Search

High-altitude adaptation of Tibetans represents a remarkable case of natural selection during recent human evolution. Previous genome-wide scans found many non-coding variants under selection, suggesting a pressing need to understand the functional role of non-coding regulatory elements (REs). Here, we generate time courses of paired ATAC-seq and RNA-seq data on cultured HUVECs under hypoxic and normoxic conditions. We further develop a variant interpretation methodology (vPECA) to identify active selected REs (ASREs) and associated regulatory network. We discover three causal SNPs of EPAS1, the key adaptive gene for Tibetans. These SNPs decrease the accessibility of ASREs with weakened binding strength of relevant TFs, and cooperatively down-regulate EPAS1 expression. We further construct the downstream network of EPAS1, elucidating its roles in hypoxic response and angiogenesis. Collectively, we provide a systematic approach to interpret phenotype-associated noncoding variants in proper cell types and relevant dynamic conditions, to model their impact on gene regulation., Tibetan adaptation to the high-altitude environment represents a case of natural selection during recent human evolution. Here the authors investigated the chromatin and transcriptional landscape of umbilical endothelial cells from Tibetan and Han Chinese donors and provide genome-wide characterization of the hypoxia regulatory network associated high-altitude adaptation.

Human genetic variants can influence the severity of symptoms infected with SARS-COV-2. Several genome-wide association studies have identified human genomic risk single nucleotide polymorphisms (SNPs) associated with coronavirus disease 2019 (COVID-19) severity. However, the causal tissues or cell types underlying COVID-19 severity are uncertain. In addition, candidate genes associated with these risk SNPs were investigated based on genomic proximity instead of their functional cellular contexts. Here, we compiled regulatory networks of 77 human contexts and revealed those risk SNPs' enriched cellular contexts and associated risk SNPs with transcription factors, regulatory elements, and target genes. Twenty-one human contexts were identified and grouped into two categories: immune cells and epithelium cells. We further aggregated the regulatory networks of immune cells and epithelium cells. These two aggregated regulatory networks were investigated to reveal their association with risk SNPs' regulation. Two genomic clusters, the chemokine receptors cluster and the oligoadenylate synthetase (OAS) cluster, showed the strongest association with COVID-19 severity, and they had different regulatory programs in immune and epithelium contexts. Our findings were supported by analysis of both SNP array and whole genome sequencing-based genome wide association study (GWAS) summary statistics.The online version contains supplementary material available at 10.1007/s43657-022-00066-x.

Motivation Regulatory elements (REs), such as enhancers and promoters, are known as regulatory sequences functional in a heterogeneous regulatory network to control gene expression by recruiting transcription regulators and carrying genetic variants in a context specific way. Annotating those REs relies on costly and labor-intensive next-generation sequencing and RNA-guided editing technologies in many cellular contexts. Results We propose a systematic Gene Ontology Annotation method for Regulatory Elements (RE-GOA) by leveraging the powerful word embedding in natural language processing. We first assemble a heterogeneous network by integrating context specific regulations, protein–protein interactions and gene ontology (GO) terms. Then we perform network embedding and associate regulatory elements with GO terms by assessing their similarity in a low dimensional vector space. With three applications, we show that RE-GOA outperforms existing methods in annotating TFs’ binding sites from ChIP-seq data, in functional enrichment analysis of differentially accessible peaks from ATAC-seq data, and in revealing genetic correlation among phenotypes from their GWAS summary statistics data. Availability and implementation The source code and the systematic RE annotation for human and mouse are available at https://github.com/AMSSwanglab/RE-GOA. Supplementary information Supplementary data are available at Bioinformatics online.

Cranial Neural Crest Cells (CNCC) originate at the cephalic region from forebrain, midbrain and hindbrain, migrate into the developing craniofacial region, and subsequently differentiate into multiple cell types. The entire specification, delamination, migration, and differentiation process is highly regulated and abnormalities during this craniofacial development cause birth defects. To better understand the molecular networks underlying CNCC, we integrate paired gene expression & chromatin accessibility data and reconstruct the genome-wide human Regulatory network of CNCC (hReg-CNCC). Consensus optimization predicts high-quality regulations and reveals the architecture of upstream, core, and downstream transcription factors that are associated with functions of neural plate border, specification, and migration. hReg-CNCC allows us to annotate genetic variants of human facial GWAS and disease traits with associated cis-regulatory modules, transcription factors, and target genes. For example, we reveal the distal and combinatorial regulation of multiple SNPs to core TF ALX1 and associations to facial distances and cranial rare disease. In addition, hReg-CNCC connects the DNA sequence differences in evolution, such as ultra-conserved elements and human accelerated regions, with gene expression and phenotype. hReg-CNCC provides a valuable resource to interpret genetic variants as early as gastrulation during embryonic development. The network resources are available at https://github.com/AMSSwanglab/hReg-CNCC., Zhanying Feng et al. present hReg-CNCC, a high-quality gene regulatory network for human cranial neural crest cells (CNCCs) constructed by consensus optimization modeling. It may be useful in interpreting genetic variants involved in embryonic development by linking the cis-regulatory sequences in this network with GWAS SNPs, disease risk loci, and evolutionarily-conserved regions of the genome.

