Sample size estimation software tools | ChIP sequencing data analysis
ChIP-seq technology enables investigators to study genome-wide binding of transcription factors and mapping of epigenomic marks. Although the availability of basic analysis tools for ChIP-seq data is rapidly increasing, there has not been much progress on the related design issues. A challenging question for designing a ChIP-seq experiment is how deeply should the ChIP and the control samples be sequenced? The answer depends on multiple factors some of which can be set by the experimenter based on pilot/preliminary data.
A statistical framework for power calculations in ChIP-seq experiments by considering a local Poisson model, which is commonly adopted by many peak callers. Evaluations with simulations and data-driven computational experiments demonstrate that CSSP (ChIP-seq Statistical Power) can reliably estimate the power of a ChIP-seq experiment at different sequencing depths based on pilot data. Furthermore, it provides an analytical approach for calculating the required depth for a targeted power while controlling the false discovery rate at a user-specified level. Our results enable researchers to use their own or publicly available data for determining required sequencing depths of their ChIP-seq experiments and potentially make better use of the multiplexing functionality of the sequencers.
Chooses a diverse panel of genomic assays that leverages methods from submodular optimization. SSA serves as a model for how submodular optimization can be applied to other discrete problems in biology. This method is computationally efficient, results in high-quality panels according to several quality measures, and is mathematically optimal under some assumptions. It can be used partway through the investigation of a cell type, when several assays are already available. The tool can determine the most informative next experiments to perform.