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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

An algorithm for chemical genomic profiling that minimizes batch effects: bucket evaluations.

Daniel Shabtai¹, Guri Giaever, Corey Nislow

¹Department of Cell and Systems Biology and the Donnelly Centre, University of Toronto, Toronto, ON, Canada.

BMC Bioinformatics

|September 27, 2012

Summary

This summary is machine-generated.

A new method called Bucket Evaluations (BE) accurately compares chemical genomic profiles to find similarities between experiments. This approach minimizes batch effects and is applicable to various datasets, including gene expression and sequencing data.

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Area of Science:

Genomics
Chemical Biology
Bioinformatics

Background:

Chemical genomics integrates small molecule perturbation with genomics to understand gene function and drug action.
Large-scale chemical genomic screens capture comprehensive mutant or gene knock-down sensitivities.
Comparing these profiles requires robust methods to correlate large datasets and overcome experimental variations like batch effects.

Purpose of the Study:

To develop a novel algorithm for analyzing chemogenomic profiles.
To identify potential drug targets and compounds with similar activity profiles.
To provide a robust and extensible method for comparing diverse biological datasets.

Main Methods:

Developed the Bucket Evaluations (BE) algorithm using levelled rank comparisons.
BE minimizes batch effects by avoiding the need to pre-define disrupting experimental influences.
The method was validated on gene expression microarray and high-throughput sequencing chemogenomic data.

Main Results:

The BE algorithm accurately identifies drugs and compounds with similar chemogenomic profiles.
Levelled rank comparisons effectively minimize batch effects, revealing true biological signals.
The method demonstrated applicability across various dataset types, including gene expression and sequencing data.

Conclusions:

Bucket Evaluations (BE) is a highly accurate, non-parametric correlation approach for comparing experimental datasets.
BE is suitable for analyzing perturbed datasets like chemical genomic profiles.
Publicly available software and a user interface for BE have been developed.