A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships

Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators with query gene sets. Identification of transcriptional regulators is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

Key points

An introduction to available computational methods for predicting functional TRs from a query gene set.A detailed walk-through along with practical concerns and limitations.A systematic benchmark of NGS-based methods in terms of accuracy, sensitivity, coverage, and usability, using 570 TR perturbation-derived gene sets.NGS-based methods outperform motif-based methods. Among NGS methods, those utilizing larger databases and adopting region-centric approaches demonstrate favorable performance. BART, ChIP-Atlas, and Lisa are recommended as these methods have overall better performance in evaluated scenarios.

The impact of HTLV-1 expression on the 3D structure and expression of host chromatin

A typical HTLV-1-infected individual carries >104 different HTLV-1-infected T cell clones, each with a single-copy provirus integrated in a unique genomic site. We previously showed that the HTLV-1 provirus causes aberrant transcription in the flanking host genome and, by binding the chromatin architectural protein CTCF, forms abnormal chromatin loops with the host genome. However, it remained unknown whether these effects were exerted simply by the presence of the provirus or were induced by its transcription. To answer this question, we sorted HTLV-1-infected T-cell clones into cells positive or negative for proviral plus-strand expression, and then quantified host and provirus transcription using RNA-seq, and chromatin looping using quantitative chromosome conformation capture (q4C), in each cell population. We found that proviral plus-strand transcription induces aberrant transcription and splicing in the flanking genome but suppresses aberrant chromatin loop formation with the nearby host chromatin. Reducing provirus-induced host transcription with an inhibitor of transcriptional elongation allows recovery of chromatin loops in the plus-strand-expressing population. We conclude that aberrant host transcription induced by proviral expression causes temporary, reversible disruption of chromatin looping in the vicinity of the provirus.

An in silico procedure for generating protein-mediated chromatin interaction data and comparison of significant interaction calling methods

The ability to simulate high-throughput data with high fidelity to real experimental data is fundamental for benchmarking methods used to detect true long-range chromatin interactions mediated by a specific protein. Yet, such tools are not currently available. To fill this gap, we develop an in silico experimental procedure, ChIA-Sim, which imitates the experimental procedures that produce real ChIA-PET, Hi-ChIP, or PLAC-seq data. We show the fidelity of ChIA-Sim to real data by using guiding characteristics of several real datasets to generate data using the simulation procedure. We also used ChIA-Sim data to demonstrate the use of our in silico procedure in benchmarking methods for significant interactions analysis by evaluating four methods for significant interaction calling (SIC). In particular, we assessed each method's performance in terms of correct identification of long-range interactions. We further analyzed four experimental datasets from publicly available databases and shew that the trend of the results are consistent with those seen in data generated from ChIA-Sim. This serves as additional evidence that ChIA-Sim closely resembles data produced from the experimental protocols it models after.

Penaeid Shrimp Chromosome Studies Entering the Post-Genomic Era

Chromosome studies provide the foundation for comprehending inheritance, variation, systematics, and evolution. Penaeid shrimps are a group of crustaceans with great economic importance. Basic cytogenetic information obtained from these shrimps can be used to study their genome structure, chromosome relationships, chromosome variation, polyploidy manipulation, and breeding. The study of shrimp chromosomes experienced significant growth in the 1990s and has been closely linked to the progress of genome research since the application of next-generation sequencing technology. To date, the genome sequences of five penaeid shrimp species have been published. The availability of these genomes has ushered the study of shrimp chromosomes into the post-genomic era. Currently, research on shrimp cytogenetics not only involves chromosome counting and karyotyping, but also extends to investigating submicroscopic changes; exploring genome structure and regulation during various cell divisions; and contributing to the understanding of mechanisms related to growth, sexual control, stress resistance, and genome evolution. In this article, we provide an overview of the progress made in chromosome research on penaeid shrimp. We emphasize the mutual promotion between studies on chromosome structure and genome research and highlight the impact of chromosome-level assembly on studies of genome structure and function. Additionally, we summarize the emerging trends in post-genomic-era shrimp chromosome research.

Advanced image-free analysis of the nano-organization of chromatin and other biomolecules by Single Molecule Localization Microscopy (SMLM)

The cell as a system of many components, governed by the laws of physics and chemistry drives molecular functions having an impact on the spatial organization of these systems and vice versa. Since the relationship between structure and function is an almost universal rule not only in biology, appropriate methods are required to parameterize the relationship between the structure and function of biomolecules and their networks, the mechanisms of the processes in which they are involved, and the mechanisms of regulation of these processes. Single molecule localization microscopy (SMLM), which we focus on here, offers a significant advantage for the quantitative parametrization of molecular organization: it provides matrices of coordinates of fluorescently labeled biomolecules that can be directly subjected to advanced mathematical analytical procedures without the need for laborious and sometimes misleading image processing. Here, we propose mathematical tools for comprehensive quantitative computer data analysis of SMLM point patterns that include Ripley distance frequency analysis, persistent homology analysis, persistent 'imaging', principal component analysis and co-localization analysis. The application of these methods is explained using artificial datasets simulating different, potentially possible and interpretatively important situations. Illustrative analyses of real complex biological SMLM data are presented to emphasize the applicability of the proposed algorithms. This manuscript demonstrated the extraction of features and parameters quantifying the influence of chromatin (re)organization on genome function, offering a novel approach to study chromatin architecture at the nanoscale. However, the ability to adapt the proposed algorithms to analyze essentially any molecular organizations, e.g., membrane receptors or protein trafficking in the cytosol, offers broad flexibility of use.