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Morphine Re-arranges Chromatin Spatial Architecture of Primate Cortical Neurons. 吗啡重新排列灵长类皮质神经元的染色质空间结构。
IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Pub Date : 2023-06-01 Epub Date: 2023-05-19 DOI: 10.1016/j.gpb.2023.03.003
Liang Wang, Xiaojie Wang, Chunqi Liu, Wei Xu, Weihong Kuang, Qian Bu, Hongchun Li, Ying Zhao, Linhong Jiang, Yaxing Chen, Feng Qin, Shu Li, Qinfan Wei, Xiaocong Liu, Bin Liu, Yuanyuan Chen, Yanping Dai, Hongbo Wang, Jingwei Tian, Gang Cao, Yinglan Zhao, Xiaobo Cen

The expression of linear DNA sequence is precisely regulated by the three-dimensional (3D) architecture of chromatin. Morphine-induced aberrant gene networks of neurons have been extensively investigated; however, how morphine impacts the 3D genomic architecture of neurons is still unknown. Here, we applied digestion-ligation-only high-throughput chromosome conformation capture (DLO Hi-C) technology to investigate the effects of morphine on the 3D chromatin architecture of primate cortical neurons. After receiving continuous morphine administration for 90 days on rhesus monkeys, we discovered that morphine re-arranged chromosome territories, with a total of 391 segmented compartments being switched. Morphine altered over half of the detected topologically associated domains (TADs), most of which exhibited a variety of shifts, followed by separating and fusing types. Analysis of the looping events at kilobase-scale resolution revealed that morphine increased not only the number but also the length of differential loops. Moreover, all identified differentially expressed genes from the RNA sequencing data were mapped to the specific TAD boundaries or differential loops, and were further validated for changed expression. Collectively, an altered 3D genomic architecture of cortical neurons may regulate the gene networks associated with morphine effects. Our finding provides critical hubs connecting chromosome spatial organization and gene networks associated with the morphine effects in humans.

线性DNA序列的表达受到染色质三维结构的精确调控。吗啡诱导的神经元异常基因网络已被广泛研究;然而,吗啡如何影响神经元的3D基因组结构仍然是未知的。在这里,我们应用仅消化连接高通量染色体构象捕获(DLO-Hi-C)技术来研究吗啡对灵长类皮层神经元3D染色质结构的影响。在恒河猴身上连续服用吗啡90天后,我们发现吗啡重新排列了染色体区域,共有391个分段区室被切换。吗啡改变了超过一半的检测到的拓扑相关结构域(TAD),其中大多数表现出各种变化,随后是分离和融合类型。在千碱基尺度分辨率下对回路事件的分析表明,吗啡不仅增加了微分回路的数量,而且增加了微分环路的长度。此外,从RNA测序数据中鉴定的所有差异表达基因都被映射到特定的TAD边界或差异环,并被进一步验证为发生了变化。总的来说,皮层神经元的3D基因组结构的改变可能调节与吗啡效应相关的基因网络。我们的发现提供了连接染色体空间组织和与人类吗啡效应相关的基因网络的关键枢纽。
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引用次数: 0
Draft Genome of White-blotched River Stingray Provides Novel Clues for Niche Adaptation and Skeleton Formation. 白斑河黄貂鱼基因组草案为生态位适应和骨架形成提供了新线索。
IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Pub Date : 2023-06-01 Epub Date: 2022-12-05 DOI: 10.1016/j.gpb.2022.11.005
Jingqi Zhou, Ake Liu, Funan He, Yunbin Zhang, Libing Shen, Jun Yu, Xiang Zhang

