FisHiCal: an R package for iterative FISH-based calibration of Hi-C data

Shavit, Yoli; Hamey, Fiona; Lió, Píetro

doi:10.1093/bioinformatics/btu491

Cited by 37 publications

(37 citation statements)

References 11 publications

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“…In this study, reads mapped to the SEED database were exported from MEGAN5 into BIOM tables, which were subjected to Analysis of similarity (ANOSIM) in Qiime 26 and Principle Component Analysis (PCA) using STAMP 27 . To map reads to bacterial metabolic genes, a total of 100 each of the butyrate production operons ( bcdAB, but and ato ) and succinate dehydrogenase operon ( sdhABC ) were downloaded from the KEGG database.…”

Section: Methodsmentioning

confidence: 99%

Precision editing of the gut microbiota ameliorates colitis

Zhu

Winter

Byndloss

et al. 2018

Nature

388

288

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Summary Inflammatory diseases of the gastrointestinal tract are frequently associated with changes in gut microbial communities that include an expansion of facultative anaerobic bacteria of the Enterobacteriaceae family (phylum Proteobacteria), a common signature of dysbiosis (1–8). Here we show that a dysbiotic expansion of Enterobacteriaceae during gut inflammation could be prevented by tungstate treatment, which selectively inhibited molybdenum cofactor-dependent microbial respiratory pathways that are only operational during episodes of inflammation. In contrast, tungstate treatment caused no overt changes in the microbiota composition under homeostatic conditions. Importantly, tungstate-mediated microbiota editing reduced the severity of intestinal inflammation in murine models of colitis. We conclude that precision editing of the microbiota composition by tungstate treatment ameliorates the adverse effects of dysbiosis in the setting of gut inflammation.

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Section: Methodsmentioning

confidence: 99%

Precision editing of the gut microbiota ameliorates colitis

Zhu

Winter

Byndloss

et al. 2018

Nature

388

288

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“…(1) Bolzer et al 1 (2) Kalhor et al 14 [2], 14,23,44,45 [3], [46][47][48][49][50] [4], [51][52][53][54][55] [5], 44,56-63 and [6]. 14,23,45,64,65 Refer to the text for detailed explanation of each strategy.…”

Section: Modeling Strategiesmentioning

confidence: 99%

“…Lennard -jones Harmonic ( ) 12 [1] as dots, with no physical consistency, [46][47][48][49][50][51][52]55,59,60,63,64 [2] as spheres, 14,16,23,44,45,54,[56][57][58]61,62,65 [3] as spheres with complex physical properties, for example, a soft external surface and a hard core, 14,23 and [4] as sphere of different sizes (either representing different content in nucleotides, or different levels of compaction). 14,45,57 (b) Schematic representation of the application of the three main physical restraints, the excluded volume (U Excl ), the bond restraint (U Bond ) here represented either as a spring that could be modeled by a Lennard-Jones potential or as a harmonic restraint, the bending (U Bend ) that could in this example be modeled explicitly imposing an angle between three particles or by limiting the distance between two nonconsequtive particles (i, i + 2) using a right-truncated harmonic.…”

Section: Modeling Strategiesmentioning

confidence: 99%

“…Giorgetti et al23 ; Meluzzi and Arya 44 ; Tjong et al45 (3) Hirata et al46 ; Lesne et al47 ; Paulsen et al48 ; Shavit et al49 ; Zhang et al 50 ; (4) Park and Lin 51 ; Park and Lin 52 ; Parker 53 ; Peng et al 54 ; Zou et al 55 (5) Meluzzi and Arya 44 ; Ay et al 56 ; Baù and Marti-Renom 57 ; Duan et al 58 ; Nowotny et al 59 ; Rousseau et al 60 ; Tokuda et al 61 ; Trieu and Cheng 62 ; Varoquaux et al 63 (6) Kalhor et al 14 ; Giorgetti et al 23 ; Tjong et al 45 ; Hu et al 64 ; Wang et al 65 ; | Modeling strategies. Six main different modeling strategies are currently available for modeling genomes and genomic domains.…”

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confidence: 99%

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3D modeling of chromatin structure: is there a way to integrate and reconcile single cell and population experimental data?

Dily

Serra

Martí‐Renom

2017

WIREs Comput Mol Sci

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The genome is organized in a hierarchical fashion within the nucleus in interphase. This nonrandom folding of the chromatin fiber is thought to play important roles in the processing of the genetic information. Therefore, a better knowledge of the mechanisms underlying the threedimensional structure of the genome appears essential to fully understand nuclear processes including transcription and replication. Fluorescent In Situ Hybridization (FISH) and molecular biology methods deriving from the Chromosome Conformation Capture technique are the methods of choice to study genome 3D organization at different levels. Although these single cell and population methods allowed to highlight similar chromatin structures, they also show frequent discrepancies which might be better understood by improving the capacity to generate actual 3D models of organization based on the different types of data available. This review aims at giving an overview of the principles, advantages and limits of microscopy and molecular biology methods of analysis of genome structure and at discussing the different approaches of modeling of chromatin classically used and the improvements that are necessary to reach a better understanding on the links between chromatin structure and its spatial organization.

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“…If Fluorescence in situ hybridization experiments are available, a good normalization solution is represented by FisHiCal [8]. This is an R package that performs an iterative FISHbased Hi-C calibration that exploits the information coming from both these methods.…”

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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FisHiCal: an R package for iterative FISH-based calibration of Hi-C data

Abstract: FisHiCal v1.1 is available from http://cran.r-project.org/.

Cited by 37 publications

References 11 publications

Precision editing of the gut microbiota ameliorates colitis

Precision editing of the gut microbiota ameliorates colitis

3D modeling of chromatin structure: is there a way to integrate and reconcile single cell and population experimental data?

Parallel Exploration of the Nuclear Chromosome Conformation with NuChart-II

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