GLIMMERS: glioma molecular markers exploration using long-read sequencing.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2024-04-15 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae058

Wichayapat Thongrattana, Tantip Arigul, Bhoom Suktitipat, Manop Pithukpakorn, Sith Sathornsumetee, Thidathip Wongsurawat, Piroon Jenjaroenpun

{"title":"GLIMMERS: glioma molecular markers exploration using long-read sequencing.","authors":"Wichayapat Thongrattana, Tantip Arigul, Bhoom Suktitipat, Manop Pithukpakorn, Sith Sathornsumetee, Thidathip Wongsurawat, Piroon Jenjaroenpun","doi":"10.1093/bioadv/vbae058","DOIUrl":null,"url":null,"abstract":"Summary: 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.Availability and implementation: GLIMMERS is available at https://gitlab.com/silol_public/glimmers under the terms of the MIT license.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11087932/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbae058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Summary: 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.

Availability and implementation: GLIMMERS is available at https://gitlab.com/silol_public/glimmers under the terms of the MIT license.

查看原文本刊更多论文

GLIMMERS：利用长线程测序探索胶质瘤分子标记物。

摘要：世卫组织修订的脑肿瘤分类和分级指南包括多个拷贝数变异（CNV）标记。利用纳米孔平台上的定制增量读取方法，检测整个基因组中的 CNV 和改变的周转时间大大缩短。然而，由于需要使用多种软件工具、提取 CNV 标记和解释结果，这种方法对非生物信息学家来说具有挑战性，这就造成了必要的时间和专业资源方面的障碍。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：利用长读数测序探索胶质瘤分子标记物，这是一种开放获取的工具，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，采用MIT许可条款。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

发文量