Table 1 The main tools of the ChIPseeqer framework.

Offers basic quality control tools.

Splits read files (e.g., bed, eland) into one read file per chromosome.

Peak detection algorithm.

Creates a promoters-based annotation of the detected peaks (i.e., gene name-description, peaks)

Finds the peaks distribution in the genome (e.g., exons/introns/intergenic) and creates lists of these peaks.

Creates a UCSC Genome Browser track for the detected peaks.

Creates a UCSC Genome Browser track for the reads density.

Finds the peaks that overlap with repeating elements, CpG islands and segmental duplicates.

Runs FIRE for the detected peaks, in order to perform an unsupervised motif discovery.

Runs MyScanACE for the detected peaks, in order to look for specific motifs (Jaspar, Bulyk PBM databases).

Runs PAGE for the genes associated with the detected peaks, in order to perform pathways analysis.

Looks for genes (and their corresponding peaks) that are associated to a specific pathway (e.g., apoptosis, GO:0060742).

Estimates the conservation scores for the detected peaks and for random intervals to allow comparison.

Creates a reads density matrix for a window around the TSS or the TES of the genes, or for any interval selected.

Estimates the avg/max reads number for every input peak, across multiple ChIP-seq datasets and creates a peak-based reads matrix.

Clusters a matrix (e.g., k-means, hierarchical, SOMs) and visualizes the clustering.

Compares two lists of peaks and finds the overlapping peaks and the peaks that are unique in each list.

Compares two lists of genes and finds the common genes and the genes that are unique in each list.

Estimates the Jaccard similarity coefficient for a set of peak files. The larger the coefficient, the more similarity you have between two peak files

Creates gene-based matrices (one for promoters, one for exons, etc) for many peak files. Summarizes the number of peaks that fall in specific gene parts, across many different peak files (TFs).

Finds peaks that are away from known genes.

Finds the closest gene(s) for each peak.

Estimates the avg/max reads number for every peak, for a ChIP-seq dataset and creates a peak-based read matrix.

Extracts the peaks that have a specific FIRE motif (can be applied after running FIRE).

Creates the input file for iPAGE from a list of genes.