1. A hidden Markov model for extracting nucleosome positions from tiling array data.

Reference: Yuan GC, Liu YJ, Dion MF, Slack MD, Wu LF, Altschuler SJ, Rando OJ.  Genome-scale identification of nucleosome positions in S. Cerevisiae.  Science 2005;309(5734):626-630 [paper]

Download MATLAB source code

2. N-score model: a wavelet analysis based model for predicting nucleosome positions from DNA sequence information.

References:

2a) Yuan GC, Liu JS. Genomic sequence is highly predictive of local nucleosome depletion. PLoS Computational Biology 2008. doi:10.1371/journal.pcbi.0040013.eor) [paper]

2b) Yuan GC. Targeted recruitment of histone modifications in humans predicted by genomic sequences. J Comput Biol 2009 Feb;16(2):341-355. [paper]

Download the latest and faster version in python (written by Yijing Zhang and Luca Pinello)

Download MATLAB source code

Download predicted nucleosome position: This is based on the 2003 version of yeast genome (Download fasta files)

3. MIM (Motif-Independent Metric) for sequence specificity

Reference: Pinello L, Lo Bosco G, Hanlon B, Yuan GC. A motif-independent metric for DNA sequence specificity. BMC Bioinformatics. 2011 Oct 21;12:408. [paper]

[Link to Github].

4. A hidden Markov model for identifying chromatin domains from multiple histone modificaiton data

Reference: Larson JL, Yuan GC. Epigenetic domains found in mouse embryonic stem cells via a hidden Markov model. BMC Bioinformatics. 2010 Nov 12;11:557. [paper]

[ Download Matlab and R codes].

5. MAnorm: a robust model for quantitative comparison of ChIP-seq datasets

Reference: Shao Z, Zhang Y, Yuan GC., Orkin SH, Waxman DJ. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets. Genome Biology. 2012 Mar 16;13(3):R16. [paper]

[Software page]

6. SCUBA: a method for single-cell gene expression analysis

Details will come soon.

[Link to Github]

7. PANDA: a message-passing method for gene regulatory network reconstruction

Reference: Glass K, Huttenhower C, Quackenbush J, Yuan GC. Passing Messages between Biological Networks to Refine Predicted Interactions. PLOS ONE. 2013 May 31, 8(5), e64832. [paper]

[Link to SourceForge]