CATCHprofiles: Clustering and Alignment Tool for ChIP Profiles

Chromatin Immuno Precipitation (ChIP) profiling detects in vivo protein-DNA binding, and has revealed a large combinatorial complexity in the binding of chromatin associated proteins and their post-translational modifications. To fully explore the spatial and combinatorial patterns in ChIP-profiling data and detect potentially meaningful patterns, the areas of enrichment must be aligned and clustered, which is an algorithmically and computationally challenging task. We have developed CATCHprofiles, a novel tool for exhaustive pattern detection in ChIP profiling data. CATCHprofiles is built upon a computationally efficient implementation for the exhaustive alignment and hierarchical clustering of ChIP profiling data. The tool features a graphical interface for examination and browsing of the clustering results. CATCHprofiles requires no prior knowledge about functional sites, detects known binding patterns “ab initio”, and enables the detection of new patterns from ChIP data at a high resolution, exemplified by the detection of asymmetric histone and histone modification patterns around H2A.Z-enriched sites. CATCHprofiles' capability for exhaustive analysis combined with its ease-of-use makes it an invaluable tool for explorative research based on ChIP profiling data

On-line Edge-Coloring with a Fixed Number of Colors

We investigate a variant of on-line edge-coloring in which there is a fixed number of colors available and the aim is to color as many edges as possible. We prove upper and lower bounds on the performance of different classes of algorithms for the problem. Furthermore, we determine the performance of two specific algorithms, First-Fit and Next-Fit

On-Line Edge-Coloring with a Fixed Number of Colors

We investigate a variant of on-line edge-coloring in which there is a fixed number of colors available and the aim is to color as many edges as possible. We prove upper and lower bounds on the performance of different classes of algorithms for the problem. Moreover, we determine..

Example profile of PolII cluster with marks of active transcription.

<p>The average profile pattern of cluster 12750 (containing 2093 profiles) from the CATCH clustering of PolII binding sites. The profile pattern has a high signal for both H3K4me3 and all the histone acetylation marks, which are known to correlate with active transcription. 81% of the profiles are within 1 kb of annotated Ensembl TSS, and of the remaining 389 regions, 253 were within 1 kb of Aceview predicted TSS.</p

Correlation of pattern orientation with orientation of CTCF motif for each of the H2A.Z clusters.

<p>Only the CTCF containing patterns with a clear H2A.Z peak show correlation with the orientation of the CTCF motif. Promoter: Marks of active promoters including PolII, histone acetylation and histone methylation marks. Met: Histone methylation.</p

The orientation of the CTCF/H2A.Z pattern is correlated with the orientation of the CTCF binding motif.

<p>(a) Of the eleven zincfingers in CTCF, only four are required for strong binding. The orientation of the binding with respect to the CTCF motif was determined by Renda et al <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028272#pone.0028272-Renda1" target="_blank">[23]</a>. (b) The dominant orientation of the CTCF/H2A.Z pattern with respect to the orientation of the underlying CTCF motif. (c) The CTCF motif as derived from motif detection in genome-wide CTCF peaks in the ChIP-seq dataset of Barski et al <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0028272#pone.0028272-Barski1" target="_blank">[6]</a>.</p