ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C

Zouari, Yousra Ben; Molitor, Anne; Sikorska, Natalia; Pancaldi, Véra; Sexton, Tom

doi:10.1186/s13059-019-1706-3

Cited by 25 publications

(19 citation statements)

References 62 publications

(88 reference statements)

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“…Here we present Peakachu, a machine-learning framework to predict chromatin loops from genome-wide contact maps. To the best of our knowledge, all current loop detection algorithms are based on searching for statistically enriched interactions against a global or local background, and vary in choices of statistical model and background definitions [21][22][23][24][25][26][27][28][29] . By learning from enrichment-based platforms such as ChIA-PET/HiChIP or Capture Hi-C, Peakachu can detect high-quality loop interactions from genome-wide interaction data such as Hi-C and SPRITE, even at low sequencing depths.…”

Section: Discussionmentioning

confidence: 99%

“…To address these problems, CHiCAGO adopts a convolution background model and alleviates multiple testing via a p-value weighting procedure 28 . Alternatively, ChiCMaxima avoids statistical tests by using strategies from the signal processing field to find local maxima and integrates biological replicate information to reduce false-positive rates 29 . We observe that nearly all available tools are based on testing for significant enrichment compared to a local or global background, with specific calculations being quite empirical and difficult to generalize between techniques.…”

Section: Introductionmentioning

confidence: 99%

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A supervised learning framework for chromatin loop detection in genome-wide contact maps

et al. 2020

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Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of orthogonal data types such as ChIA-PET, HiChIP, Capture Hi-C, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here, we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. We compare Peakachu with current enrichment-based approaches, and find that Peakachu identifies a unique set of short-range interactions. We show that our models perform well in different platforms, across different sequencing depths, and across different species. We apply this framework to predict chromatin loops in 56 Hi-C datasets, and release the results at the 3D Genome Browser.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A supervised learning framework for chromatin loop detection in genome-wide contact maps

et al. 2020

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“…Generally, the analysis and interpretation of sparse 3C-datasets is not trivial and specialised analytical tools are required. In the case of pcHi-C, the available tools (ChiCMaxima, Chicago, Gothic, Chicdiff, HiCapTools [47-51]) are mainly focused on the implementation of normalization strategies to reduce the impact of non-biological biases and on strategies to detect interaction between captured loci. Conversely, the integrative modelling method presented in this study has been designed for the analysis and interpretation of sparse 3C-datasets in their third dimension, allowing for data normalisation, detection of significative interaction, and most importantly, the recovery of the full structural organization of a genomic region despite of the data sparseness.…”

Section: Discussionmentioning

confidence: 99%

3D reconstruction of genomic regions from sparse interaction data

Mendieta-Esteban

Stefano

Castillo

et al. 2020

Preprint

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Chromosome Conformation Capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterise short- and long-range enhancer-promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organisation from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalisation, detection of significant interactions, and reconstruction of the full 3D organisation of the genomic region despite of the data sparseness. Specifically, it produces reliable reconstructions, in line with the ones obtained from dense interaction matrices, with as low as the 2-3% of the data from the matrix. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organisational features such as the formation of different networks of active gene communities.

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“…ChiCMaxima was developed to identify significant interactions by defining them as local maxima after using loess smoothing on bait-specific interactions [107]. Compared to CHiCAGO,…”

Section: Interaction-based Methodsmentioning

confidence: 99%

Seeing the forest through the trees: Identifying functional interactions from Hi-C

Liu

Low

Alinejad‐Rokny

et al. 2020

Preprint

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Eukaryotic genomes are highly organised within the nucleus of a cell, allowing widely dispersed regulatory elements such as enhancers to interact with gene promoters through physical contacts in three-dimensional space. Recent chromosome conformation capture methodologies such as Hi-C have enabled the analysis of interacting regions of the genome providing a valuable insight into the three-dimensional organisation of the chromatin in the nucleus, including chromosome compartmentalisation and gene expression. Complicating the analysis of Hi-C data however is the massive amount of identified interactions, many of which do not directly drive gene function, thus hindering the identification of potentially biologically functional 3D interactions. In this review, we collate and examine the downstream analysis of Hi-C data with particular focus on methods that identify significant functional interactions. We classify three groups of approaches; structurally-associated domain discovery methods e.g. topologically-associated domains and compartments, detection of statistically significant interactions via background models, and the use of epigenomic data integration to identify functional interactions. Careful use of these three approaches is crucial to successfully identifying functional interactions within the genome.

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ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C

Cited by 25 publications

References 62 publications

A supervised learning framework for chromatin loop detection in genome-wide contact maps

A supervised learning framework for chromatin loop detection in genome-wide contact maps

3D reconstruction of genomic regions from sparse interaction data

Seeing the forest through the trees: Identifying functional interactions from Hi-C

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