TY - GEN
T1 - Chromatyping
T2 - 22nd International Conference on Research in Computational Molecular Biology, RECOMB 2018
AU - Chakraborty, Shounak
AU - Canzar, Stefan
AU - Marschall, Tobias
AU - Schulz, Marcel H.
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Measuring nucleosome positioning in cells is crucial for the analysis of epigenetic gene regulation. Reconstruction of nucleosome profiles of individual cells or subpopulations of cells remains challenging because most genome-wide assays measure nucleosome positioning and DNA accessibility for thousands of cells using bulk sequencing. Here we use characteristics of the NOMe-sequencing assay to derive a new approach, called ChromaClique, for deconvolution of different nucleosome profiles (chromatypes) from cell subpopulations of one NOMe-seq measurement. ChromaClique uses a maximal clique enumeration algorithm on a newly defined NOMe read graph that is able to group reads according to their nucleosome profiles. We show that the edge probabilities of that graph can be efficiently computed using Hidden Markov Models. We demonstrate using simulated data that ChromaClique is more accurate than a related method and scales favorably, allowing genome-wide analyses of chromatypes in cell subpopulations. Software is available at https://github.com/shounak1990/ChromaClique under MIT license.
AB - Measuring nucleosome positioning in cells is crucial for the analysis of epigenetic gene regulation. Reconstruction of nucleosome profiles of individual cells or subpopulations of cells remains challenging because most genome-wide assays measure nucleosome positioning and DNA accessibility for thousands of cells using bulk sequencing. Here we use characteristics of the NOMe-sequencing assay to derive a new approach, called ChromaClique, for deconvolution of different nucleosome profiles (chromatypes) from cell subpopulations of one NOMe-seq measurement. ChromaClique uses a maximal clique enumeration algorithm on a newly defined NOMe read graph that is able to group reads according to their nucleosome profiles. We show that the edge probabilities of that graph can be efficiently computed using Hidden Markov Models. We demonstrate using simulated data that ChromaClique is more accurate than a related method and scales favorably, allowing genome-wide analyses of chromatypes in cell subpopulations. Software is available at https://github.com/shounak1990/ChromaClique under MIT license.
UR - http://www.scopus.com/inward/record.url?scp=85046143242&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046143242&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-89929-9_2
DO - 10.1007/978-3-319-89929-9_2
M3 - Conference contribution
AN - SCOPUS:85046143242
SN - 9783319899282
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 21
EP - 36
BT - Research in Computational Molecular Biology - 22nd Annual International Conference, RECOMB 2018, Proceedings
A2 - Raphael, Benjamin J.
PB - Springer Verlag
Y2 - 21 April 2018 through 24 April 2018
ER -