The white-blotched river stingray (Potamotrygon leopoldi) is a cartilaginous fish native to the Xingu River, a tributary of the Amazon River system. As a rare freshwater-dwelling cartilaginous fish in the Potamotrygonidae family in which no member has the genome sequencing information available, P. leopoldi provides the evolutionary details in fish phylogeny, niche adaptation, and skeleton formation. In this study, we present its draft genome of 4.11 Gb comprising 16,227 contigs and 13,238 scaffolds, with contig N50 of 3937 kb and scaffold N50 of 5675 kb in size. Our analysis shows that P. leopoldi is a slow-evolving fish that diverged from elephant sharks about 96 million years ago. Moreover, two gene families related to the immune system (immunoglobulin heavy constant delta genes and T-cell receptor alpha/delta variable genes) exhibit expansion in P. leopoldi only. We also identified the Hox gene clusters in P. leopoldi and discovered that seven Hox genes shared by five representative fish species are missing in P. leopoldi. The RNA sequencing data from P. leopoldi and other three fish species demonstrate that fishes have a more diversified tissue expression spectrum when compared to mammals. Our functional studies suggest that lack of the gc gene encoding vitamin D-binding protein in cartilaginous fishes (both P. leopoldi and Callorhinchus milii) could partly explain the absence of hard bone in their endoskeleton. Overall, this genome resource provides new insights into the niche adaptation, body plan, and skeleton formation of P. leopoldi, as well as the genome evolution in cartilaginous fishes.

白斑河黄貂鱼(Potamotrygon leopoldi)是一种软骨鱼类,原产于亚马逊河系统的支流新谷河。leopoldi是Potamotrygonidae科中一种罕见的淡水软骨鱼类,没有任何成员拥有基因组测序信息,它提供了鱼类系统发育、生态位适应和骨骼形成的进化细节。在这项研究中,我们提出了由16227个重叠群和13238个支架组成的4.11 Gb的基因组草案,重叠群N50的大小为3937kb,支架N50的尺寸为5675kb。我们的分析表明,P.leopoldi是一种进化缓慢的鱼类,大约在9600万年前从象鲨分化而来。此外,与免疫系统相关的两个基因家族,免疫球蛋白重恒定德尔塔基因和T细胞受体α/德尔塔可变基因,仅在P.leopoldi中表达。我们还鉴定了P.leopoldi中的Hox基因簇,并发现五种代表性鱼类共有七个Hox基因在P.leopordi中缺失。来自P.leopoldi和其他三种鱼类的RNA测序数据表明,与相应的哺乳动物数据相比,鱼类的组织表达谱更加多样化。我们的功能研究表明,软骨鱼类(P.leopoldi和Callorhinchus milii)缺乏编码维生素D结合蛋白的GC基因,这可能是其内骨骼缺乏硬骨的部分原因。总的来说,这一基因组资源为P.leopoldi的生态位适应、身体计划、骨骼形成以及软骨鱼类的基因组进化提供了新的见解。
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引用次数: 0
Computational Assessment of the Expression-modulating Potential for Non-coding Variants. 非编码变异体表达调节潜力的计算评估
IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Pub Date : 2023-06-01 Epub Date: 2021-12-07 DOI: 10.1016/j.gpb.2021.10.003
Fang-Yuan Shi, Yu Wang, Dong Huang, Yu Liang, Nan Liang, Xiao-Wei Chen, Ge Gao

Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets, and is available online as a web server at http://carmen.gao-lab.org.

大规模的全基因组关联研究(GWAS)和表达量性状位点研究(eQTL)发现了多种通过影响基因表达而与遗传病相关的非编码变异。然而,有效、高效地精确定位因果变异仍然是一个严峻的挑战。在此,我们开发了一种新型算法 CARMEN,用于识别功能性非编码表达调节变异。多项评估表明,CARMEN 的性能优于最先进的工具。将CARMEN应用于GWAS和eQTL数据集,进一步精确定位了除已报道的前导单核苷酸多态性(SNPs)之外的几个因果变异。CARMEN 可以很好地扩展海量数据集,并可作为网络服务器在 http://carmen.gao-lab.org 上在线使用。
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引用次数: 0
Haplotype-resolved Genome of Sika Deer Reveals Allele-specific Gene Expression and Chromosome Evolution. 梅花鹿单倍型解析基因组揭示等位基因特异性基因表达和染色体进化。
IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Pub Date : 2023-06-01 Epub Date: 2022-11-15 DOI: 10.1016/j.gpb.2022.11.001
Ruobing Han, Lei Han, Xunwu Zhao, Qianghui Wang, Yanling Xia, Heping Li

