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

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

Published on: December 10, 2012

Genome-wide identification of conserved intronic non-coding sequences using a Bayesian segmentation approach.

Manjula Algama¹, Edward Tasker¹, Caitlin Williams²

¹School of Mathematical Sciences, Monash University, Melbourne, VIC, 3800, Australia.

|March 29, 2017

Summary

This summary is machine-generated.

A new Bayesian method effectively identifies conserved non-coding RNAs (ncRNAs) in vertebrate genomes. This approach reveals numerous expressed ncRNAs within introns, particularly in transcription factors, highlighting their regulatory roles.

Keywords:

Bayesian modelling Conserved non-coding sequences Genome segmentation Putative functional elements ncRNA

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Last Updated: Mar 5, 2026

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

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Published on: December 10, 2012

In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression

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Published on: March 29, 2019

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Area of Science:

Genomics and Bioinformatics
Molecular Biology
Evolutionary Biology

Background:

Identifying non-coding RNAs (ncRNAs) computationally is a significant challenge in genomics.
Conserved intronic elements may harbor functional ncRNAs or regulatory sequences.
Cross-species conservation analysis is crucial for identifying functionally important genomic regions.

Purpose of the Study:

To develop and apply a novel Bayesian segmentation approach for genome-wide identification of conserved intronic elements.
To investigate the prevalence of ncRNAs and regulatory sequences within these conserved elements.
To compare a genome-wide analysis with a pathway-focused analysis for detecting conserved intronic sequences.

Main Methods:

Genome-wide analysis utilizing Bayesian segmentation to identify conserved intronic sequences across human, mouse, and zebrafish.
Pathway-focused analysis targeting genes involved in muscle development.
Bioinformatic analysis of RNA secondary structures and experimental validation using RT-PCR for expression analysis.

Main Results:

Identified 655 deeply conserved intronic sequences genome-wide and 27 elements in a muscle development pathway analysis.
A high percentage (87% genome-wide, 70% pathway-focused) of identified elements exhibit conserved RNA secondary structures.
RT-PCR confirmed expression for 26 pathway-focused elements, indicating they contain expressed ncRNAs, often found in transcription factor introns.

Conclusions:

The Bayesian approach is highly effective for identifying conserved non-coding sequences, particularly those with RNA secondary structures.
The majority of identified conserved sequences contain evidence of conserved RNA secondary structures and are expressed, suggesting functional ncRNA roles.
Pathway-focused analyses with improved alignment quality show higher sensitivity, indicating potential for discovering more conserved intronic sequences.