Thongrattana Wichayapat, Arigul Tantip, Suktitipat Bhoom, Pithukpakorn Manop, Sathornsumetee Sith, Wongsurawat Thidathip, Jenjaroenpun Piroon
Master of Science Program in Medical Bioinformatics (International Program), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.
Division of Medical Bioinformatics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.
Bioinform Adv. 2024 Apr 15;4(1):vbae058. doi: 10.1093/bioadv/vbae058. eCollection 2024.
The revised WHO guidelines for classifying and grading brain tumors include several copy number variation (CNV) markers. The turnaround time for detecting CNVs and alterations throughout the entire genome is drastically reduced with the customized read incremental approach on the nanopore platform. However, this approach is challenging for non-bioinformaticians due to the need to use multiple software tools, extract CNV markers and interpret results, which creates barriers due to the time and specialized resources that are necessary. To address this problem and help clinicians classify and grade brain tumors, we developed GLIMMERS: glioma molecular markers exploration using long-read sequencing, an open-access tool that automatically analyzes nanopore-based CNV data and generates simplified reports.
GLIMMERS is available at https://gitlab.com/silol_public/glimmers under the terms of the MIT license.
世界卫生组织修订的脑肿瘤分类和分级指南包括几个拷贝数变异（CNV）标记。通过纳米孔平台上的定制读取增量方法，检测整个基因组中CNV和改变的周转时间大幅缩短。然而，由于需要使用多个软件工具、提取CNV标记并解释结果，这种方法对非生物信息学家来说具有挑战性，因为这需要时间和专门资源，从而造成了障碍。为了解决这个问题并帮助临床医生对脑肿瘤进行分类和分级，我们开发了GLIMMERS：使用长读测序探索胶质瘤分子标记，这是一个开放获取工具，可自动分析基于纳米孔的CNV数据并生成简化报告。
GLIMMERS可在https://gitlab.com/silol_public/glimmers上获取，遵循MIT许可条款。
