21. Giotto: a pipeline for single-cell spatial transcriptomic data analysis and visualization.

Reference: Dries D*, Zhu Q*, Eng CL, Sarkar A, Bao F, George RE, Pierson N, Cai L, Yuan GC. Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data.

[ Link to Website ][ Link to bioRxiv ]

20. DWLS: a method for cell-type deconvolution using single-cell RNA-sequencing data.

Reference: Tsoucas D, Dong R, Chen H, Zhu Q, Guo G, Yuan GC. Accurate estimation of cell-type composition from gene expression data. Nature Communications. (in press)

[ Link to Website ]

19. RESCUE. A method for imputing dropouts in single-cell RNA-sequencing data.

Reference: Tracy S, Yuan GC, Dries R. RESCUE: imputing dropout events in single-cell RNA-sequencing data. BMC Bioinformatics. (in press)

[ Link to Website ]

18. STREAM: a method for trajectory analysis from single-cell RNAseq and ATACseq data.

Reference: Chen H, Albergante L, Hsu JY, Lareau CA, Lo Bosco G, Guan J, Zhou S, Gorban AN, Bauer DE, Aryee MJ, Langenau DM, Zinovyev A, Buenrostro JD, Yuan GC, Pinello L. Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat Commun. 2019 Apr 23;10(1):1903.

[ Link to Website ]

17. CUT&RUNTools. A pipeline for analyzing CUT&RUN data.

Reference: Zhu Q, Liu N, Orkin SH, Yuan GC. CUT&RUNTools: a flexible pipeline for CUT&RUN processing and footprint analysis [ preprint ].

[ Link to Website ]

16. Predicted regulators of cell identity in the mouse cell atlas.

Reference: Suo S, Zhu Q, Saadatpour A, Fei L, Guo G, Yuan GC. Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas. Cell Rep. 2018 Nov 6;25(6):1436-1445.e3 [ paper ].

[ Link to Website ]

15. Spatial transcriptomic analysis: A computational tool for integrating sequencing- and imaging-based single-cell transcriptomic data, including cell-type mapping and spatial domain identification.

Reference: Zhu Q, Shah S, Dries R, Cai L, Yuan GC. Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data. Nat Biotechnol. 2018 Oct 29. [Epub ahead of print] [ paper ]

[ Link to Website ]

14. GiniClust2: An extension of GiniClust for simultaneous detection of both common and rare cell types from single cell data.

References: Tsoucas D, Yuan GC. GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection. Genome Biology. 2018 May 10;19(1):58. [ paper ]

Jiang L, Chen H, Pinello L, Yuan GC. GiniClust: detecting rare cell types from single-cell gene expression data with Gini index. Genome Biol. 2016 Jul 1;17(1):144. [ paper ].

[ Link to Github ]

13. Haystack: A method for quantifying epigenetic variability and prediction of driving transcription factors

References: Pinello L, Farouni R, Yuan GC. Haystack: systematic analysis of the variation of epigenetic states and cell-type specific regulatory elements. Bioinformatics. 2018 Jan 17 January. [ paper ]

Pinello L, Xu J, Orkin SH, Yuan GC. Analysis of chromatin-state plasticity identifies cell-type-specific regulators of H3K27me3 patterns. Proc Natl Acad Sci U S A. 2014 Jan 21;111(3):E344-53. [ paper ]

[ Link to Github ]

12. diHMM: a method for multi-scale chromatin state annotation from ChIPseq data.

Reference: Marco E*, Meuleman W*, Huang J*, Glass K, Pinello L, Wang J, Kellis M, Yuan GC. Multi-scale chromatin state annotation using a hierarchical hidden Markov model. Nature Communications. 2017 Apr 7;8:15011. [ paper ].

[Link to GitHub] [ Chromatin state annotations in 3 human cell lines ].

11. GiniClust: a method for detecting rare cell types from single-cell gene expression data

Reference: Jiang L, Chen H, Pinello L, Yuan GC. GiniClust: detecting rare cell types from single-cell gene expression data with Gini index. Genome Biol. 2016 Jul 1;17(1):144. [ paper ].

[Link to GitHub]

10. CRISPResso: a software package for analyzing deep-sequencing CRISPR-Cas9 genome editing outcome data

Reference: Pinello L, Canver MC, Hoban MD, Orkin SH, Kohn DB, Bauer DE#, Yuan GC#. CRISPResso: sequencing analysis toolbox for CRISPR-Cas9 genome editing. Nat Biotechnol. 2016 Jul 12;34(7):695-7. [ paper ].

[Link to GitHub]

[Link to Web Application]

9. ECLAIR: a method for robust lineage reconstruction from single-cell gene expression data

Reference: Giecold G, Marco E, Trippa L, Yuan GC. Robust Lineage Reconstruction from High-Dimensional Single-Cell Data. Nucleic Acids Res. 2016 May 20. pii: gkw452. [ paper ]

[Link to GitHub]

8. HubPredictor: a method for predicting chromatin interaction hubs using histone marks information.

Reference: Huang J, Marco E, Pinello L, Yuan GC. Predicting chromatin organization using histone marks. Genome Biol. 2015 Aug 14;16(1):162. [ paper ]

[Link to GitHub]

7. SCUBA: a method for extracting lineage relationships and modeling gene expression dynamics from single-cell gene expression data.

Reference: Marco E, Karp RL, Guo G, Robson P, Hart AH, Trippa L, Yuan GC. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc Natl Acad Sci U S A. 2014 Dec 30;111(52):E5643-50. [paper]

[Link to GitHub]

6. 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]

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]

4. 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].

3. 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].

2. N-score: 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)

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