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3D Genomics
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3D Genomics

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (15 May 2019) | Viewed by 33235

Special Issue Editors

Prof. Dr. Miguel Andrade

E-Mail Website
Guest Editor
Faculty of Biology, Johannes Gutenberg University of Mainz, 55128 Mainz, Germany
Interests: computational biology; systems biology; genomics; evolution; protein and gene function prediction; data mining; text mining; bioinformatics; protein repeats; 3D genome structure
Dr. Enrique M. Muro

E-Mail Website
Guest Editor
iomE-Faculty of Biology, Johannes Gutenberg University of Mainz, 55128 Mainz, Germany
Interests: computational biology; bioinformatics; systems biology; genomics; evolution; data mining; artificial intelligence; non coding RNA; pseudogenes; mobile DNA; repeats; 3D genome structure

Special Issue Information

Dear Colleagues,

DNA was discovered in 1869. In 1953, the chemical structure of the DNA was elucidated through the double helix model; and the human genome project was declared complete in 2003, providing us with a first consensus sequence. Since then, the human genome and many other genomes have been annotated all over. Our knowledge of transcriptional regulatory networks and of the epigenetic mechanisms controlling gene expression is increasing rapidly.

However, although we have obtained a great deal of information about mammalian genomes, we are still far from understanding their regulation. A key missing factor is the 3D structure of the genome. It is known that the three-dimensional chromatin structure has more function than the mere packaging of the genomic material into the nucleus. Unravelling the structure of the genome is a must in the agenda to expand our understanding of the biological functionality orchestrated by variables that exceed a unidimensional genome.

Microscopy was the main technique used to observe the structure of the chromatin, but the recent advances on chromatin conformation techniques (3C) and derived techniques, especially Hi-C, are starting to allow us to solve part of the puzzle led by the recent discovery of topologically associating domains (TADs), which are keystones in gene regulation with causality in human disease.

In this Special Issue, we are interested in publishing short manuscripts of about 3000–5000 words with one or two figures, reviewing one or more of the basic aspects of the 3D genome indicated in the keywords section. Non-technical language for a more divulgative style is also encouraged.

Prof. Miguel Andrade
Dr. Enrique M. Muro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

For this Special Issue we are glad to offer a 15% discount from our APC to all planned contributions.

Keywords

  • 3D chromatin folding
  • structure in relation with gene regulation and diseases
  • structural experimental data: capture, detection algorithms, normalization, visualization, applications
  • structural predictions based on the linear genome
  • CNVs and enhancer adoption
  • chromosome translocation
  • DNA repair

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Published Papers (5 papers)

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Research

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15 pages, 1865 KiB  
Article
HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks
by Tong Liu and Zheng Wang
Genes 2019, 10(11), 862; https://doi.org/10.3390/genes10110862 - 30 Oct 2019
Cited by 16 | Viewed by 3714
Abstract
We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different [...] Read more.
We present a deep-learning package named HiCNN2 to learn the mapping between low-resolution and high-resolution Hi-C (a technique for capturing genome-wide chromatin interactions) data, which can enhance the resolution of Hi-C interaction matrices. The HiCNN2 package includes three methods each with a different deep learning architecture: HiCNN2-1 is based on one single convolutional neural network (ConvNet); HiCNN2-2 consists of an ensemble of two different ConvNets; and HiCNN2-3 is an ensemble of three different ConvNets. Our evaluation results indicate that HiCNN2-enhanced high-resolution Hi-C data achieve smaller mean squared error and higher Pearson’s correlation coefficients with experimental high-resolution Hi-C data compared with existing methods HiCPlus and HiCNN. Moreover, all of the three HiCNN2 methods can recover more significant interactions detected by Fit-Hi-C compared to HiCPlus and HiCNN. Based on our evaluation results, we would recommend using HiCNN2-1 and HiCNN2-3 if recovering more significant interactions from Hi-C data is of interest, and HiCNN2-2 and HiCNN if the goal is to achieve higher reproducibility scores between the enhanced Hi-C matrix and the real high-resolution Hi-C matrix. Full article
(This article belongs to the Special Issue 3D Genomics)
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27 pages, 7758 KiB  
Article
Chromatin Interaction Analysis with Updated ChIA-PET Tool (V3)
by Guoliang Li, Tongkai Sun, Huidan Chang, Liuyang Cai, Ping Hong and Qiangwei Zhou
Genes 2019, 10(7), 554; https://doi.org/10.3390/genes10070554 - 22 Jul 2019
Cited by 24 | Viewed by 8724
Abstract
Understanding chromatin interactions is important because they create chromosome conformation and link the cis- and trans- regulatory elements to their target genes for transcriptional regulation. Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing is a genome-wide high-throughput technology that detects chromatin interactions associated [...] Read more.
Understanding chromatin interactions is important because they create chromosome conformation and link the cis- and trans- regulatory elements to their target genes for transcriptional regulation. Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing is a genome-wide high-throughput technology that detects chromatin interactions associated with a specific protein of interest. We developed ChIA-PET Tool for ChIA-PET data analysis in 2010. Here, we present the updated version of ChIA-PET Tool (V3) as a computational package to process the next-generation sequence data generated from ChIA-PET experiments. It processes short-read and long-read ChIA-PET data with multithreading and generates statistics of results in an HTML file. In this paper, we provide a detailed demonstration of the design of ChIA-PET Tool V3 and how to install it and analyze RNA polymerase II (RNAPII) ChIA-PET data from human K562 cells with it. We compared our tool with existing tools, including ChiaSig, MICC, Mango and ChIA-PET2, by using the same public data set in the same computer. Most peaks detected by the ChIA-PET Tool V3 overlap with those of other tools. There is higher enrichment for significant chromatin interactions from ChIA-PET Tool V3 in aggregate peak analysis (APA) plots. The ChIA-PET Tool V3 is publicly available at GitHub. Full article
(This article belongs to the Special Issue 3D Genomics)
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Review

