normGAM: an R package to remove systematic biases in genome architecture mapping data

Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data

Chromosome conformation capture technologies that provide frequency information for contacts between genomic regions have been crucial for increasing our understanding of genome folding and regulation. However, such data do not provide direct evidence of the spatial 3D organization of chromatin. In this opinion article, we discuss the development and application of computational methods to reconstruct chromatin 3D structures from experimental 2D contact data, highlighting how such modeling provides biological insights and can suggest mechanisms anchored to experimental data. By applying different reconstruction methods to the same contact data, we illustrate some state-of-the-art of these techniques and discuss our gene resolution approach based on Brownian dynamics and Monte Carlo sampling.

Multiplex-GAM: genome-wide identification of chromatin contacts yields insights overlooked by Hi-C

Technology for measuring 3D genome topology is increasingly important for studying gene regulation, for genome assembly and for mapping of genome rearrangements. Hi-C and other ligation-based methods have become routine but have specific biases. Here, we develop multiplex-GAM, a faster and more affordable version of genome architecture mapping (GAM), a ligation-free technique that maps chromatin contacts genome-wide. We perform a detailed comparison of multiplex-GAM and Hi-C using mouse embryonic stem cells. When examining the strongest contacts detected by either method, we find that only one-third of these are shared. The strongest contacts specifically found in GAM often involve 'active' regions, including many transcribed genes and super-enhancers, whereas in Hi-C they more often contain 'inactive' regions. Our work shows that active genomic regions are involved in extensive complex contacts that are currently underestimated in ligation-based approaches, and highlights the need for orthogonal advances in genome-wide contact mapping technologies.

Dissecting the cosegregation probability from genome architecture mapping

Genome architecture mapping (GAM) is a recently developed methodology that offers the cosegregation probability of two genomic segments from an ensemble of thinly sliced nuclear profiles, enabling us to probe and decipher three-dimensional chromatin organization. The cosegregation probability from GAM binned at 1 Mb, which thus probes the length scale associated with the genomic separation greater than 1 Mb, is, however, not identical to the contact probability obtained from Hi-C, and its correlation with interlocus distance measured with fluorescence in situ hybridization is not so good as the contact probability. In this study, by using a polymer-based model of chromatins, we derive a theoretical expression of the cosegregation probability as well as that of the contact probability and carry out quantitative analyses of how they differ from each other. The results from our study, validated with in silico GAM analysis on three-dimensional genome structures from fluorescence in situ hybridization, suggest that to attain strong correlation with the interlocus distance, a properly normalized version of cosegregation probability needs to be calculated based on a large number of nuclear slices (n>103).

Normalization of Chromosome Contact Maps: Matrix Balancing and Visualization

Over the last decade, genomic proximity ligation approaches have reshaped our vision of chromosomes 3D organizations, from bacteria nucleoids to larger eukaryotic genomes. The different protocols (3Cseq, Hi-C, TCC, MicroC [XL], Hi-CO, etc.) rely on common steps (chemical fixation digestion, ligation…) to detect pairs of genomic positions in close proximity. The most common way to represent these data is a matrix, or contact map, which allows visualizing the different chromatin structures (compartments, loops, etc.) that can be associated to other signals such as transcription, protein occupancy, etc. as well as, in some instances, to biological functions.In this chapter we present and discuss the filtering of the events recovered in proximity ligation experiments as well as the application of the balancing normalization procedure on the resulting contact map. We also describe a computational tool for visualizing normalized contact data dubbed Scalogram.The different processes described here are illustrated and supported by the laboratory custom-made scripts pooled into "hicstuff," an open-access python package accessible on github ( https://github.com/koszullab/hicstuff ).

