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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

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Updated: Jan 10, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

基准测试大规模单细胞RNA-seq分析

Ilaria Billato¹, Herve Pages², Vince Carey³

¹Department of Biology, University of Padova, via Ugo Bassi 47, Padova, 35132, Italy.

bioRxiv : the preprint server for biology

|November 24, 2025

概括

此摘要是机器生成的。

基准测试单细胞RNA测序 (scRNA-seq) 分析框架显示,GPU加速和优化的算法显著提高了大型数据集的计算性能和可扩展性. 不同的管道提供了速度和准确性之间的权衡.

关键词:

一个单细胞RNA-seqq.一个基准的基准指标.可扩展性可扩展性.

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Last Updated: Jan 10, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

单细胞RNA测序 (scRNA-seq) 产生了大量的数据集,为分析带来了重大的计算挑战.
现有的分析框架在可扩展性,效率和准确性方面各不相同,需要全面的基准测试.
算法选择和硬件基础设施极大地影响scRNA-seq数据处理的性能.

研究的目的:

为了对五个著名的scRNA-seq分析框架的可扩展性,效率和准确性进行基准测试.
评估算法和基础设施因素对计算性能的影响.
为分析大规模scRNA-seq数据集提供实用指南.

主要方法:

使用各种数据集进行Seurat,OSCA,Scraper,Scanpy和Rapids_singlecell的系统比较,其中包括130万个细胞小鼠大脑数据集.
在不同的数据表示 (密集,稀疏,HDF5) 和硬件 (CPU与GPU) 中评估六个单值分解 (SVD) 算法用于主要组件分析 (PCA).
使用带有基准标签的数据集对聚类准确性的评估.

主要成果:

通过GPU加速的计算,特别是使用rapids_singlecell,实现了比基于CPU的方法加快15倍的速度.
在CPU上,ARPACK和IRLBA对于稀疏矩阵的效率最高,而随机SVD则在HDF5数据上表现出色.
OSCA 和 scrapper 展示了最高的集群精度 (ARI 高达 0.97),而 rapids_singlecell 是最快的整体管道.

结论:

在scRNA-seq分析中的可扩展性严重依赖于算法优化和硬件基础设施.
GPU 加速和优化的 BLAS/LAPACK 配置大大提高了性能.
基于生物导体的管道提供了强大的准确性,补充了用于大规模数据分析的更快的基于GPU的解决方案.