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Table 1 Comparison of selected software tools for ChIP-Seq data analysis

From: jChIP: a graphical environment for exploratory ChIP-Seq data analysis

Application

Interface

Installation

Platform

Functionality

Pros

Cons

UCSC genome browser [7]

web-based

not needed

platform independent

genome browsing, tracks displaying, tracks comparing, referring to known genomic features

no installation, graphical interface, rich annotation database

batch process of a large number of data files not possible

BEDTools [6]

command-line

compilation from source through package managers

UNIX LINUX MacOS

interrogation and manipulation of genomic features, comparisons of discontinuous features

fast, divided into several applications

no graphical interface,

HOMER [3]

command-line

Perl installation scripts

UNIX LINUX MacOS Cygwin

data visualisation, peak and enriched motif finding, assembling data across multiple experiments, annotating peaks, basic quality control (sequence bias, fragment length estimation), creating histograms, and heatmaps, re-centering peaks on motifs

fast, divided into several applications, multiple additional scripts helpful by analysis

no graphical interface

ChipSeeker [5]

R package

through R package manager

platform independent (R package needed)

data visualisation, peak detection, pathways enrichment analysis, retrieving the nearest genes around the peak, genomic region annotation, peak significance estimation, conservation analysis, clustering analysis, data comparison with GEO database

interaction with other R packages,

R environment required, programming skill needed

CisGenome [4]

GUI (MS Windows only) command line

compilation from source installer (for MS Windows)

packages for all platforms (GUI only for MS Windows)

peak detection, gene annotation, motif analysis, motif mapping, novel motif discovery, data visualisation

GUI (MS Windows only), divided into several applications

no graphical interface (Linux, UNIX, MacOS)

jChIP

GUI

not required

platform independent (Java runtime environment required)

data visualisation, matching reads to genomic locations, datasets comparision, creating reads count histograms, basic quality control

no installation, graphical interface

only exploratory analysis available