Epigenetic information regulates gene expression and development. However, our understanding of the evolution of epigenetic regulation on brain development in primates is limited. Here, we compared chromatin accessibility landscapes and transcriptomes during fetal prefrontal cortex (PFC) development between rhesus macaques and humans. A total of 304,761 divergent DNase I-hypersensitive sites (DHSs) are identified between rhesus macaques and humans, although many of these sites share conserved DNA sequences. Interestingly, most of the cis-elements linked to orthologous genes with dynamic expression are divergent DHSs. Orthologous genes expressed at earlier stages tend to have conserved cis-elements, whereas orthologous genes specifically expressed at later stages seldom have conserved cis-elements. These genes are enriched in synapse organization, learning and memory. Notably, DHSs in the PFC at early stages are linked to human educational attainment and cognitive performance. Collectively, the comparison of the chromatin epigenetic landscape between rhesus macaques and humans suggests a potential role for regulatory elements in the evolution of differences in cognitive ability between non-human primates and humans.

With the development of electronic assembly workshop to the direction of digitalization, intelligence and customization, surface mount technology (SMT), as an important process of electronic assembly, has higher and higher requirements for mixed production. A key supporting point of mixed production integration is the acquisition and analysis of production data. This paper is for the characters of multi-variety and small-batch of SMT production line in military electronic industry. And considering the problem of inconsistence of data files and difficulty of reading data in the process of production, this paper used the scheme of distributed data management with system registration drive to realize data real-time acquisition of manufacturing execution in SMT workshop automatic, and promote comprehensive analysis ability of military SMT production data and processing efficiency in the way of multi-variety and small-batch effectively.

Motivation Single cell RNA-seq data offers us new resource and resolution to study cell type identity and its conversion. However, data analyses are challenging in dealing with noise, sparsity and poor annotation at single cell resolution. Detecting cell-type-indicative markers is promising to help denoising, clustering and cell type annotation. Results We developed a new method, scTIM, to reveal cell-type-indicative markers. scTIM is based on a multi-objective optimization framework to simultaneously maximize gene specificity by considering gene-cell relationship, maximize gene’s ability to reconstruct cell–cell relationship and minimize gene redundancy by considering gene–gene relationship. Furthermore, consensus optimization is introduced for robust solution. Experimental results on three diverse single cell RNA-seq datasets show scTIM’s advantages in identifying cell types (clustering), annotating cell types and reconstructing cell development trajectory. Applying scTIM to the large-scale mouse cell atlas data identifies critical markers for 15 tissues as ‘mouse cell marker atlas’, which allows us to investigate identities of different tissues and subtle cell types within a tissue. scTIM will serve as a useful method for single cell RNA-seq data mining. Availability and implementation scTIM is freely available at https://github.com/Frank-Orwell/scTIM. Supplementary information Supplementary data are available at Bioinformatics online.

Chromatin accessibility, as a powerful marker of active DNA regulatory elements, provides valuable information for understanding regulatory mechanisms. The revolution in high-throughput methods has accumulated massive chromatin accessibility profiles in public repositories. Nevertheless, utilization of these data is hampered by cumbersome collection, time-consuming processing, and manual chromatin accessibility (openness) annotation of genomic regions. To fill this gap, we developed OpenAnnotate (http://health.tsinghua.edu.cn/openannotate/) as the first web server for efficiently annotating openness of massive genomic regions across various biosample types, tissues, and biological systems. In addition to the annotation resource from 2729 comprehensive profiles of 614 biosample types of human and mouse, OpenAnnotate provides user-friendly functionalities, ultra-efficient calculation, real-time browsing, intuitive visualization, and elaborate application notebooks. We show its unique advantages compared to existing databases and toolkits by effectively revealing cell type-specificity, identifying regulatory elements and 3D chromatin contacts, deciphering gene functional relationships, inferring functions of transcription factors, and unprecedentedly promoting single-cell data analyses. We anticipate OpenAnnotate will provide a promising avenue for researchers to construct a more holistic perspective to understand regulatory mechanisms., Graphical Abstract Graphical AbstractOpenAnnotate is a web server for efficiently annotating chromatin accessibility of massive genomic regions across various biosample types, tissues and biological systems.

At present, the global manufacturing industry is accelerating towards the era of digitalization, networking and intelligence. Aiming at the manufacturing characteristics of electronic information equipment, such as the integration of scientific research and production, multi variety and small batch, fast renewal, high coupling of multi disciplines, and huge supply chain, this paper studies and analyzes the technical hotspots of intelligent manufacturing of electronic information equipment, such as 3D printing of mechatronic system structures, design and manufacturing of microsystem, immersive virtual assembly and digital twin. At the same time, based on the guiding ideology of Model-Based Systems Engineering (MBSE), the exploration and practice of electronic information equipment design mode, production mode, support mode and management mode are carried out. The research shows that the application of new technology and mode of intelligent manufacturing effectively shortens the development cycle of electronic information equipment, greatly improves the production efficiency and comprehensive utilization of resources, and significantly reduces the operating cost and the rate of defective products.

when the autonomous mobile robot moves, it depends on vision system to catch the targeted objects, so vision system is one of the key technologies for intelligent robot. In order to solve the problem of recognizing objects characterized by specified color and shape in the autonomous robot, a seed fill method based on HUV model is proposed and improved in this paper. First the hue histogram and brightness histogram from a color image are obtained and analyzed. The parameter U and V can represent the main feature of a color' but the hue loses sense when the values of RGB are very small. In this paper this shortcoming was improved through cooperating with shape. Then the objects characterized by specified color and shape were recognized effectively. Through experiments, the recognizing of targeted objects with specified color and shape were effectively realized in a variety of background, and the adaptability to changed light has been improved strongly.