Preprocessing of Public RNA-sequencing Datasets to Facilitate Downstream Analyses of Human Diseases: Dataset

<strong>Publicly available RNA-sequencing (RNA-seq) data are a rich resource for elucidating the mechanisms of human disease; however, preprocessing these data requires considerable bioinformatic expertise and computational infrastructure. Analyzing multiple datasets with a consistent computational workflow increases the accuracy of downstream meta-analyses. This collection of datasets represents the human intracellular transcriptional response to disorders and diseases such as acute lymphoblastic leukemia (ALL), B-cell lymphomas, chronic obstructive pulmonary disease (COPD), colorectal cancer, lupus erythematosus; as well as infection with pathogens including Borrelia burgdorferi, hantavirus, influenza A virus, Middle East respiratory syndrome coronavirus (MERS-CoV), Streptococcus pneumoniae, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We calculated the statistically significant differentially expressed genes and Gene Ontology (GO) terms for all datasets. In addition, a subset of the datasets also include results from splice variant analyses, intracellular signaling pathway enrichments as well as read mapping and quantification. All analyses were performed using well-established algorithms and are provided to facilitate future data mining activities, wet lab studies, and to accelerate collaboration and discovery.</strong>公开可用的RNA测序（RNA-sequencing，简称RNA-seq）数据是阐明人类疾病发病机制的宝贵资源；然而，此类数据的预处理工作需要深厚的生物信息学专业知识与完善的计算基础设施支撑。采用统一的计算流程分析多组数据集，可提升下游荟萃分析的准确性。本数据集合集收录了人类细胞内对多种病症与疾病的转录应答数据，涵盖急性淋巴细胞白血病（ALL）、B细胞淋巴瘤、慢性阻塞性肺疾病（COPD）、结直肠癌、红斑狼疮等病症，以及伯氏疏螺旋体、汉坦病毒、甲型流感病毒、中东呼吸综合征冠状病毒（MERS-CoV）、肺炎链球菌、呼吸道合胞病毒（RSV）、严重急性呼吸综合征冠状病毒（SARS-CoV）、严重急性呼吸综合征冠状病毒2（SARS-CoV-2）等病原体感染相关数据。我们为所有数据集计算了具有统计学显著性的差异表达基因与基因本体（Gene Ontology，简称GO）注释条目。此外，部分子数据集还包含剪接变体分析、细胞内信号通路富集分析以及读段比对与定量的分析结果。所有分析均采用经过验证的成熟算法完成，本数据集合集的发布旨在助力后续的数据挖掘与湿实验研究，加速科研合作与成果发现。