Kassambara Alboukadel, Moreaux Jerome
Department of Biological Hematology, CHU Montpellier, Montpellier, France.
Institute of Human Genetics, UMR 9002, CNRS and University of Montpellier, Montpellier, France.
Methods Mol Biol. 2018;1792:157-166. doi: 10.1007/978-1-4939-7865-6_11.
DNA microarrays have considerably helped to improve the understanding of biological processes and diseases including multiple myeloma (MM). GEP analyses have been successful to classify MM, define risk, identify therapeutic targets, predict treatment response, and understand drug resistance.This generated large amounts of publicly available data that could benefit from easy-to-use bioinformatics resources to analyze them. Here we present easy-to-use and open-access bioinformatics tools to extract and visualize the most prominent information from GEP data.
DNA微阵列极大地帮助了人们对包括多发性骨髓瘤（MM）在内的生物过程和疾病的理解。基因表达谱（GEP）分析已成功用于MM的分类、风险定义、治疗靶点识别、治疗反应预测以及耐药性理解。这产生了大量可公开获取的数据，而利用易于使用的生物信息学资源来分析这些数据会大有裨益。在此，我们展示了易于使用且开放获取的生物信息学工具，用于从GEP数据中提取并可视化最突出的信息。