Despite the scientific and medicinal importance of diploid sika deer (Cervus nippon), its genome resources are limited and haplotype-resolved chromosome-scale assembly is urgently needed. To explore mechanisms underlying the expression patterns of the allele-specific genes in antlers and the chromosome evolution in Cervidae, we report, for the first time, a high-quality haplotype-resolved chromosome-scale genome of sika deer by integrating multiple sequencing strategies, which was anchored to 32 homologous groups with a pair of sex chromosomes (XY). Several expanded genes (RET, PPP2R1A, PPP2R1B, YWHAB, YWHAZ, and RPS6) and positively selected genes (eIF4E, Wnt8A, Wnt9B, BMP4, and TP53) were identified, which could contribute to rapid antler growth without carcinogenesis. A comprehensive and systematic genome-wide analysis of allele expression patterns revealed that most alleles were functionally equivalent in regulating rapid antler growth and inhibiting oncogenesis. Comparative genomic analysis revealed that chromosome fission might occur during the divergence of sika deer and red deer (Cervus elaphus), and the olfactory sensation of sika deer might be more powerful than that of red deer. Obvious inversion regions containing olfactory receptor genes were also identified, which arose since the divergence. In conclusion, the high-quality allele-aware reference genome provides valuable resources for further illustration of the unique biological characteristics of antler, chromosome evolution, and multi-omics research of cervid animals.

尽管二倍体梅花鹿在科学和医学上具有重要意义,但其基因组资源有限,迫切需要单倍型解析的染色体规模组装。为了探索鹿角等位基因特异性基因的表达模式和鹿科染色体进化的机制,我们首次通过整合多种测序策略,构建了一个高质量的梅花鹿单倍型染色体规模基因组,该基因组锚定在具有一对性染色体(XY)的32个同源群中。鉴定了几个扩增基因(RET、PPP2R1A、PPP2R1B、YWAB、YWHAZ和RPS6)和阳性选择基因(eIF4E、Wnt8A、Wnt9B、BMP4和TP53),它们可以促进鹿角的快速生长而不致癌。对等位基因表达模式进行全面系统的全基因组分析表明,大多数等位基因在调节鹿角快速生长和抑制肿瘤发生方面功能等效。比较基因组分析表明,梅花鹿和马鹿(Cervus elaphus)在分化过程中可能发生染色体分裂,梅花鹿嗅觉可能比马鹿更强烈。还发现了明显的含有嗅觉受体基因的反转区,这是自分化以来出现的。总之,高质量的等位基因知晓参考基因组为进一步阐明鹿角的独特生物学特征、染色体进化和鹿动物的多组学研究提供了宝贵的资源。
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引用次数: 0
Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants. 非编码变异功能和致病性预测计算方法的性能比较。
IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Pub Date : 2023-06-01 Epub Date: 2022-03-08 DOI: 10.1016/j.gpb.2022.02.002
Zheng Wang, Guihu Zhao, Bin Li, Zhenghuan Fang, Qian Chen, Xiaomeng Wang, Tengfei Luo, Yijing Wang, Qiao Zhou, Kuokuo Li, Lu Xia, Yi Zhang, Xun Zhou, Hongxu Pan, Yuwen Zhao, Yige Wang, Lin Wang, Jifeng Guo, Beisha Tang, Kun Xia, Jinchen Li

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481-0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984-0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837-0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766-0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

