Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data [0.03%]
单细胞RNA和ATAC测序数据联合整合算法的基准测试
Michelle Y Y Lee,Klaus H Kaestner,Mingyao Li
Michelle Y Y Lee
Background: Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin accessibility in single nuclei. These two data types prov...
Utilizing AAV-mediated LEAPER 2.0 for programmable RNA editing in non-human primates and nonsense mutation correction in humanized Hurler syndrome mice [0.03%]
利用AAV载体介导的LEAPER 2.0在非人灵长类动物中进行程序化RNA编辑以及在人类黏多糖贮积症I型模型小鼠中进行无义突变纠正
Zongyi Yi,Yanxia Zhao,Zexuan Yi et al.
Zongyi Yi et al.
Background: The endogenous adenosine deaminases acting on RNA (ADAR) have been harnessed to facilitate precise adenosine-to-inosine editing on RNAs. However, the practicability of this approach for therapeutic purposes is...
aMeta: an accurate and memory-efficient ancient metagenomic profiling workflow [0.03%]
Ameta：一种准确且高效的记忆古代元基因组档案工作流程
Zoé Pochon,Nora Bergfeldt,Emrah Kırdök et al.
Zoé Pochon et al.
Analysis of microbial data from archaeological samples is a growing field with great potential for understanding ancient environments, lifestyles, and diseases. However, high error rates have been a challenge in ancient metagenomics, and th...
Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors [0.03%]
计算算法去卷积异质性批量卵巢肿瘤组织的表现取决于实验因素
Ariel A Hippen,Dalia K Omran,Lukas M Weber et al.
Ariel A Hippen et al.
Background: Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary ...
SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies [0.03%]
SPIRAL：整合和对齐不同实验、条件和技术的空间分辨转录组数据
Tiantian Guo,Zhiyuan Yuan,Yan Pan et al.
Tiantian Guo et al.
Properly integrating spatially resolved transcriptomics (SRT) generated from different batches into a unified gene-spatial coordinate system could enable the construction of a comprehensive spatial transcriptome atlas. Here, we propose SPIR...
Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and applications to B cell aging [0.03%]
单细胞RNA测序数据中细胞间表达差异的测量：比较分析及其在B淋巴细胞衰老研究中的应用
Huiwen Zheng,Jan Vijg,Atefeh Taherian Fard et al.
Huiwen Zheng et al.
Background: Single-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently facilitate the study of cell-to-cell variability at the cell type level. Although differ...
DIVE: a reference-free statistical approach to diversity-generating and mobile genetic element discovery [0.03%]
一种无参考基因组的统计方法DIVE用于多样性和可移动遗传元件的发现
Jordi Abante,Peter L Wang,Julia Salzman
Jordi Abante
Diversity-generating and mobile genetic elements are key to microbial and viral evolution and can result in evolutionary leaps. State-of-the-art algorithms to detect these elements have limitations. Here, we introduce DIVE, a new reference-...
GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership [0.03%]
GO मैत्रिक रेखांकन के साथ अनुक्रम गिनतारा डेटा की व्याख्या: सदस्यता के तरह Differential Expression Analysis
Peter Carbonetto,Kaixuan Luo,Abhishek Sarkar et al.
Peter Carbonetto et al.
Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or oth...
Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections [0.03%]
用于福尔马林固定石蜡包埋组织切片人宿主-病原体共定位研究的双空间解析转录组学
Hailey Sounart,Enikő Lázár,Yuvarani Masarapu et al.
Hailey Sounart et al.
Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from huma...
GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging [0.03%]
通过整合基因表达和图像的细胞分割深度学习框架GeneSegNet
Yuxing Wang,Wenguan Wang,Dongfang Liu et al.
Yuxing Wang et al.
When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We devel...