Coolpup.py: versatile pile-up analysis of Hi-C data.

Recently a biochemistry experiment named methyl-3C was developed to simultaneously capture the chromosomal conformations and DNA methylation levels on individual single cells. However, the number of data sets generated from this experiment is still small in the scientific community compared with the greater amount of single-cell Hi-C data generated from separate single cells. Therefore, a computational tool to predict single-cell methylation levels based on single-cell Hi-C data on the same individual cells is needed. We developed a graph transformer named scHiMe to accurately predict the base-pair-specific (bp-specific) methylation levels based on both single-cell Hi-C data and DNA nucleotide sequences. We benchmarked scHiMe for predicting the bp-specific methylation levels on all of the promoters of the human genome, all of the promoter regions together with the corresponding first exon and intron regions, and random regions on the whole genome. Our evaluation showed a high consistency between the predicted and methyl-3C-detected methylation levels. Moreover, the predicted DNA methylation levels resulted in accurate classifications of cells into different cell types, which indicated that our algorithm successfully captured the cell-to-cell variability in the single-cell Hi-C data. scHiMe is freely available at http://dna.cs.miami.edu/scHiMe/.

Methods for comparative ChIA-PET and Hi-C data analysis.

The human genome is tightly packaged into chromatin whose functional output depends on both one-dimensional (1D) local chromatin states and three-dimensional (3D) genome organization. Currently, chromatin modifications and 3D genome organization are measured by distinct assays. An emerging question is whether it is possible to deduce 3D interactions by integrative analysis of 1D epigenomic data and associate 3D contacts to functionality of the interacting loci. Here we present EpiTensor, an algorithm to identify 3D spatial associations within topologically associating domains (TADs) from 1D maps of histone modifications, chromatin accessibility and RNA-seq. We demonstrate that active promoter–promoter, promoter–enhancer and enhancer–enhancer associations identified by EpiTensor are highly concordant with those detected by Hi-C, ChIA-PET and eQTL analyses at 200 bp resolution. Moreover, EpiTensor has identified a set of interaction hotspots, characterized by higher chromatin and transcriptional activity as well as enriched TF and ncRNA binding across diverse cell types, which may be critical for stabilizing the local 3D interactions.

Chromatin conformation capture methods such as Hi-C have improved understanding of nuclear architecture. However, reconstruction from single-cell Hi-C (scHi-C) data is challenging due to limited DNA contacts per cell. We have previously developed the recurrence plot-based reconstruction (RPR) method for reconstructing three-dimensional (3D) genomic structure from Hi-C data even from low-coverage DNA contact information. Here we used the RPR method to analyze scHi-C data derived from early-stage F1 hybrid embryos as a proof-of-concept for understanding of global chromosomal architecture. We found that paternal and maternal chromosomes become gradually intermingled from the 1-cell to the 64-cell stage, and that discrete chromosome territories are largely established between 8-cell and 64-cell stages. We also observed Rabl-like polarization of chromosomes from the 2- to 8-cell stage, which was mostly dissolved by the 64-cell stage. We also noted transient rod-like extension and parallel chromosome alignment at the 4-cell stage. These findings indicate dynamic chromosomal reorganization before territory formation. RPR and scHi-C together capture 3D chromosomal architecture of individual cells during early embryogenesis.

Single-cell high-throughput chromatin conformation capture technologies (scHi-C) has been used to map chromatin spatial organization in complex tissues. However, computational tools to detect differential chromatin contacts (DCCs) from scHi-C datasets in development and through disease pathogenesis are still lacking. Here, we present SnapHiC-D, a computational pipeline to identify DCCs between two scHi-C datasets. Compared to methods designed for bulk Hi-C data, SnapHiC-D detects DCCs with high sensitivity and accuracy. We used SnapHiC-D to identify cell-type-specific chromatin contacts at 10 Kb resolution in mouse hippocampal and human prefrontal cortical tissues, demonstrating that DCCs detected in the hippocampal and cortical cell types are generally associated with cell-type-specific gene expression patterns and epigenomic features. SnapHiC-D is freely available at https://github.com/HuMingLab/SnapHiC-D.

