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Related Experiment Video

Updated: Jun 29, 2026

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

BATS: a Bayesian user-friendly software for analyzing time series microarray experiments.

Claudia Angelini¹, Luisa Cutillo, Daniela De Canditiis

¹Istituto per le Applicazioni del Calcolo, Mauro Picone, CNR-Napoli, Italy. c.angelini@iac.cnr.it

BMC Bioinformatics

|October 8, 2008

Summary

This summary is machine-generated.

The Bayesian Analysis of Time Series (BATS) software efficiently identifies and ranks differentially expressed genes from time-course microarray data. This free tool aids researchers in understanding gene expression changes over time in response to various treatments.

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Area of Science:

Molecular Biology
Bioinformatics
Computational Biology

Background:

Gene expression is influenced by treatments and varies over time.
High-throughput analysis of time-course microarray data requires specialized tools.
Identifying genes associated with biological processes or treatments is a key goal.

Purpose of the Study:

To present the Bayesian Analysis of Time Series (BATS) software.
To provide a computationally efficient method for analyzing time-course microarray data.
To identify, rank, and estimate expression profiles of differentially expressed genes.

Main Methods:

Developed a fully Bayesian approach for analyzing one-sample time-course microarray experiments.
Implemented the methodology in the user-friendly BATS software package.
The method utilizes explicit expressions for computational efficiency.

Main Results:

BATS automatically identifies and ranks differentially expressed genes.
The software estimates gene expression profiles with at least 5-6 time points.
BATS handles technical challenges like small sample sizes, non-uniform sampling, and missing data.

Conclusions:

BATS is a free, user-friendly software for analyzing simulated and real microarray time-course experiments.
The software is available online with a user manual and example.
Facilitates high-throughput identification of genes in time-course studies.