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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.

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Related Experiment Video

Updated: Feb 22, 2026

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Finding optimum width of discretization for gene expressions using functional annotations.

Sampa Misra¹, Shubhra Sankar Ray¹

¹203 B.T. Road, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700108, India.

Computers in Biology and Medicine

|September 25, 2017

Summary

This summary is machine-generated.

Gene expression discretization is improved with Gene Annotation Based Discretization (GABD). This method enhances gene similarity analysis and disease prediction by reducing noise and errors in gene expression data.

Keywords:

Bioinformatics Computational biology Discretization Gene annotation Gene expression Pattern recognition Saccharomyces cerevisiae

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene expression data preprocessing is crucial for accurate analysis.
Noise and experimental errors in gene expression data can impact downstream tasks like network analysis and disease prediction.
Supervised discretization methods aim to improve data quality for biological insights.

Purpose of the Study:

To develop a novel supervised discretization method for gene expression data using gene annotations.
To evaluate the performance of the proposed method against existing discretization techniques.
To demonstrate the utility of the method in gene function prediction.

Main Methods:

Developed Gene Annotation Based Discretization (GABD) method.
Determined discretization width by maximizing positive predictive value (PPV) using gene annotations for gene pairs.
Compared GABD with equal width, equal frequency, and k-means discretization methods.

Main Results:

GABD captures gene similarity more effectively than original gene expressions.
GABD demonstrated superior performance in terms of PPV compared to existing methods.
Successfully predicted functions for 23 unclassified Saccharomyces cerevisiae genes using GABD and k-medoid clustering.

Conclusions:

GABD offers an effective approach for gene expression data discretization.
The method improves gene similarity assessment and aids in functional genomics applications.
The developed method and source code are valuable resources for the bioinformatics community.