GRiNCH: simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization

Lee, Da-Inn; Roy, Sushmita

doi:10.1186/s13059-021-02378-z

Cited by 23 publications

(31 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applying NMF to a Hi-C matrix yields low-dimensional factors or latent features which can be used to cluster the row or the column entities, i.e., genomic regions. These clusters of genomic regions correspond to densely interacting regions of the genome such as topologically associating domains (TADs) ( Lee and Roy, 2021 ). MVNMF extends NMF to a multi-task setting with multiple input matrices, each corresponding to a task or a time point (e.g.…”

Section: Resultsmentioning

confidence: 99%

“…We use a simple heuristic to pick

, the number of the factors, which also is the number of clusters. Based on our previous work on single-task NMF to Hi-C data, we set

such that the expected size of each cluster is about 1 Mb in length, which corresponds to the average size of TADs ( Lee and Roy, 2021 ). For example, for an input matrix that corresponds to a chromosome of size 10 Mb, we set

Here, we used 56-282 factors to capture TAD like structures, corresponding to the size of the rat chromosomes.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Deciphering the Role of 3D Genome Organization in Breast Cancer Susceptibility

et al. 2022

Self Cite

View full text Add to dashboard Cite

Cancer risk by environmental exposure is modulated by an individual’s genetics and age at exposure. This age-specific period of susceptibility is referred to as the “Window of Susceptibility” (WOS). Rats have a similar WOS for developing breast cancer. A previous study in rat identified an age-specific long-range regulatory interaction for the cancer gene, Pappa, that is associated with breast cancer susceptibility. However, the global role of three-dimensional genome organization and downstream gene expression programs in the WOS is not known. Therefore, we generated Hi-C and RNA-seq data in rat mammary epithelial cells within and outside the WOS. To systematically identify higher-order changes in 3D genome organization, we developed NE-MVNMF that combines network enhancement followed by multitask non-negative matrix factorization. We examined three-dimensional genome organization dynamics at the level of individual loops as well as higher-order domains. Differential chromatin interactions tend to be associated with differentially up-regulated genes with the WOS and recapitulate several human SNP-gene interactions associated with breast cancer susceptibility. Our approach identified genomic blocks of regions with greater overall differences in contact count between the two time points when the cluster assignments change and identified genes and pathways implicated in early carcinogenesis and cancer treatment. Our results suggest that WOS-specific changes in 3D genome organization are linked to transcriptional changes that may influence susceptibility to breast cancer.

show abstract

Section: Resultsmentioning

confidence: 99%

“…We use a simple heuristic to pick

, the number of the factors, which also is the number of clusters. Based on our previous work on single-task NMF to Hi-C data, we set

Here, we used 56-282 factors to capture TAD like structures, corresponding to the size of the rat chromosomes.…”

Section: Methodsmentioning

confidence: 99%

Deciphering the Role of 3D Genome Organization in Breast Cancer Susceptibility

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lee et al [ 85 ] developed a clustering method based on constrained non-negative matrix-factorization (NMF), called GRiNCH. GRiNCH is based on NMF, which is a strong dimensionality reduction technique to infer meaningful low-dimensional structures from higher-dimensional data.…”

Section: Methodsmentioning

confidence: 99%

“…Only in last two years, many techniques have been proposed to detect TADs across different species. These novel methods include the most recent TADBD, SpectralTAD, OnTAD, Constrained HAC, MSTD, GRiNCH, and deDoc [ 1 , 3 , 56 , 57 , 59 – 85 ]. Up-to-now, there are almost 31 TAD callers and Table 1 summarizes the characteristics of these methods.…”

Section: Introductionmentioning

confidence: 99%

A comparison of topologically associating domain callers over mammals at high resolution

Sefer

2022

BMC Bioinformatics

View full text Add to dashboard Cite

Background Topologically associating domains (TADs) are locally highly-interacting genome regions, which also play a critical role in regulating gene expression in the cell. TADs have been first identified while investigating the 3D genome structure over High-throughput Chromosome Conformation Capture (Hi-C) interaction dataset. Substantial degree of efforts have been devoted to develop techniques for inferring TADs from Hi-C interaction dataset. Many TAD-calling methods have been developed which differ in their criteria and assumptions in TAD inference. Correspondingly, TADs inferred via these callers vary in terms of both similarities and biological features they are enriched in. Result We have carried out a systematic comparison of 27 TAD-calling methods over mammals. We use Micro-C, a recent high-resolution variant of Hi-C, to compare TADs at a very high resolution, and classify the methods into 3 categories: feature-based methods, Clustering methods, Graph-partitioning methods. We have evaluated TAD boundaries, gaps between adjacent TADs, and quality of TADs across various criteria. We also found particularly CTCF and Cohesin proteins to be effective in formation of TADs with corner dots. We have also assessed the callers performance on simulated datasets since a gold standard for TADs is missing. TAD sizes and numbers change remarkably between TAD callers and dataset resolutions, indicating that TADs are hierarchically-organized domains, instead of disjoint regions. A core subset of feature-based TAD callers regularly perform the best while inferring reproducible domains, which are also enriched for TAD related biological properties. Conclusion We have analyzed the fundamental principles of TAD-calling methods, and identified the existing situation in TAD inference across high resolution Micro-C interaction datasets over mammals. We come up with a systematic, comprehensive, and concise framework to evaluate the TAD-calling methods performance across Micro-C datasets. Our research will be useful in selecting appropriate methods for TAD inference and evaluation based on available data, experimental design, and biological question of interest. We also introduce our analysis as a benchmarking tool with publicly available source code.

show abstract

“…It is worth mentioning that a set of methods were particularly developed for some variant Hi-C techniques such as ChIA-PET [76] , capture-Hi-C [77] ), HiChIP [78] and Split-Pool Recognition of Interactions by Tag Extension (SPRITE) [79] due to their sparse or asymmetric interaction matrices. GRiNCH [80] is one such tool based on a non-negative matrix factorization and graph regularization, to identify TADs of genome organization from sparse chromosome interaction matrices.…”

Section: Computational Methods In Mapping 3d Chromatin Domains and In...mentioning

confidence: 99%

Mapping nucleosome and chromatin architectures: A survey of computational methods

Fang

Wang²,

Liu³

et al. 2022

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

GRiNCH: simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization

Cited by 23 publications

References 86 publications

Deciphering the Role of 3D Genome Organization in Breast Cancer Susceptibility

Deciphering the Role of 3D Genome Organization in Breast Cancer Susceptibility

A comparison of topologically associating domain callers over mammals at high resolution

Mapping nucleosome and chromatin architectures: A survey of computational methods

Contact Info

Product

Resources

About