les

les estimates loci of enhanced significance in tiling microarray data to identify regions involved in differential transcription, ChIP-chip, and DNA modification analysis and to quantify regulation at the gene-set level without predefined significance thresholds or reference sets.

Key Features:

• Loci estimation: Estimates Loci of Enhanced Significance (LES) in tiling microarray data without relying on predefined thresholds or a reference set.
• Gene-set focus: Assesses regulation of entire gene sets and estimates the number of differentially expressed genes within a set rather than testing individual genes only.
• Applicability to tiling arrays: Provides a universal framework applicable to differential transcription, ChIP-chip, and DNA modification analyses on tiling microarray datasets.
• Model independence: Operates independently of specific statistical models at the probe level.
• Implementation: Distributed as an R package for integration into computational workflows.

Scientific Applications:

• Differential transcription analysis: Identifying genomic regions with altered transcriptional activity using tiling microarray data.
• ChIP-chip analysis: Detecting protein–DNA interaction loci across tiled genomic regions.
• DNA modification mapping: Locating regions with differential DNA modifications measured by tiling arrays.
• Gene-set regulation inference: Estimating numbers of differentially expressed genes within predefined gene sets to assess coordinated regulation.
• Comparative induction analysis: Comparing induction levels across different gene sets to characterize relative regulatory responses.

Methodology:

Estimates LES without predefined significance thresholds or a reference set; assesses regulation at the gene-set level rather than per-gene testing; functions independently of specific probe-level statistical models; implemented as an R package.