Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates [0.03%]
通过理解和解释RNA速度估算来控制RNA速度
Shijie C Zheng,Genevieve Stein-O&#x;Brien,Leandros Boukas et al.
Shijie C Zheng et al.
Background: RNA velocity analysis of single cells offers the potential to predict temporal dynamics from gene expression. In many systems, RNA velocity has been observed to produce a vector field that qualitatively reflec...
The Quartet Data Portal: integration of community-wide resources for multiomics quality control [0.03%]
quartet数据门户：社区资源的整合以实现多组学质量控制
Jingcheng Yang,Yaqing Liu,Jun Shang et al.
Jingcheng Yang et al.
The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users...
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...