Wiggler

Wiggler generates normalized genome-wide signal coverage tracks from raw read alignment files to facilitate analysis of chromatin properties across sequencing assays such as ChIP-seq, DNase-seq, FAIRE-seq, and MNase-seq.

Key Features:

• Normalized coverage tracks: Produces normalized genome-wide signal coverage tracks from raw read alignment files.
• Replicate pooling: Supports pooling of replicate datasets while allowing per-replicate parameters for read shifting and smoothing.
• Read shifting and smoothing: Applies read shifting and smoothing tailored to individual replicates and data types.
• Signal density mapping: Generates signal density maps for ChIP-seq, DNase-seq, FAIRE-seq, and MNase-seq data.
• Mappability modeling: Models variability in mappability to normalize signal density and distinguish missing data from true zero signals.
• Annotation map generation: Converts multiple chromatin datasets into discrete annotation maps for regulatory element annotation.

Scientific Applications:

• Large-scale chromatin mapping: Processes datasets from projects such as ENCODE to map chromatin properties across human cell lines.
• Regulatory element annotation: Aids systematic annotation of non-coding regulatory elements by integrating multiple chromatin data tracks.
• Evolutionary constraint analysis: Enables correlation of annotated non-coding regulatory elements with mammalian evolutionary constraints.
• Disease-locus investigation: Facilitates re-examination of disease-associated loci by generating chromatin-based hypotheses.
• Quiescent region identification: Reveals regions of the genome that remain quiescent across different cell types.
• Chromatin–transcription relationships: Supports analysis of the interplay between chromatin activity and RNA transcription.

Methodology:

Processes raw read alignment files to produce normalized genome-wide coverage tracks; pools replicates while applying per-replicate read shifting and smoothing; models mappability variability to normalize signal and distinguish missing data from true zeros; generates signal density maps for ChIP-seq, DNase-seq, FAIRE-seq, and MNase-seq and converts multiple chromatin datasets into discrete annotation maps.