HiCNorm: removing biases in Hi-C data via Poisson regression

Hu, Ming; Deng, Ke; Selvaraj, Siddarth; Qin, Zhaohui S.; Ren, Bing; Liu, Jun S.

doi:10.1093/bioinformatics/bts570

Cited by 240 publications

(299 citation statements)

References 6 publications

(8 reference statements)

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“…Mapped reads were assigned to restriction fragments with BEDtools 67 , tabulated with custom AWK scripts and imported into R (https://www.r-project.org/). Raw counts of Hi-C links were aggregated in 1 Mb bins and normalized separately for intra-and interchromosomal contacts using HiCNorm 68 . Contact probability matrices were plotted using standard R functions 69 .…”

Section: Methodsmentioning

confidence: 99%

A chromosome conformation capture ordered sequence of the barley genome

Mascher

Gundlach

Himmelbach

et al. 2017

Nature

1,263

1,506

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Barley remains dated to the dawn of agriculture have been found at several archaeological sites 1,2 . In addition to indications that barley was an important food crop, recent excavations have fuelled speculation that beverages from fermented grains may have motivated early Neolithic hunter-gatherers to erect some of humankind's oldest monuments 3,4 . Moreover, brewing beer may also have played a role in the eastward spread of the crop after its initial domestication in the Fertile Crescent 5,6 . Since 2012, both genetic research and crop improvement in barley have benefited from a partly ordered draft sequence assembly 7 . This community resource has underpinned gene isolation 8,9 and population genomic studies 10 . However, these and other efforts have also revealed limitations of the current draft assembly. The limitations are often direct consequences of two characteristic genomic features: the extreme abundance of repetitive elements, and the severely reduced frequency of meiotic recombination in pericentromeric regions 11 .These factors have limited the contiguity of whole-genome assemblies to kilobase-sized sequences originating from low-copy regions of the genome. Thus, a detailed investigation of the composition of the repetitive fraction of the genome-including expanded gene families-and of the distribution of targets of selection and crop improvement in (genetically defined) pericentromeric regions has been beyond reach.Here we present a map-based reference sequence of the barley genome including the first comprehensively ordered assembly of the pericentromeric regions of a Triticeae genome. The resource highlights a conspicuous distinction between distal and proximal regions of chromosomes that is reflected by the intranuclear chromatin organization. Moreover, chromosomal compartments are differentiated by an exponential gradient of gene density and recombination rate, striking contrasts in the distribution of retrotransposon families, and distinct patterns of genetic diversity.Cereal grasses of the Triticeae tribe have been the major food source in temperate regions since the dawn of agriculture. Their large genomes are characterized by a high content of repetitive elements and large pericentromeric regions that are virtually devoid of meiotic recombination. Here we present a high-quality reference genome assembly for barley (Hordeum vulgare L.). We use chromosome conformation capture mapping to derive the linear order of sequences across the pericentromeric space and to investigate the spatial organization of chromatin in the nucleus at megabase resolution. The composition of genes and repetitive elements differs between distal and proximal regions. Gene family analyses reveal lineage-specific duplications of genes involved in the transport of nutrients to developing seeds and the mobilization of carbohydrates in grains. We demonstrate the importance of the barley reference sequence for breeding by inspecting the genomic partitioning of sequence variation in modern elite germplasm, highlightin...

