maskBAD

maskBAD identifies and masks microarray probes whose signal differences are likely caused by sequence divergence, improving accuracy of comparative gene expression analysis across species or strains.

Key Features:

• Identification of Problematic Probes: Identifies microarray probes whose signals are likely affected by sequence divergence using only the microarray data.
• Correction of False Expression Differences: Removes or excludes identified probes to reduce falsely reported expression differences, removing 98% of false differences in simulated datasets.
• Efficiency in Probe Detection and Exclusion: Detects approximately 70% of probes with sequence differences in human and chimpanzee data while excluding 18% of probes without sequence differences.

Scientific Applications:

• Cross-Species Gene Expression Analysis: Mitigates sequence-divergence-induced artifacts in comparative gene expression studies across species or strains.
• Enhanced Data Reliability: Improves interpretation of microarray data for evolutionary biology, genetic research, and comparative genomics by reducing false expression differences due to probe-target mismatches.

Methodology:

Performs statistical analysis of probe-level microarray signals to identify probes exhibiting sequence-divergence-induced discrepancies, operating entirely on the microarray dataset without requiring external sequence information.