Normalization software tools | ChIP sequencing data analysis
Chip-seq experiments are becoming a standard approach for genome-wide profiling protein-DNA interactions, such as detecting transcription factor binding sites, histone modification marks and RNA Polymerase II occupancy. However, when comparing a ChIP sample versus a control sample, such as Input DNA, normalization procedures have to be applied in order to remove experimental source of biases.
Allows comparison and integration of multiple ChIP-seq datasets and extraction of qualitative as well as quantitative information. seqMINER can handle the biological complexity of most experimental situations and proposes methods to the user for data classification according to the analysed features. In addition, through multiple graphical representations, seqMINER allows visualization and modelling of general as well as specific patterns in a given dataset.
Identifies peak regions in ChIP-Seq datasets that correspond to sites of transcription factor binding. PeakSeq scores the results of ChIP-Seq experiments by compensating for the mappability map and comparing against a normalized matching control dataset. This method was developed for use with tag sequence data from the Illumina Genome Analyzer platform. It can also be used to identify broader regions of binding that show significant enrichment relative to control.
Permits users to compare ChIP-Seq data sets. MAnorm is an application designed for quantitative comparison of ChIP-Seq data sets that have a substantial number of peak regions in common. This method is useful for both epigenetic modifications and transcription factors. This application can serve for obtaining cell type-specific and cell state-specific regulation during organism development and disease onset.
A user friendly program for analysis of ChIP-Seq experiments. PAPST allows users to interact with the significant peaks called from ChIP-seq experiments. It allows powerful co-localization analysis of multiple peaks sets. Written in pure Java, PAPST facilitates fast and interactive exploratory research of DNA binding by transcription factors and epigenetic modifications.
Estimates normalization factor between the ChIP and the control samples. NCIS can accommodate both low and high sequencing depth datasets. This software proceeds by evaluating available ChIP-seq normalization factor estimators through databased simulations. The method utilized is data-adaptive and extends CisGenome’s estimator by choosing the optimal value of bin-width and the threshold of total read counts in a data-adaptive manner.
Assists users with chromatin immunoprecipitation sequencing (ChIP-seq) data analyses. EaSeq is a computational environment which (i) provides extensive visualization and interactivity, (ii) combines the exploratory power of genome browsers with a comprehensive set of tools for genome-wide analysis and visualization and (iii) allows experimentalists to easily extract knowledge from hundreds of genome-wide datasets with a standard personal computer.
A diagnostic tool to examine the appropriateness of the estimated normalization procedure. By plotting the empirical densities of log relative risks in bins of equal read count, along with the estimated normalization constant, after logarithmic transformation, the researcher is able to assess the appropriateness of the estimated normalization constant.