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Probe-Level Universal Search (PLUS) algorithm for gender differentiation in affymetrix datasets.

Anna S Karyagyna¹, Michail O Vassiliev, Anna S Ershova

¹NF Gamaleya Research Institute of Epidemiology and Microbiology, Russian Academy of Medical Sciences, Institute of Agricultural Biotechnology, Moscow, Russia. akaryagina@gmail.com

Journal of Bioinformatics and Computational Biology

|June 18, 2010

Summary

A new algorithm, Probe Level Universal Search (PLUS), analyzes gene expression data at the probe level. It identifies specific probes that accurately determine sample gender and detect chromosomal abnormalities.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Affymetrix microarrays measure gene expression using probe sets, converting individual probe signals into a single gene expression value.
This aggregation process can obscure important information from individual probes, potentially impacting downstream analysis.
Current methods often overlook the value of analyzing probe-level data independently.

Purpose of the Study:

To introduce a novel feature selection algorithm, the Probe Level Universal Search (PLUS) algorithm, for probe-level analysis of gene expression data.
To develop a method for identifying specific probes capable of distinguishing between different sample classes.
To apply the PLUS algorithm for accurate gender differentiation using Affymetrix microarray data.

Main Methods:

The PLUS algorithm evaluates individual perfect-match Affymetrix probe intensities.
It selects probes that effectively differentiate between two predefined sample classes.
The algorithm was specifically applied to identify probes for gender differentiation in gene expression datasets.

Main Results:

A universal set of 3' Gene Affymetrix microarray probes for gender differentiation was identified.
This set includes 38 probes from the XIST gene (X-chromosome) and 17 probes from five Y-chromosome genes (RPS4Y1, EIF1A, DDX3Y, JARID1D, USP9Y).
The selected probes accurately differentiated sample sex chromosome karyotype, identified mislabeled samples, and detected chromosomal abnormalities associated with syndromes and cancer.

Conclusions:

The PLUS algorithm provides a powerful tool for probe-level analysis in gene expression studies.
Individual probe analysis, as facilitated by PLUS, enhances the accuracy of sample classification and identification of biological variations.
This approach has significant implications for clinical diagnostics, including the detection of genetic disorders and cancer-related chromosomal aberrations.