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1. HMCan Algorithm:

Histone Modification in Cancer (HMCan) is Hidden Markov Model (HMM) based tool that is developed to detect histone modification in cancer ChIP-seq data.It applies three correction steps to the data: copy number correction, GC bias correction and noise level correction.
HMCan has two main stages: data profiling stage, and peak calling stage. In data profiling stage, HMCan constructs the density profile from the aligned reads, and then normalizes it. Normalization includes: Library size normalization, copy number normalization, GC bais normalization and noise level correction. For peak calling, HMCan estimates initial HMMs parameters using right side exact Poisson's test. Then, it applies iterative HMMs to fine tune parameters and perform final peak calling.

HMCan workflow


2. HMCan Input:

HMCan accepts multiple types of alignment file formats, that include: BAM, SAM,BED


3. HMCan Output:

HMCan outputs 4 files, those files are:

  • Peaks: a BED file contains the coordinates of the enriched regions for a certain mark.
  • Regions: a BED file contains the coordinates of the enriched regions for a certain mark.
  • Density: a WIG file contains the normalized for each data bin.
  • Posterior Probability: a WIG file contains the posterior probability of each bin to be enriched given its value.

HMCan snapshot

King Abdullah University of Science and Technology / Computational Bioscience Research Center ©2017