Hu Xi, Song Jialin, Chyr Jacqueline, Wan Jinping, Wang Xiaoyan, Du Jianqiang, Duan Junbo, Zhang Huqin, Zhou Xiaobo, Wu Xiaoming
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States.
Front Genet. 2022 Aug 12;13:928862. doi: 10.3389/fgene.2022.928862. eCollection 2022.
Hematologic malignancies, such as acute promyelocytic leukemia (APL) and acute myeloid leukemia (AML), are cancers that start in blood-forming tissues and can affect the blood, bone marrow, and lymph nodes. They are often caused by genetic and molecular alterations such as mutations and gene expression changes. Alternative polyadenylation (APA) is a post-transcriptional process that regulates gene expression, and dysregulation of APA contributes to hematological malignancies. RNA-sequencing-based bioinformatic methods can identify APA sites and quantify APA usages as molecular indexes to study APA roles in disease development, diagnosis, and treatment. Unfortunately, APA data pre-processing, analysis, and visualization are time-consuming, inconsistent, and laborious. A comprehensive, user-friendly tool will greatly simplify processes for APA feature screening and mining. Here, we present APAview, a web-based platform to explore APA features in hematological cancers and perform APA statistical analysis. APAview server runs on Python3 with a Flask framework and a Jinja2 templating engine. For visualization, APAview client is built on Bootstrap and Plotly. Multimodal data, such as APA quantified by QAPA/DaPars, gene expression data, and clinical information, can be uploaded to APAview and analyzed interactively. Correlation, survival, and differential analyses among user-defined groups can be performed via the web interface. Using APAview, we explored APA features in two hematological cancers, APL and AML. APAview can also be applied to other diseases by uploading different experimental data.
血液系统恶性肿瘤，如急性早幼粒细胞白血病（APL）和急性髓系白血病（AML），是起源于造血组织的癌症，可影响血液、骨髓和淋巴结。它们通常由遗传和分子改变引起，如突变和基因表达变化。可变聚腺苷酸化（APA）是一种调节基因表达的转录后过程，APA失调会导致血液系统恶性肿瘤。基于RNA测序的生物信息学方法可以识别APA位点并量化APA使用情况，作为研究APA在疾病发生、诊断和治疗中作用的分子指标。不幸的是，APA数据的预处理、分析和可视化既耗时、又不一致且费力。一个全面、用户友好的工具将大大简化APA特征筛选和挖掘的过程。在此，我们展示了APAview，一个基于网络的平台，用于探索血液系统癌症中的APA特征并进行APA统计分析。APAview服务器在Python3上运行，带有Flask框架和Jinja2模板引擎。为了进行可视化，APAview客户端基于Bootstrap和Plotly构建。多模态数据，如通过QAPA/DaPars量化的APA、基因表达数据和临床信息，可以上传到APAview并进行交互式分析。可以通过网络界面在用户定义的组之间进行相关性、生存和差异分析。使用APAview，我们探索了两种血液系统癌症APL和AML中的APA特征。通过上传不同的实验数据将APAview应用于其他疾病。
