HilbertVis

HilbertVis visualizes genome-position-dependent data by mapping integer vectors of scores (e.g., ChIP-chip, ChIP-Seq) onto two-dimensional Hilbert curves to preserve spatial locality and reveal distributional patterns.

Key Features:

• Hilbert curve mapping: Maps one-dimensional genomic data onto a two-dimensional plane using Hilbert curve algorithms.
• Integer-vector representation: Transforms genome-position-dependent scores into integer vectors for Hilbert mapping.
• Preservation of spatial relationships: Maintains proximity of genomic positions in the two-dimensional representation to facilitate pattern detection.
• Complementary analysis with genome browsers: Provides an alternative visualization that highlights structural aspects of data not emphasized by linear genome browsers.

Scientific Applications:

• ChIP-chip and ChIP-Seq interpretation: Enhances visualization and interpretation of ChIP-chip and ChIP-Seq score distributions across the genome.
• Detection of spatial clustering: Aids identification of spatial distribution and clustering of genomic features.
• Exploration of genomic feature patterns: Facilitates discovery of underlying biological patterns and structures in genome-position-dependent datasets.

Methodology:

Genome-position-dependent scores are converted into integer vectors and mapped onto a two-dimensional Hilbert curve using a Hilbert curve algorithm, preserving spatial relationships.