Most publicly accessible single-cell Hi-C data are sparse and cannot reach a higher resolution. Therefore, learning latent representations (bin-specific embeddings) of sparse single-cell Hi-C matrices would provide us with a novel way of mining valuable information hidden in the limited number of single-cell Hi-C contacts. We present scHiCEmbed, an unsupervised computational method for learning bin-specific embeddings of single-cell Hi-C data, and the computational system is applied to the tasks of 3D structure reconstruction of whole genomes and detection of topologically associating domains (TAD). The only input of scHiCEmbed is a raw or scHiCluster-imputed single-cell Hi-C matrix. The main process of scHiCEmbed is to embed each node/bin in a higher dimensional space using graph auto-encoders. The learned n-by-3 bin-specific embedding/latent matrix is considered the final reconstructed 3D genome structure. For TAD detection, we use constrained hierarchical clustering on the latent matrix to classify bins: S_Dbw is used to determine the optimal number of clusters, and each cluster is considered as one potential TAD. Our reconstructed 3D structures for individual chromatins at different cell stages reveal the expanding process of chromatins during the cell cycle. We observe that the TADs called from single-cell Hi-C data are not shared across individual cells and that the TAD boundaries called from raw or imputed single-cell Hi-C are significantly different from those called from bulk Hi-C, confirming the cell-to-cell variability in terms of TAD definitions. The source code for scHiCEmbed is publicly available, and the URL can be found in the conclusion section.

Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e.,peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the "true" interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

MotivationHigh-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5 kb) by conducting Hi-C experiments needs millions of mammalian cells, which may eventually generate billions of paired-end reads with a high sequencing cost. Therefore, it will be important and helpful if we can enhance the resolutions of Hi-C data by computational methods.ResultsWe developed a new computational method named HiCNN that used a 54-layer very deep convolutional neural network to enhance the resolutions of Hi-C data. The network contains both global and local residual learning with multiple speedup techniques included resulting in fast convergence. We used mean squared errors and Pearson’s correlation coefficients between real high-resolution and computationally predicted high-resolution Hi-C data to evaluate the method. The evaluation results show that HiCNN consistently outperforms HiCPlus, the only existing tool in the literature, when training and testing data are extracted from the same cell type (i.e. GM12878) and from two different cell types in the same or different species (i.e. GM12878 as training with K562 as testing, and GM12878 as training with CH12-LX as testing). We further found that the HiCNN-enhanced high-resolution Hi-C data are more consistent with real experimental high-resolution Hi-C data than HiCPlus-enhanced data in terms of indicating statistically significant interactions. Moreover, HiCNN can efficiently enhance low-resolution Hi-C data, which eventually helps recover two chromatin loops that were confirmed by 3D-FISH.Availability and implementationHiCNN is freely available at http://dna.cs.miami.edu/HiCNN/.Supplementary informationSupplementary data are available at Bioinformatics online.

The long-standing question in radiation and cancer biology is how principles of chromosome organization impact the formation of chromosomal aberrations (CAs). To address this issue, we developed a physical modeling approach and analyzed high-throughput genomic data from chromosome conformation capture (Hi-C) and translocation sequencing (HTGTS) methods. Combining modeling of chromosome structure and of chromosomal aberrations induced by ionizing radiation (IR) and nuclease we made predictions which quantitatively correlated with key experimental findings in mouse chromosomes: chromosome contact maps, high frequency of cis-translocation breakpoints far outside of the site of nuclease-induced DNA double-strand breaks (DSBs), the distinct shape of breakpoint distribution in chromosomes with different 3D organizations. These correlations support the heteropolymer globule principle of chromosome organization in G1-arrested pro-B mouse cells. The joint analysis of Hi-C, HTGTS and physical modeling data offers mechanistic insight into how chromosome structure heterogeneity, globular folding and lesion dynamics drive IR-recurrent CAs. The results provide the biophysical and computational basis for the analysis of chromosome aberration landscape under IR and nuclease-induced DSBs.

