Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data

Greenwald, William W.; He, Li; Smith, Erin N.; Benaglio, Paola; Nariai, Naoki; Frazer, Kelly A.

doi:10.1186/s12859-017-1621-0

Cited by 44 publications

(33 citation statements)

References 12 publications

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“…It can be extended with additional columns and easily viewed in any text editor on any platform. A simplified version of this format, PGL, was recently published [56]. Another specialized text format, .hic, was designed to store matrices at different resolutions, with an index allowing quick access to any region [39].…”

Section: Discussionmentioning

confidence: 99%

HiCcompare: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection

Stansfield

Dozmorov

2017

Preprint

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Changes in spatial chromatin interactions are now emerging as a unifying mechanism orchestrating regulation of gene expression. Evolution of chromatin conformation capture methods into Hi-C sequencing technology now allows an insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence-and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions. We developed a simple and effective method HiCcompare for the joint normalization and differential analysis of multiple Hi-C datasets. The method avoids constraining Hi-C data within a rigid statistical model, allowing a data-driven normalization of biases using locally weighted linear regression (loess). The method identifies region-specific chromatin interaction changes complementary to changes due to large-scale genomic rearrangements, such as copy number variants (CNVs). HiCcompare outperforms methods for normalizing individual Hi-C datasets in detecting a priori known chromatin interaction differences in simulated and real-life settings while detecting biologically relevant changes. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/.

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Section: Discussionmentioning

confidence: 99%

HiCcompare: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection

Stansfield

Dozmorov

2017

Preprint

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“…The Hi-C data was then pooled across all three samples to create a single contact map, and loops were called with HICCUPs at the same resolutions with the same parameters. A single loop set was then created by identifying loops where both anchors were within 20kb of one another via pgltools 29 and retaining the loop with the highest resolution. If multiple loops were found at the highest resolution, loops were kept from the contact map with the highest overall sequencing depth.…”

Section: Methodsmentioning

confidence: 99%

“…To determine the normalized tag counts of ATAC-seq data at loop anchors, loop anchors were converted to a regular BED file with pgltools 29 , and HOMER 31 was used to find the normalized tag density across all loop anchors for each ATAC-seq sample. Output from HOMER was normalized to a maximum height of 1 for each sample to determine the distribution of ATAC-seq signal within each sample, rather than the relative magnitude coverage difference between ATAC-seq samples.…”

Section: Methodsmentioning

confidence: 99%

Pancreatic islet chromatin accessibility and conformation defines distal enhancer networks of type 2 diabetes risk

Greenwald

Chiou

Yan³

et al. 2018

Preprint

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36The gene targets of enhancer activity in pancreatic islets are largely unknown, impeding 37 discovery of islet regulatory networks involved in type 2 diabetes (T2D) risk. We 38 mapped chromatin state, accessibility and conformation using ChIP-seq, ATAC-seq and 39 Hi-C in human pancreatic islets, which we integrated with T2D genetic fine-mapping and 40 islet expression QTL data. Active islet regulatory elements preferentially interacted with 41 other active elements, often at distances over 1MB, and we identified target genes for 42 thousands of distal islet enhancers. A third of T2D risk signals mapped in islet 43 enhancers, and target genes regulated by these signals were specifically involved in 44 processes related to protein transport and secretion. Among implicated target genes of 45 T2D islet enhancer signals with no prior known role in islet function, we demonstrated 46 that reduced IGF2BP2 activity in mouse islets leads to impaired glucose-stimulated 47 insulin secretion. These results link distal islet enhancer regulation of protein secretion 48 and transport to genetic risk of T2D, and highlight the utility of high-throughput chromatin 49 conformation maps to uncover the gene regulatory networks of complex disease. 50 51 Introduction 52 53 Genetic risk of type 2 diabetes (T2D) is largely mediated through variants affecting 54 transcriptional regulatory activity in pancreatic islets 1-7 . Genetic fine-mapping combined 55 with epigenomic annotation data can identify causal variants at T2D risk loci mapping in 56 islet regulatory elements 1,2 . The gene targets of islet regulatory elements, however, are 57 largely unknown, impeding discovery of disease-relevant gene networks perturbed by 58 risk variants and novel therapeutic avenues. The spatial organization of chromatin plays 59 a critical role in tissue-specific gene regulation, and recently developed high-throughput 60 techniques such as Hi-C identify physical relationships between genomic regions in 61 human tissues genome-wide 8-10 . Tissue-specific maps of chromatin organization can 62 identify candidate target genes of distal regulatory elements and inform the molecular 63 mechanisms of disease risk variants 9 . 65Here, we defined the spatial organization of transcriptional regulatory elements in 66 primary pancreatic islets, through which we mapped genetic effects on islet gene 67 expression and T2D risk. Islet active regulatory elements preferentially interacted with 68 other active elements, in many cases over 1MB, and we identified putative distal target 69 genes for thousands of islet enhancers. A third of known T2D risk signals had likely 70 causal variants in islet enhancers, and target genes of these signals were strongly 71 enriched for processes related to protein secretion and transport. Among target genes 72 with no previously known role in islet function, we demonstrated that reduced activity of 73 IGF2BP2 in mouse islets leads to impaired glucose-stimulated insulin secretion. 74Together our results define distal regulatory programs ...

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“…Out of all interactions, we chose high confidence interactions with P-value less than 1E-9. We used PGLtool (27) to find overlap between high confidence HiC based chromatin interaction and predicted interacting peakpairs using co-accessibility.…”

Section: Estimating Co-accessibility Among Sites and Evaluating The Pmentioning

confidence: 99%

FITs: Forest of imputation trees for recovering true signals in single-cell open chromatin profiles

Sharma

Pandey

Mongia

et al. 2020

Preprint

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The advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA cause high drop-out rate and noise in single-cell open-chromatin profiles. We introduce here a robust method called as Forest of Imputation Trees (FITs) to recover original signals from highly sparse and noisy single-cell open-chromatin profiles. FITs makes a forest of imputation trees to avoid bias during the restoration of read-count matrices. It resolves the challenging issue of recovering open chromatin signals without blurring out information at genomic sites with cell-type-specific activity. FITs is generalized for wider applicability, especially for highly sparse read-count matrices. The superiority of FITs in recovering signals of minority cells also makes it highly useful for single-cell open-chromatin profile from in vivo samples.First made online as thesis work at https://repository.iiitd.edu.in/xmlui/handle/123456789/807

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Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data

Cited by 44 publications

References 12 publications

HiCcompare: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection

HiCcompare: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection

Pancreatic islet chromatin accessibility and conformation defines distal enhancer networks of type 2 diabetes risk

FITs: Forest of imputation trees for recovering true signals in single-cell open chromatin profiles

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