人类基因组中的非编码变异通过其调控和修饰作用对人类性状和复杂疾病产生重大影响。因此,越来越多的计算方法被用来预测人类非编码序列中变异的影响。然而,缺乏经验的用户很难从数十种可用方法中选择合适的计算方法。为了解决这个问题,我们在四个独立的非编码变异基准数据集上评估了 24 种方法的 12 个性能指标:(1)来自临床相关序列变异(ClinVar)的罕见种系变异;(2)来自癌症中体细胞突变目录(COSMIC)的罕见体细胞变异;(3)来自编辑的表达量性状位点(eQTL)数据的常见调控变异;以及(4)来自编辑的全基因组关联研究(GWAS)的疾病相关常见变异。所有 24 种测试方法在不同条件下的表现都不尽相同,这表明在不同情况下它们的优缺点也各不相同。重要的是,对于来自 ClinVar 的罕见种系变异,现有方法的性能是可以接受的,接收者操作特征曲线下面积(AUROC)为 0.4481-0.8033,而对于来自 COSMIC 的罕见体细胞变异(AUROC = 0.4984-0.7131)、来自已整合 eQTL 数据的常见调控变异(AUROC = 0.4837-0.6472)和来自已整合 GWAS 的疾病相关常见变异(AUROC = 0.4766-0.5188),现有方法的性能较差。我们还比较了 24 种方法对自闭症谱系障碍中的非编码从头突变的预测性能,发现注释依赖性删除法(CADD)和上下文依赖性容许度评分法(CDTS)显示出更好的性能。总之,我们评估了 24 种计算方法在不同情况下的性能,为正确选择工具提供了初步建议,并为开发解释非编码变异的新技术提供了指导。
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引用次数: 0
Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes. 绘制多因子介导的染色质相互作用以评估肺癌相关基因的失调。
IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Pub Date : 2023-06-01 Epub Date: 2023-01-23 DOI: 10.1016/j.gpb.2023.01.004
Yan Zhang, Jingwen Zhang, Wei Zhang, Mohan Wang, Shuangqi Wang, Yao Xu, Lun Zhao, Xingwang Li, Guoliang Li

Studies on the lung cancer genome are indispensable for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, dysregulation of long-range chromatin interactions in lung cancer remains poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II (RNAPII), CCCTC-binding factor (CTCF), enhancer of zeste homolog 2 (EZH2), and histone 3 lysine 27 trimethylation (H3K27me3) using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes, either through loops or domains, and their distributions along the genome were distinct from and complementary to those associated with RNAPII. Cancer-related genes were highly enriched with chromatin interactions, and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes, such as additional repressive interactions on FOXO4 and promoter-promoter interactions between NF1 and RNF135. Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes, suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.

对肺癌癌症基因组的研究对于开发癌症的治疗方法是必不可少的。全基因组重新测序、全基因组关联研究和转录组测序极大地提高了我们对癌症基因组的理解。然而,癌症中长程染色质相互作用的失调仍不清楚。为了更好地理解癌症基因组的三维(3D)基因组相互作用特征,我们使用A549细胞系作为模型系统,并产生了与RNA聚合酶II(RNAPII)、CCCTC-结合因子(CTCF)、齐斯特同源物2(EZH2)增强子、,组蛋白3赖氨酸27三甲基化(H3K27me3)。分析表明,EZH2/H3K27me3介导的相互作用通过环或结构域进一步抑制靶基因,并且它们在基因组中的分布与RNAPII相关的分布不同并互补。癌症相关基因与染色质相互作用高度富集,A549细胞系特异性的染色质相互关系与致癌基因和肿瘤抑制基因相关,例如对FOXO4的额外抑制性相互作用以及NF1和RNF135之间的启动子-启动子相互作用。敲除与染色质相互作用相关的锚逆转了癌症相关基因的失调,这表明染色质的相互作用对于肺癌相关基因的正确表达至关重要。这些发现证明了癌症基因组的三维景观和基因调控关系。
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引用次数: 0
Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL. 基于TINDL的临床前到临床抗癌药物反应预测和生物标志物鉴定。
IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY Pub Date : 2023-06-01 Epub Date: 2023-02-11 DOI: 10.1016/j.gpb.2023.01.006
David Earl Hostallero, Lixuan Wei, Liewei Wang, Junmei Cairns, Amin Emad

Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

预测癌症患者对不同治疗的反应和识别药物反应的生物标志物是个体化医学的两个主要目标。在这里,我们开发了一个名为TINDL的深度学习框架,该框架完全针对临床前癌症细胞系(CCL)进行训练,以预测癌症患者对不同治疗的反应。TINDL利用组织形成的标准化来说明肿瘤的组织类型和癌症类型,并减少CCL和患者肿瘤之间的统计差异。此外,通过使深度学习黑匣子具有可解释性,该模型识别了一小组基因,这些基因的信使RNA(mRNA)表达水平可以预测训练模型中的药物反应,从而能够识别药物反应的生物标志物。使用来自CCL和癌症肿瘤的两个大型数据库的数据,我们发现该模型可以区分10种(14种)药物的敏感肿瘤和耐药性肿瘤,优于其他各种机器学习模型。此外,我们对该模型为其中一种药物(他莫昔芬)鉴定的10个基因进行的小干扰RNA(siRNA)敲除实验证实,在MCF7细胞中,他莫昔芬敏敏感性受到所有这些基因的显著影响,在T47D细胞中,这些基因中有7个受到影响。此外,涉及多种药物的基因指出了药物之间的共同作用机制,并提出了几个重要的信号通路。总之,本研究为癌症药物反应的预测和药物反应生物标志物的识别提供了一个强大的深度学习框架。代码可访问https://github.com/ddhostallero/tindl.
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引用次数: 0
deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues deCS:人类组织中单细胞RNA测序数据的系统细胞类型注释工具。
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-04-01 DOI: 10.1016/j.gpb.2022.04.001
Guangsheng Pei , Fangfang Yan , Lukas M. Simon , Yulin Dai , Peilin Jia , Zhongming Zhao

Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and subjective. The increasing number of scRNA-seq datasets, as well as numerous published genetic studies, has motivated us to build a comprehensive human cell type reference atlas. Here, we present decoding Cell type Specificity (deCS), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth, and feature selection strategies. Our results demonstrate that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait–cell type associations in 51 human complex traits, providing deep insights into the cellular mechanisms underlying disease pathogenesis. All documents for deCS, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.

单细胞RNA测序(scRNA-seq)正在彻底改变复杂和动态细胞机制的研究。然而,细胞类型注释仍然是一个主要挑战,因为它在很大程度上依赖于先验知识和手动管理,这是繁琐和主观的。越来越多的scRNA-seq数据集,以及大量已发表的遗传学研究,促使我们建立一个全面的人类细胞类型参考图谱。在这里,我们提出了解码细胞类型特异性(deCS),这是一种自动细胞类型注释方法,通过全面收集人类细胞类型表达谱和标记基因来增强。我们使用deCS对来自不同组织类型的scRNA-seq数据进行注释,并系统评估了不同条件下的注释准确性,包括参考面板、测序深度和特征选择策略。我们的结果表明,扩展引用对于提高注释准确性至关重要。与许多现有的最先进的注释工具相比,deCS显著减少了计算时间,提高了精度。deCS可以整合到标准scRNA-seq分析管道中,以增强细胞类型注释。最后,我们证明了deCS在识别51个人类复杂性状中的性状-细胞类型关联方面的广泛用途,为疾病发病机制的细胞机制提供了深入的见解。deCS的所有文档,包括源代码、用户手册、演示数据和教程,都可以在https://github.com/bsml320/deCS.
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引用次数: 0
VIS Atlas: A Database of Virus Integration Sites in Human Genome from NGS Data to Explore Integration Patterns VIS图谱:从NGS数据中获取人类基因组中病毒整合位点的数据库,以探索整合模式。
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-04-01 DOI: 10.1016/j.gpb.2023.02.005
Ye Chen , Yuyan Wang , Ping Zhou , Hao Huang , Rui Li , Zhen Zeng , Zifeng Cui , Rui Tian , Zhuang Jin , Jiashuo Liu , Zhaoyue Huang , Lifang Li , Zheying Huang , Xun Tian , Meiying Yu , Zheng Hu

Integration of oncogenic DNA viruses into the human genome is a key step in most virus-induced carcinogenesis. Here, we constructed a virus integration site (VIS) Atlas database, an extensive collection of integration breakpoints for three most prevalent oncoviruses, human papillomavirus, hepatitis B virus, and Epstein–Barr virus based on the next-generation sequencing (NGS) data, literature, and experimental data. There are 63,179 breakpoints and 47,411 junctional sequences with full annotations deposited in the VIS Atlas database, comprising 47 virus genotypes and 17 disease types. The VIS Atlas database provides (1) a genome browser for NGS breakpoint quality check, visualization of VISs, and the local genomic context; (2) a novel platform to discover integration patterns; and (3) a statistics interface for a comprehensive investigation of genotype-specific integration features. Data collected in the VIS Atlas aid to provide insights into virus pathogenic mechanisms and the development of novel antitumor drugs. The VIS Atlas database is available at https://www.vis-atlas.tech/.

