RNA-Sequencing (RNA-Seq) is currently the leading technology for genome-wide transcript quantification. Mapping the raw reads to transcript and gene level counts can be achieved by different aligners. Here we report an in-depth comparison of transcript quantification methods. Our goal is the specific use of cost-efficient RNA-Seq analysis for deployment in a cloud infrastructure composed of interacting microservices. The individual modules cover file transfer into the cloud and APIs to handle the cloud alignment jobs. We next demonstrate how newly generated RNA-Seq data can be placed in the context of thousands of previously published datasets in near real time. With in-depth benchmarks, we identify suitable gene count quantification methods to facilitate cost-effective, accurate, and cloud-based RNA-Seq analysis service. Pseudo-alignment algorithms such as kallisto and Salmon combine high read quality estimation with cost efficient runtime performance. HISAT2 is the fastest of the classical aligners with good alignment quality. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

中文翻译：

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云中可互操作的RNA-Seq分析。

RNA测序（RNA-Seq）是目前用于全基因组转录本定量的领先技术。通过不同的比对仪可以将原始读数映射到转录本和基因水平计数。在这里，我们报告了笔录量化方法的深入比较。我们的目标是在具有交互作用的微服务组成的云基础架构中部署具有成本效益的RNA-Seq分析的特定用途。各个模块涵盖了文件到云中的传输以及用于处理云对齐作业的API。接下来，我们演示如何将近实时地将新生成的RNA-Seq数据放置在成千上万个以前发布的数据集中。通过深入的基准测试，我们确定了合适的基因计数定量方法，以促进具有成本效益的，准确的，基于云的RNA-Seq分析服务。伪对齐算法（例如kallisto和Salmon）将高读取质量估计与具有成本效益的运行时性能结合在一起。HISAT2是传统对准器中速度最快的对准器，具有良好的对准质量。本文是由Federico Manuel Giorgi博士和Shaun Mahony博士编辑的题为：转录谱和调控基因网络的特刊的一部分。