for Esophageal Squamous Cell Carcinoma

Development of Compendium for Esophageal Squamous Cell Carcinoma

Published on: April 12, 2024

GO PaD: the Gene Ontology Partition Database.

Gil Alterovitz¹, Michael Xiang, Mamta Mohan

¹Division of Health Sciences and Technology Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA. gil@mit.edu

Nucleic Acids Research

|November 14, 2006

Summary

This study introduces the GO Partition Database, an information-theoretic approach to improve gene enrichment analysis. It maximizes information for gene function discovery by organizing Gene Ontology terms by specificity, avoiding common pitfalls.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene Ontology (GO) is crucial for inferring gene function in large genetic studies.
Current gene enrichment analysis often assumes GO term specificity based on graph level, which can be inaccurate.
This assumption can lead to incorrect conclusions and missed discoveries in functional genomics.

Purpose of the Study:

To address limitations in gene enrichment analysis caused by inaccurate GO term specificity assumptions.
To develop a novel approach that maximizes information for functional analysis of gene sets.
To provide researchers with a tool for analyzing gene datasets at arbitrary levels of specificity.

Main Methods:

Developed an information-theoretic approach encoded in the GO Partition Database.
Designed the database to feature ontology partitions with Gene Ontology terms of similar specificity.
Included information-theoretic statistics within GO partitions for flexible data analysis.

Main Results:

The GO Partition Database maximizes information for gene enrichment analysis.
It enables analysis at arbitrary levels of specificity, overcoming limitations of fixed-level approaches.
Provides functional analysis for genes across human and 10 other organisms.

Conclusions:

The GO Partition Database offers a more accurate and efficient method for gene functional analysis.
Researchers can now choose analysis specificity, improving discovery potential in genomics.
This resource enhances the utility of Gene Ontology in large-scale genetic studies.