We describe an approach for reconstructing three-dimensional (3D) structures from single-cell Hi-C data. This approach has been inspired by a method of recurrence plots and visualization tools for nonlinear time series data. Some examples are also presented.

Open source software (OSS) is computer software that has its underlying source code made available under a licence. This can allow developers and users to adapt and improve it (Raymond, 2001). Computer software can be broadly split into two development models: • Proprietary, or closed software, owned by a company or individual. Copies of the binary are made public; the source code is not usually made public. • Open-source software (OSS), where the source code is released with the binary. Users and developers can be licenced to use and modify the code, and to distribute any improvements they make. Both OSS and proprietary approaches allow companies to make a profit. Companies developing proprietary software make money by developing software and then selling licences to use the software. For example, Microsoft receives a payment for every copy of Windows sold with a personal computer. OSS companies make their money by providing services, such as advising clients on the GPL licence. The licencee can either charge a fee for this service or work free of charge. In practice, software companies often develop both types of software. OSS is developed by an ongoing, iterative process where people share the ideas expressed in the source code. The aim is that a large community of developers and users can contribute to the development of the code, check it for errors and bugs, and make the improved version available to others. Project management software is used to allow developers to keep track of the various versions. There are two main types of open-source licences (although there are many variants and subtypes developed by other companies): • Berkeley Software Distribution (BSD) Licence: This permits a licencee to “close” a version (by withholding the most recent modifications to the source code) and sell it as a proprietary product; • GNU General Public Licence (GNU, GPL, or GPL): Under this licence, licencees may not “close” versions. The licencee may modify, copy, and redistribute any derivative version, under the same GPL licence. The licencee can either charge a fee for this service or work free of charge. Free software first evolved during the 1970s but in the 1990s forked into two movements, namely free software and open source (Berry, 2004). Richard Stallman, an American software developer who believes that sharing source code and ideas is fundamental to freedom of speech, developed a free version of the widely used Unix operating system. The resulting GNU program was released under a specially created General Public Licence (GNU, GPL). This was designed to ensure that the source code would remain openly available to all. It was not intended to prevent commercial usage or distribution (Stallman, 2002). This approach was christened free software. In this context, free meant that anyone could modify the software. However, the term “free” was often misunderstood to mean no cost. Hence, during the 1990s, Eric Raymond and others proposed that open-source software was coined as a less contentious and more business-friendly term. This has become widely accepted within the software and business communities; however there are still arguments about the most appropriate term to use (Moody, 2002). The OSMs are usually organised into a network of individuals who work collaboratively on the Internet, developing major software projects that sometimes rival commercial software but are always committed to the production of quality alternatives to those produced by commercial companies (Raymond, 2001; Williams, 2002). Groups and individuals develop software to meet their own and others’ needs in a highly decentralised way, likened to a Bazaar (Raymond, 2001). These groups often make substantive value claims to support their projects and foster an ethic of community, collaboration, deliberation, and intellectual freedom. In addition, it is argued by Lessig (1999) that the FLOSS community can offer an inspiration in their commitment to transparency in their products and their ability to open up governmental regulation and control through free/libre and open source code.

