HiC-ACT: improved detection of chromatin interactions from Hi-C data via aggregated Cauchy test

Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40 kb bin) but is invalid at high resolution (e.g., 5 or 10 kb bins) because spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov random field-based methods accommodate spatial dependency but are computationally intensive. It is urgent to develop approaches that can model spatial dependence in a computationally efficient and scalable manner. Here, we develop HiC-ACT, an aggregated Cauchy test (ACT)-based approach, to improve the detection of chromatin interactions by post-processing results from methods assuming independence. To benchmark the performance of HiC-ACT, we re-analyzed deeply sequenced Hi-C data from a human lymphoblastoid cell line, GM12878, and mouse embryonic stem cells (mESCs). Our results demonstrate advantages of HiC-ACT in improving sensitivity with controlled type I error. By leveraging information from neighboring chromatin interactions, HiC-ACT enhances the power to detect interactions with lower signal-to-noise ratio and similar (if not stronger) epigenetic signatures that suggest regulatory roles. We further demonstrate that HiC-ACT peaks show higher overlap with known enhancers than Fit-Hi-C/FitHiC2 peaks in both GM12878 and mESCs. HiC-ACT, effectively a summary statistics-based approach, is computationally efficient (∼6 min and ∼2 GB memory to process 25,000 pairwise interactions)

FIREcaller: Detecting frequently interacting regions from Hi-C data

Hi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and cross-sample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and super-FIREs identified, in a tissue-specific manner, are closely related to gene regulation, are enriched for enhancer-promoter (E-P) interactions, tend to overlap with regions exhibiting epigenomic signatures of cis-regulatory roles, and aid the interpretation or GWAS variants. The FIREcaller package is implemented in R and freely available at https://yunliweb.its.unc.edu/FIREcaller

Maps: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments

Hi-C and chromatin immunoprecipitation (ChIP) have been combined to identify long-range chromatin interactions genome-wide at reduced cost and enhanced resolution, but extracting information from the resulting datasets has been challenging. Here we describe a computational method, MAPS, Model-based Analysis of PLAC-seq and HiChIP, to process the data from such experiments and identify long-range chromatin interactions. MAPS adopts a zero-truncated Poisson regression framework to explicitly remove systematic biases in the PLAC-seq and HiChIP datasets, and then uses the normalized chromatin contact frequencies to identify significant chromatin interactions anchored at genomic regions bound by the protein of interest. MAPS shows superior performance over existing software tools in the analysis of chromatin interactions from multiple PLAC-seq and HiChIP datasets centered on different transcriptional factors and histone marks

Transcriptional network orchestrating regional patterning of cortical progenitors

We uncovered a transcription factor (TF) network that regulates cortical regional patterning in radial glial stem cells. Screening the expression of hundreds of TFs in the developing mouse cortex identified 38 TFs that are expressed in gradients in the ventricular zone (VZ). We tested whether their cortical expression was altered in mutant mice with known patterning defects (Emx2, Nr2f1, and Pax6), which enabled us to define a cortical regionalization TF network (CRTFN). To identify genomic programming underlying this network, we performed TF ChIP-seq and chromatin-looping conformation to identify enhancer–gene interactions. To map enhancers involved in regional patterning of cortical progenitors, we performed assays for epigenomic marks and DNA accessibility in VZ cells purified from wild-type and patterning mutant mice. This integrated approach has identified a CRTFN and VZ enhancers involved in cortical regional patterning in the mouse

Cell-type-specific 3D epigenomes in the developing human cortex

Lineage-specific epigenomic changes during human corticogenesis have been difficult to study owing to challenges with sample availability and tissue heterogeneity. For example, previous studies using single-cell RNA sequencing identified at least 9 major cell types and up to 26 distinct subtypes in the dorsal cortex alone1,2. Here we characterize cell-type-specific cis-regulatory chromatin interactions, open chromatin peaks, and transcriptomes for radial glia, intermediate progenitor cells, excitatory neurons, and interneurons isolated from mid-gestational samples of the human cortex. We show that chromatin interactions underlie several aspects of gene regulation, with transposable elements and disease-associated variants enriched at distal interacting regions in a cell-type-specific manner. In addition, promoters with increased levels of chromatin interactivity—termed super-interactive promoters—are enriched for lineage-specific genes, suggesting that interactions at these loci contribute to the fine-tuning of transcription. Finally, we develop CRISPRview, a technique that integrates immunostaining, CRISPR interference, RNAscope, and image analysis to validate cell-type-specific cis-regulatory elements in heterogeneous populations of primary cells. Our findings provide insights into cell-type-specific gene expression patterns in the developing human cortex and advance our understanding of gene regulation and lineage specification during this crucial developmental window

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale

Affective touch communication in close adult relationships

Inter-personal touch is a powerful aspect of social interaction that we expect to be particularly important for emotional communication. We studied the capacity of closely acquainted humans to signal the meaning of several word cues (e.g. gratitude, sadness) using touch sensation alone. Participants communicated all cues with above chance performance. We show that emotionally close people can accurately signal the meaning of different words through touch, and that performance is affected by the amount of contextual information available. Even with minimal context and feedback, both attention-getting and love were communicated surprisingly well. Neither the type of close relationship, nor self-reported comfort with touch significantly affected performance

Affective touch communication in close adult relationships

Inter-personal touch is a powerful aspect of social interaction that we expect to be particularly important for emotional communication. We studied the capacity of closely acquainted humans to signal the meaning of several word cues (e.g. gratitude, sadness) using touch sensation alone. Participants communicated all cues with above chance performance. We show that emotionally close people can accurately signal the meaning of different words through touch, and that performance is affected by the amount of contextual information available. Even with minimal context and feedback, both attention-getting and love were communicated surprisingly well. Neither the type of close relationship, nor self-reported comfort with touch significantly affected performance

Affective touch communication in close adult relationships

Inter-personal touch is a powerful aspect of social interaction that we expect to be particularly important for emotional communication. We studied the capacity of closely acquainted humans to signal the meaning of several word cues (e.g. gratitude, sadness) using touch sensation alone. Participants communicated all cues with above chance performance. We show that emotionally close people can accurately signal the meaning of different words through touch, and that performance is affected by the amount of contextual information available. Even with minimal context and feedback, both attention-getting and love were communicated surprisingly well. Neither the type of close relationship, nor self-reported comfort with touch significantly affected performance

The Language of Social Touch Is Intuitive and Quantifiable

Touch is a powerful communication tool, but we have a limited understanding of the role played by particular physical features of interpersonal touch communication. In this study, adults living in Sweden performed a task in which messages (attention, love, happiness, calming, sadness, and gratitude) were conveyed by a sender touching the forearm of a receiver, who interpreted the messages. Two experiments (N = 32, N = 20) showed that within close relationships, receivers could identify the intuitive touch expressions of the senders, and we characterized the physical features of the touches associated with successful communication. Facial expressions measured with electromyography varied by message but were uncorrelated with communication performance. We developed standardized touch expressions and quantified the physical features with 3D hand tracking. In two further experiments (N = 20, N = 16), these standardized expressions were conveyed by trained senders and were readily understood by strangers unacquainted with the senders. Thus, the possibility emerges of a standardized, intuitively understood language of social touch.Funding Agencies|Facebook; Swedish Research Council</p