STmut: a framework for visualizing somatic alterations in spatial transcriptomics data of cancer [0.03%]
STmut：用于可视化空间转录组数据中癌症体细胞改变的框架
Limin Chen,Darwin Chang,Bishal Tandukar et al.
Limin Chen et al.
Spatial transcriptomic technologies, such as the Visium platform, measure gene expression in different regions of tissues. Here, we describe new software, STmut, to visualize somatic point mutations, allelic imbalance, and copy number alter...
Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance [0.03%]
全面评估胚系变异呼叫性能的 Quartet DNA 参考材料和数据集
Luyao Ren,Xiaoke Duan,Lianhua Dong et al.
Luyao Ren et al.
Background: Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results...
Cooperation of MLL1 and Jun in controlling H3K4me3 on enhancers in colorectal cancer [0.03%]
MLL1和Jun在结直肠癌增强子上控制H3K4me3的协作作用
Xiang Lin,Ji-Dong Chen,Chen-Yu Wang et al.
Xiang Lin et al.
Background: Enhancer dysregulation is one of the important features for cancer cells. Enhancers enriched with H3K4me3 have been implicated to play important roles in cancer. However, their detailed features and regulatory...
Single-cell multiomics of the human retina reveals hierarchical transcription factor collaboration in mediating cell type-specific effects of genetic variants on gene regulation [0.03%]
人类视网膜单细胞多组学揭示了转录因子在介导基因变异对基因调控的类型特异性效应中的层次合作关系
Jun Wang,Xuesen Cheng,Qingnan Liang et al.
Jun Wang et al.
Background: Systematic characterization of how genetic variation modulates gene regulation in a cell type-specific context is essential for understanding complex traits. To address this question, we profile gene expressio...
Methylation-directed regulatory networks determine enhancing and silencing of mutation disease driver genes and explain inter-patient expression variation [0.03%]
甲基化导向的调控网络决定了对突变疾病驱动基因的增强和沉默作用并解释了患者间的表达差异
Yifat Edrei,Revital Levy,Daniel Kaye et al.
Yifat Edrei et al.
Background: Common diseases manifest differentially between patients, but the genetic origin of this variation remains unclear. To explore possible involvement of gene transcriptional-variation, we produce a DNA methylati...
Acute expression of human APOBEC3B in mice results in RNA editing and lethality [0.03%]
小鼠急性表达人APOBEC3B会导致RNA编辑并致死
Alicia Alonso de la Vega,Nuri Alpay Temiz,Rafail Tasakis et al.
Alicia Alonso de la Vega et al.
Background: RNA editing has been described as promoting genetic heterogeneity, leading to the development of multiple disorders, including cancer. The cytosine deaminase APOBEC3B is implicated in tumor evolution through D...
CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms [0.03%]
基于注意力机制的零样本建模工具CREaTor
Yongge Li,Fusong Ju,Zhiyuan Chen et al.
Yongge Li et al.
Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2 Mb from ...
Multi-omics analysis reveals the molecular response to heat stress in a "red tide" dinoflagellate [0.03%]
多组学分析揭示了造成赤潮的原生动物对高温胁迫的分子响应
Katherine E Dougan,Zhi-Luo Deng,Lars Wöhlbrand et al.
Katherine E Dougan et al.
Background: "Red tides" are harmful algal blooms caused by dinoflagellate microalgae that accumulate toxins lethal to other organisms, including humans via consumption of contaminated seafood. These algal blooms are drive...
Yuqiu Yang,Kaiwen Wang,Zeyu Lu et al.
Yuqiu Yang et al.
Recently, many analysis tools have been devised to offer insights into data generated via cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data...
N-of-one differential gene expression without control samples using a deep generative model [0.03%]
无需对照样本的单例差异基因表达分析的深度生成模型方法
Iñigo Prada-Luengo,Viktoria Schuster,Yuhu Liang et al.
Iñigo Prada-Luengo et al.
Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representati...