Revisiting Assessment of Computational Methods for Hi-C Data Analysis

The performances of algorithms for Hi-C data preprocessing, the identification of topologically associating domains, and the detection of chromatin interactions and promoter-enhancer interactions have been mostly evaluated using semi-quantitative or synthetic data approaches, without utilizing the most recent methods, since 2017. In this study, we comprehensively evaluated 24 popular state-of-the-art methods for the complete end-to-end pipeline of Hi-C data analysis, using manually curated or experimentally validated benchmark datasets, including a CRISPR dataset for promoter-enhancer interaction validation. Our results indicate that, although no single method exhibited superior performance in all situations, HiC-Pro, DomainCaller, and Fit-Hi-C2 showed relatively balanced performances of most evaluation metrics for preprocessing, topologically associating domain identification, and chromatin interaction/promoter-enhancer interaction detection, respectively. The comprehensive comparison presented in this manuscript provides a reference for researchers to choose Hi-C analysis tools that best suit their needs.

In the loop: promoter-enhancer interactions and bioinformatics

Enhancer-promoter regulation is a fundamental mechanism underlying differential transcriptional regulation. Spatial chromatin organization brings remote enhancers in contact with target promoters in cis to regulate gene expression. There is considerable evidence for promoter-enhancer interactions (PEIs). In the recent years, genome-wide analyses have identified signatures and mapped novel enhancers; however, being able to precisely identify their target gene(s) requires massive biological and bioinformatics efforts. In this review, we give a short overview of the chromatin landscape and transcriptional regulation. We discuss some key concepts and problems related to chromatin interaction detection technologies, and emerging knowledge from genome-wide chromatin interaction data sets. Then, we critically review different types of bioinformatics analysis methods and tools related to representation and visualization of PEI data, raw data processing and PEI prediction. Lastly, we provide specific examples of how PEIs have been used to elucidate a functional role of non-coding single-nucleotide polymorphisms. The topic is at the forefront of epigenetic research, and by highlighting some future bioinformatics challenges in the field, this review provides a comprehensive background for future PEI studies.

Different core promoters possess distinct regulatory activities in the Drosophila embryo

There are numerous examples of shared enhancers interacting with just a subset of target promoters. In some cases, specific enhancer-promoter interactions depend on promoter competition, whereby the activation of a preferred target promoter precludes expression of linked genes. Here, we employ a transgenic embryo assay to obtain evidence that promoter selection is influenced by the TATA element. Both the AE1 enhancer from the Drosophila Antennapedia gene complex (ANT-C) and the IAB5 enhancer from the Bithorax complex (BX-C) preferentially activate TATA-containing promoters when challenged with linked TATA-less promoters. In contrast, the rho neuroectoderm enhancer (NEE) does not discriminate between these two classes of promoters. Thus, certain upstream activators, such as Ftz, prefer TATA-containing promoters, whereas other activators, including Dorsal, work equally well on both classes of promoters. These results provide in vivo evidence that different core promoters possess distinct regulatory activities. We discuss the possibility that an invariant TFIID complex can adopt different conformations on the core promoter.