Genome-Wide Investigation of the NAC Transcription Factor Family in Miscanthus sinensis and Expression Analysis Under Various Abiotic Stress

The NAC transcription factor family is deemed to be a large plant-specific gene family that plays important roles in plant development and stress response. Miscanthus sinensis is commonly planted in vast marginal land as forage, ornamental grass, or bioenergy crop which demand a relatively high resistance to abiotic stresses. The recent release of a draft chromosome-scale assembly genome of M. sinensis provided a basic platform for the genome-wide investigation of NAC proteins. In this study, a total of 261 M. sinensis NAC genes were identified and a complete overview of the gene family was presented, including gene structure, conserved motif compositions, chromosomal distribution, and gene duplications. Results showed that gene length, molecular weights (MW), and theoretical isoelectric points (pI) of NAC family were varied, while gene structure and motifs were relatively conserved. Chromosomal mapping analysis found that the M. sinensis NAC genes were unevenly distributed on 19 M. sinensis chromosomes, and the interchromosomal evolutionary analysis showed that nine pairs of tandem duplicates genes and 121 segmental duplications were identified, suggesting that gene duplication, especially segmental duplication, is possibly associated with the amplification of M. sinensis NAC gene family. The expression patterns of 14 genes from M. sinensis SNAC subgroup were analyzed under high salinity, PEG, and heavy metals, and multiple NAC genes could be induced by the treatment. These results will provide a very useful reference for follow-up study of the functional characteristics of NAC genes in the mechanism of stress-responsive and potential roles in the development of M. sinensis.

The 3D Genome: From Structure to Function

The genome is the most functional part of a cell, and genomic contents are organized in a compact three-dimensional (3D) structure. The genome contains millions of nucleotide bases organized in its proper frame. Rapid development in genome sequencing and advanced microscopy techniques have enabled us to understand the 3D spatial organization of the genome. Chromosome capture methods using a ligation approach and the visualization tool of a 3D genome browser have facilitated detailed exploration of the genome. Topologically associated domains (TADs), lamin-associated domains, CCCTC-binding factor domains, cohesin, and chromatin structures are the prominent identified components that encode the 3D structure of the genome. Although TADs are the major contributors to 3D genome organization, they are absent in Arabidopsis. However, a few research groups have reported the presence of TAD-like structures in the plant kingdom.

HCMB: A stable and efficient algorithm for processing the normalization of highly sparse Hi-C contact data

The high-throughput genome-wide chromosome conformation capture (Hi-C) method has recently become an important tool to study chromosomal interactions where one can extract meaningful biological information including P(s) curve, topologically associated domains, A/B compartments, and other biologically relevant signals. Normalization is a critical pre-processing step of downstream analyses for the elimination of systematic and technical biases from chromatin contact matrices due to different mappability, GC content, and restriction fragment lengths. Especially, the problem of high sparsity puts forward a huge challenge on the correction, indicating the urgent need for a stable and efficient method for Hi-C data normalization. Recently, some matrix balancing methods have been developed to normalize Hi-C data, such as the Knight-Ruiz (KR) algorithm, but it failed to normalize contact matrices with high sparsity. Here, we presented an algorithm, Hi-C Matrix Balancing (HCMB), based on an iterative solution of equations, combining with linear search and projection strategy to normalize the Hi-C original interaction data. Both the simulated and experimental data demonstrated that HCMB is robust and efficient in normalizing Hi-C data and preserving the biologically relevant Hi-C features even facing very high sparsity. HCMB is implemented in Python and is freely accessible to non-commercial users at GitHub: https://github.com/HUST-DataMan/HCMB.

The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): conference summary and innovations in genomics

The goal of this editorial is to summarize the 2019 International Conference on Intelligent Biology and Medicine (ICIBM 2019) conference that took place on June 9–11, 2019 in The Ohio State University, Columbus, OH, and to provide an introductory summary of the seven articles presented in this supplement issue. ICIBM 2019 hosted four keynote speakers, four eminent scholar speakers, five tutorials and workshops, twelve concurrent sessions and a poster session, totaling 23 posters, spanning state-of-the-art developments in bioinformatics, genomics, next-generation sequencing (NGS) analysis, scientific databases, cancer and medical genomics, and computational drug discovery. A total of 105 original manuscripts were submitted to ICIBM 2019, and after careful review, seven were selected for this supplement issue. These articles cover methods and applications for functional annotations of miRNA targeting, clonal evolution of bacterial cells, gene co-expression networks that describe a given phenotype, functional binding site analysis of RNA-binding proteins, normalization of genome architecture mapping data, sample predictions based on multiple NGS data types, and prediction of an individual’s genetic admixture given exonic single nucleotide polymorphisms data.