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SSA / Submodular Selection of Assays
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.
CSSP / ChIP-SEQ Statistical Power
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.
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