Iterative Correction of Hi-C Data Reveals Hallmarks of Chromosome Organization

By Maxim Imakaev, Geoffrey Fudenberg, R. McCord, N. Naumova, Anton Goloborodko, B. Lajoie, Job Dekker, Leonid A. Mirny. Nature Methods, 2012

1,380 citations recorded by the source corpus.

Extracting biologically meaningful information from chromosomal interactions obtained with genome-wide chromosome conformation capture (3C) analyses requires the elimination of systematic biases. We present a computational pipeline that integrates a strategy to map sequencing reads with a data-driven method for iterative correction of biases, yielding genome-wide maps of relative contact probabilities. We validate this ICE (iterative correction and eigenvector decomposition) technique on published data obtained by the high-throughput 3C method Hi-C, and we demonstrate that eigenvector decomposition of the obtained maps provides insights into local chromatin states, global patterns of chromosomal interactions, and the conserved organization of human and mouse chromosomes.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/ef0205dd67334640161a22b416d56760cf666857

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Citations in the corpus

Architectural protein subclasses shape 3D organization of genomes during lineage commitment. (2013, positive): “…as to 5C specifically, we developed a probabilistic model that simultaneously captures the distance-dependent background level of nonspecific chromatin interactions and the nonbiological contribution from each primer (Imakaev et al., 2012; Yaffe and Tanay, 2011) (Extended Experimental Procedures).”
Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data (2013, background): “Compartment signal can be quantified by eigenvector expansion of the interaction map 64,111,112 .”
Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells (2013, methodology): “We normalized the Hi-C interaction frequencies according to the iterative correction method (20).”
Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts (2014, methodology): “More recently, Imakaev et al. (2012) developed an iterative correction method that does not require a priori knowledge and is based on the assumption that all loci should have an equal number of total contacts.”
HiC-Pro: an optimized and flexible pipeline for Hi-C data processing (2015, methodology): “Fragments with a size outside the expected range can be discarded if specified but are usually the result of random breaks or star activity of the enzyme, and can therefore be included in downstream analysis [17].”
Chromatin organization by an interplay of loop extrusion and compartmental segregation (2017, methodology): “To do so, we compute compartment profiles from eigenvector decomposition of the Hi-C maps (2, 3) and assign compartmental segments of type A/B to intervals with positive/negative compartment profile.”
CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data (2015, background): “[8] and is generally consistent with data from molecular dynamics simulations of chromatin fibres [18].”
Complex multi-enhancer contacts captured by Genome Architecture Mapping (GAM) (2017, positive): “Other, more technical limitations of 3C are biases due to GC content, protein occupancy and restriction site density20,23–25, which can lead to discrepancies between 3C-based methods and 3D-fluorescence in situ hybridisation (FISH)18 that complicate data interpretation.”
HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient (2017, positive): “The bias correction was performed using the Iterative Correction and Eigenvector decomposition procedure (ICE) (Imakaev et al. 2012).”
Formation of Chromosomal Domains by Loop Extrusion (2015, methodology): “For these simulations, we converted ChIP-seq data for CTCF and cohesin over a 15Mb region of human chromosome 14 into BE occupancy and directionality (Methods).”
Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization (2017, positive): “8 and the iterative mapping strategy, as described in (Imakaev et al., 2012; Lajoie et al., 2015).”
Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, quality control and visualization (2020, methodology): “Imakaev (31), HiCExplorer also offers the Knight-Ruiz correction (32), first used for Hi-C matrices by (9).”