Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based deconvolution of genome-wide DNA binding
Bioinformatics
An Efficient Algorithm for Optimal Multilevel Thresholding of Irregularly Sampled Histograms
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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Genome-wide profiling of DNA-binding proteins using ChIP-Seq has emerged as an alternative to ChIP-chip methods. Due to the large amounts of data produced by next generation sequencing, ChIP-Seq offers many advantages, such as much higher resolution, less noise and greater coverage than its predecessor, the ChIP-chip array. Multi-level thresholding algorithms have been applied to many problems in image and signal processing. These algorithms have been used for transcriptomics and genomics data analysis such as sub-grid and spot detection in DNA microarrays, and also for detecting significant regions based on next generation sequencing data. We show that our Optimal Multilevel Thresholding algorithm (OMT) has higher accuracy in detecting enriched regions (peaks) in comparison with previously proposed peak finders by testing three algorithms on the well-known FoxA1 Data set and also for four transcription factors (with a total of six antibodies) for Drosophila melanogaster. Using a small number of parameters is another advantage of the proposed method.