Against the “Networked Information Economy”: Rethinking Decentralization, Community, and Free Software Development Ben Roberts (bio) The free, libre, and open source software (FLOSS) movement is often cited as an example of fundamental changes in production associated with the rise of information networks. Don Tapscott and Anthony D. Williams see it as an example of wikinomics, a powerful new form of economic production based on sharing and self-organization.1 Yochai Benkler argues that the form of social or peer production evident in FLOSS can compete with or even displace traditional forms of capitalist organization and sees it as a symptom of the “networked information economy,” replacing the “industrial information economy,” which has been in force since the late-nineteenth century.2 The main feature of this networked information economy is the much greater role within it for decentralized individual action.3 This essay contests this broadly liberal take on the significance of the free software movement. It argues that Benkler is typical of an approach that tends to view the Internet as the primary enabler of decentralization (for example, in the form of decentralized software development). Of course, it would be foolish to discount the role that networks have played in the success of FLOSS. But, as I will argue, an examination of its history suggests that its origins can’t be entirely accounted for in purely technological and economic grounds. The proponents of such an approach often resist the claim that their focus on networks is technologically determinist,4 arguing that the network itself is shaped by social and cultural factors. However, this makes the explanatory power of networks rather circular: the existence of networks explains social and cultural behavior at the same time as being explained by it. The explanation of free software in terms of the economic properties of networks is simply too static and asocial; it serves to explain how a particular mode of production can functionally compete with the market without really being able to account for how this change has come about. In particular, as I will show, the marginalization [End Page 385] of the question of labor in theories of peer production disconnects the analysis from wider changes in the nature of economic production. Moreover, the emphasis on the empowerment of individuals ignores the possibility that decentralization consists in radical new forms of sociality and collectivity, something that can perhaps be seen in the communities built around particular free software projects. As I will argue, Gilbert Simondon’s concept of individuation may help inform a better understanding of the emergence of such free software communities. From the perspective of a nonprogrammer, it is easiest to understand the concept of FLOSS by explaining how proprietary software such as Microsoft Windows is distributed. The operating system and applications that users run on their personal computers (PCs) are most often compiled software. The creators of the application have written the code in any one of a number of different human-readable programming languages; this program is known as the source code. The source code is then converted by another program known as a compiler into object or binary code, which is a machine-readable version of the program executed when the application is run on a PC. Binary, or object, code is the form in which software is most often distributed to users, either on installation media such as compact discs (CDs), installed on a computing device before sale or, increasingly, downloaded over the Net. The end user of proprietary software created and distributed in this fashion has no access to the original source code and is therefore unable to alter, change, or fix the software running on their machine: in the words of Bob Young, the former chief executive officer (CEO) of open source software company Red Hat, purchasing proprietary software is like buying a “car with the hood welded shut.”5 In addition to the absence of source code, proprietary software is usually distributed under the terms of a highly restrictive software license. In effect, the applications run by a user remain the property of the software maker and may be used only under the terms of this contract. Typically, the license prevents sharing...

Open-source software abbreviated as OSS is computer software that is available with source code and is provided under a software license that permits users to study, change, and improve the software. For the commercial software the source code and certain other rights are normally reserved for copyright holders,i.e. the company who developes the software. A group of people in a collaborative manner often developes the Open source software, not under the roof of a large organization. This strategy makes open source software cheap, reliable and modifiable if needed. In this context we shall discuss mainly the features of Open Source Software, differences of open source and free software and open source software movement in Indian perspective.

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.

The 3D organization of the genome facilitates gene regulation, replication, and repair, making it a key feature of genomic function and one that remains to be properly understood. Over the past two decades, a variety of chromosome conformation capture (3C) methods have delineated genome folding from megabase-scale compartments and topologically associating domains (TADs) down to kilobase-scale enhancer-promoter interactions. Understanding thefunctional role of each layer of genome organization is a gateway to understanding cell state, development, and disease. Here, we discuss the evolution of 3C-based technologies for mapping 3D genome organization. We focus on genomics methods and provide a historical account of the development from 3C to Hi-C. We also discuss ChIP-based techniques that focus on 3D genome organization mediated by specific proteins, capture-based methods that focus on particular regions or regulatory elements, 3C-orthogonal methods that do not rely on restriction digestion and proximity ligation, and methods for mapping the DNA–RNA and RNA–RNA interactomes. We consider the biological discoveries that have come from these methods, examine the mechanistic contributions of CTCF, cohesin, and loop extrusion to genomic folding, and detail the 3D genome field’s current understanding of nuclear architecture. Finally, we give special consideration to Micro-C as an emerging frontier in chromosome conformation capture and discuss recent Micro-C findings uncovering fine-scale chromatin organization in unprecedented detail.This article is categorized under:Gene Expression and Transcriptional Hierarchies > Regulatory Mechanisms Gene Expression and Transcriptional Hierarchies > Gene Networks and Genomics