将致癌DNA病毒整合到人类基因组中是大多数病毒诱导致癌的关键步骤。在这里,我们构建了一个病毒整合位点(VIS)图谱数据库,该数据库基于下一代测序(NGS)数据、文献和实验数据,广泛收集了三种最流行的肿瘤病毒,人乳头瘤病毒、乙型肝炎病毒和爱泼斯坦-巴尔病毒的整合断点。VIS图谱数据库中保存了63179个断点和47411个带完整注释的连接序列,包括47种病毒基因型和17种疾病类型。VIS Atlas数据库提供(1)用于NGS断点质量检查、VIS可视化和本地基因组上下文的基因组浏览器;(2) 一个发现集成模式的新颖平台;和(3)用于全面调查基因型特异性整合特征的统计界面。VIS图谱中收集的数据有助于深入了解病毒的致病机制和新型抗肿瘤药物的开发。VIS Atlas数据库可在https://www.vis-atlas.tech/.
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引用次数: 0
WheatCENet: A Database for Comparative Co-expression Networks Analysis of Allohexaploid Wheat and Its Progenitors 小麦CENet:一个用于异六倍体小麦及其祖先比较共表达网络分析的数据库。
IF 9.5 2区 生物学 Q1 Mathematics Pub Date : 2023-04-01 DOI: 10.1016/j.gpb.2022.04.007
Zhongqiu Li , Yiheng Hu , Xuelian Ma , Lingling Da , Jiajie She , Yue Liu , Xin Yi , Yaxin Cao , Wenying Xu , Yuannian Jiao , Zhen Su

Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks. Here, using large-scale transcriptome data, we constructed co-expression networks for diploid, tetraploid, and hexaploid wheat species, and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors, named WheatCENet. WheatCENet is a platform for searching and comparing specific functional co-expression networks, as well as identifying the related functions of the genes clustered therein. Functional annotations like pathways, gene families, protein–protein interactions, microRNAs (miRNAs), and several lines of epigenome data are integrated into this platform, and Gene Ontology (GO) annotation, gene set enrichment analysis (GSEA), motif identification, and other useful tools are also included. Using WheatCENet, we found that the network of WHEAT ABERRANT PANICLE ORGANIZATION 1 (WAPO1) has more co-expressed genes related to spike development in hexaploid wheat than its progenitors. We also found a novel motif of CCWWWWWWGG (CArG) specifically in the promoter region of WAPO-A1, suggesting that neofunctionalization of the WAPO-A1 gene affects spikelet development in hexaploid wheat. WheatCENet is useful for investigating co-expression networks and conducting other analyses, and thus facilitates comparative and functional genomic studies in wheat. WheatCENet is freely available at http://bioinformatics.cpolar.cn/WheatCENet and http://bioinformatics.cau.edu.cn/WheatCENet.

多倍体化事件后的遗传和表观遗传学变化可能导致可变的基因表达和修饰的调控网络。在这里,利用大规模转录组数据,我们构建了二倍体、四倍体和六倍体小麦物种的共表达网络,并建立了一个比较异六倍体麦及其祖先共表达网络的平台,名为WheatCENet。WheatCENet是一个用于搜索和比较特定功能共表达网络以及鉴定其中聚集的基因的相关功能的平台。将通路、基因家族、蛋白质-蛋白质相互作用、微小RNA(miRNA)和几行表观基因组数据等功能注释集成到该平台中,还包括基因本体论(GO)注释、基因集富集分析(GSEA)、基序鉴定和其他有用工具。利用WheatCENet,我们发现在六倍体小麦中,小麦异常小穗组织1(WAPO1)网络比其祖先具有更多与穗发育相关的共表达基因。我们还在WAPO-A1的启动子区发现了一个新的CCWWWWWWGG基序(CArG),这表明WAPO-A1基因的新功能化影响了六倍体小麦的小穗发育。小麦CENet有助于研究共表达网络和进行其他分析,从而促进小麦的比较和功能基因组研究。WheatCENet可在http://bioinformatics.cpolar.cn/WheatCENet和http://bioinformatics.cau.edu.cn/WheatCENet.
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引用次数: 6
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Genomics, Proteomics & Bioinformatics
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