locsmoc

locsmoc constructs piecewise polynomial models of one-dimensional genomic signals from micro-array and sequencing projects to represent and analyze genome-associated functions along chromosomal coordinates.

Key Features:

• Piecewise Polynomial Framework: Provides a general framework for building piecewise polynomial representations of genome-scale signals.
• Segmentation Capabilities: Performs piecewise-constant segmentation for copy-number analyses on array and DNA sequencing data.
• Higher-Order Polynomial Curves: Fits higher-order polynomial curves to detect trends and discontinuities in transcription levels from RNA-seq data.
• Alignment Quality Diagnosis: Applies piecewise-linear functions to diagnose and quantify alignment quality at exon borders (splice sites).

Scientific Applications:

• Copy-Number Analysis: Detects copy-number alterations via piecewise-constant segmentation of array and DNA sequencing signals.
• Transcription Level Analysis: Identifies trends and discontinuities in transcription using higher-order polynomial fits on RNA-seq data.
• Sequencing Data Quality Assessment: Quantifies alignment quality at exon borders (splice sites) with piecewise-linear models.

Methodology:

Transforms genomic associations into piecewise-polynomial curves and applies piecewise-constant segmentation, higher-order polynomial fitting, and piecewise-linear functions for alignment diagnosis.