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Section: Methodsmentioning

confidence: 99%

A chromosome conformation capture ordered sequence of the barley genome

Mascher

Gundlach

Himmelbach

et al. 2017

Nature

1,263

1,506

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“…In the development of this new version of the software, particular attention has been paid at optimising the data structures employed for the construction of the graph, in order to facilitate the parallel implementation of the algorithm. This novel implementation refines the normalisation routine, which relies on a modified version of the Hu et al approach [10]: while the original prototype used the Poisson regression model to provide a score to each read, NuChart-II exploits the same regression analysis to assign a confidence score to each edge of the neighbourhood graph, so that the user can evaluate the reliability of each contact. The engineering of the new software has been conducted on top of FastFlow, using the ParallelFor pattern discussed above (see section II-B): FastFlow aims at simplifying the programmers life in developing complex parallel applications, while providing high runtime efficiency.…”

Section: A Nuchart-iimentioning

confidence: 99%

“…This approach can remove the majority of systematic biases, at the expense of very high computational costs, due to the observation of paired-end reads spanning all possible fragment end pairs. Hu et al [10] proposed a parametric model based on a Poisson regression. This is a simplified, and less computationally intensive normalisation procedure than the one described by Yaffe and Tanay, since it corrects the systematic biases in Hi-C contact maps at the desired resolution level, instead of modelling Hi-C data at the fragment end level.…”

Section: Introductionmentioning

confidence: 99%

Parallel Exploration of the Nuclear Chromosome Conformation with NuChart-II

Tordini

Drocco

Misale

et al. 2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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Abstract-High-throughput molecular biology techniques are widely used to identify physical interactions between genetic elements located throughout the human genome. Chromosome Conformation Capture (3C) and other related techniques allow to investigate the spatial organisation of chromosomes in the cell's natural state. Recent results have shown that there is a large correlation between co-localization and co-regulation of genes, but these important information are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we introduce NuChart-II, a tool for Hi-C data analysis that provides a gene-centric view of the chromosomal neighbourhood in a graph-based manner. NuChart-II is an efficient and highly optimized C++ re-implementation of a previous prototype package developed in R. Representing Hi-C data using a graphbased approach overcomes the common view relying on genomic coordinates and permits the use of graph analysis techniques to explore the spatial conformation of a gene neighbourhood.

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“…Mostly during the past five years, several software applications have been developed to analyze and visualize genomic interaction data, with different characteristics and outputs. If we consider Hi-C, available analysis software applications are chromoR [6], HiCdat [7], HiCNorm [8], Hi-Corrector [9], Hi-C Pipeline [10], HiC-Pro [11], HiCUP [12], HiFive [13], HIPPIE [14], HiTC [15], HOMER [16], ICE [17]. However, besides correcting biases and generating contact maps, most of them do not provide the entire pipeline to pre-process the raw data (downloadable files) or to compute topological domain coordinates.…”

Section: Introductionmentioning

confidence: 99%

GITAR: an Open Source Tool for Analysis and Visualization of Hi-C Data

Calandrelli

Guan

et al. 2018

Preprint

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Interactions between chromatin segments play a large role in functional genomic assays and developments in genomic interaction detection methods have shown interacting topological domains within the genome. Among these methods, Hi-C plays a key role. Albeit the presence of several software to process and visualize Hi-C data, a tool to perform a comprehensive analysis including also data pre-processing and topological domains computation was still missing. To address this need we developed GITAR (Genome Interaction Tools and Resources). GITAR is composed of two modules: 1) HiCtool, a Python library to process and visualize Hi-C data, including topologically associating domains (TADs) analysis and 2) Processed data, a large collection of human and mouse datasets processed using HiCtool. HiCtool leads the user step-by-step through a pipeline which goes from the source data to the computation, visualization and storage of intra-chromosomal contact maps and topological domain coordinates. A large collection of standardized processed data allows to compare different datasets in a consistent way and it saves time of work to obtain data for additional analyses. GITAR enables to work with Hi-C data even without any programming or bioinformatic expertise and it is available online at www.genomegitar.org as an open source software.

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HiCNorm: removing biases in Hi-C data via Poisson regression

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 240 publications

References 6 publications

A chromosome conformation capture ordered sequence of the barley genome

A chromosome conformation capture ordered sequence of the barley genome

Parallel Exploration of the Nuclear Chromosome Conformation with NuChart-II

GITAR: an Open Source Tool for Analysis and Visualization of Hi-C Data

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