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15 pages, 644 KiB  
Review
Computational Processing and Quality Control of Hi-C, Capture Hi-C and Capture-C Data
by Peter Hansen, Michael Gargano, Jochen Hecht, Jonas Ibn-Salem, Guy Karlebach, Johannes T. Roehr and Peter N. Robinson
Genes 2019, 10(7), 548; https://doi.org/10.3390/genes10070548 - 18 Jul 2019
Cited by 5 | Viewed by 6061
Abstract
Hi-C, capture Hi-C (CHC) and Capture-C have contributed greatly to our present understanding of the three-dimensional organization of genomes in the context of transcriptional regulation by characterizing the roles of topological associated domains, enhancer promoter loops and other three-dimensional genomic interactions. The analysis [...] Read more.
Hi-C, capture Hi-C (CHC) and Capture-C have contributed greatly to our present understanding of the three-dimensional organization of genomes in the context of transcriptional regulation by characterizing the roles of topological associated domains, enhancer promoter loops and other three-dimensional genomic interactions. The analysis is based on counts of chimeric read pairs that map to interacting regions of the genome. However, the processing and quality control presents a number of unique challenges. We review here the experimental and computational foundations and explain how the characteristics of restriction digests, sonication fragments and read pairs can be exploited to distinguish technical artefacts from valid read pairs originating from true chromatin interactions. Full article
(This article belongs to the Special Issue 3D Genomics)
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15 pages, 1689 KiB  
Review
Super-Resolution Microscopy of Chromatin
by Udo J. Birk
Genes 2019, 10(7), 493; https://doi.org/10.3390/genes10070493 - 28 Jun 2019
Cited by 15 | Viewed by 5172
Abstract
Since the advent of super-resolution microscopy, countless approaches and studies have been published contributing significantly to our understanding of cellular processes. With the aid of chromatin-specific fluorescence labeling techniques, we are gaining increasing insight into gene regulation and chromatin organization. Combined with super-resolution [...] Read more.
Since the advent of super-resolution microscopy, countless approaches and studies have been published contributing significantly to our understanding of cellular processes. With the aid of chromatin-specific fluorescence labeling techniques, we are gaining increasing insight into gene regulation and chromatin organization. Combined with super-resolution imaging and data analysis, these labeling techniques enable direct assessment not only of chromatin interactions but also of the function of specific chromatin conformational states. Full article
(This article belongs to the Special Issue 3D Genomics)
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12 pages, 706 KiB  
Review
A (3D-Nuclear) Space Odyssey: Making Sense of Hi-C Maps
by Irene Mota-Gómez and Darío G. Lupiáñez
Genes 2019, 10(6), 415; https://doi.org/10.3390/genes10060415 - 29 May 2019
Cited by 9 | Viewed by 8350
Abstract
Three-dimensional (3D)-chromatin organization is critical for proper enhancer-promoter communication and, therefore, for a precise execution of the transcriptional programs governing cellular processes. The emergence of Chromosome Conformation Capture (3C) methods, in particular Hi-C, has allowed the investigation of chromatin interactions on a genome-wide [...] Read more.
Three-dimensional (3D)-chromatin organization is critical for proper enhancer-promoter communication and, therefore, for a precise execution of the transcriptional programs governing cellular processes. The emergence of Chromosome Conformation Capture (3C) methods, in particular Hi-C, has allowed the investigation of chromatin interactions on a genome-wide scale, revealing the existence of overlapping molecular mechanisms that we are just starting to decipher. Therefore, disentangling Hi-C signal into these individual components is essential to provide meaningful biological data interpretation. Here, we discuss emerging views on the molecular forces shaping the genome in 3D, with a focus on their respective contributions and interdependence. We discuss Hi-C data at both population and single-cell levels, thus providing criteria to interpret genomic function in the 3D-nuclear space. Full article
(This article belongs to the Special Issue 3D Genomics)
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