MyeloDB: a multi-omics resource for multiple myeloma

IF 3.9 4区 生物学 Q1 GENETICS & HEREDITY
Ambuj Kumar, Keerthana Vinod Kumar, Kavita Kundal, Avik Sengupta, Simran Sharma, Kunjulakshmi R, Rahul Kumar
{"title":"MyeloDB: a multi-omics resource for multiple myeloma","authors":"Ambuj Kumar,&nbsp;Keerthana Vinod Kumar,&nbsp;Kavita Kundal,&nbsp;Avik Sengupta,&nbsp;Simran Sharma,&nbsp;Kunjulakshmi R,&nbsp;Rahul Kumar","doi":"10.1007/s10142-023-01280-0","DOIUrl":null,"url":null,"abstract":"<div><p>Multiple myeloma (MM) is a common type of blood cancer affecting plasma cells originating from the lymphoid B-cell lineage. It accounts for about 10% of all hematological malignancies and can cause significant end-organ damage. The emergence of genomic technologies such as next-generation sequencing and gene expression analysis has opened new possibilities for early detection of multiple myeloma and identification of personalized treatment options. However, there remain significant challenges to overcome in MM research, including integrating multi-omics data, achieving a comprehensive understanding of the disease, and developing targeted therapies and biomarkers. The extensive data generated by these technologies presents another challenge for data analysis and interpretation. To bridge this gap, we have developed a multi-omics open-access database called MyeloDB. It includes gene expression profiling, high-throughput CRISPR-Cas9 screens, drug sensitivity resources profile, and biomarkers. MyeloDB contains 47 expression profiles, 3 methylation profiles comprising a total of 5630 patient samples and 25 biomarkers which were reported in previous studies. In addition to this, MyeloDB can provide significant insight of gene mutations in MM on drug sensitivity. Furthermore, users can download the datasets and conduct their own analyses. Utilizing this database, we have identified five novel genes, i.e., <i>CBFB</i>, <i>MANF</i>, <i>MBNL1</i>, <i>SEPHS2</i>, and <i>UFM1</i> as potential drug targets for MM. We hope MyeloDB will serve as a comprehensive platform for researchers and foster novel discoveries in MM. MyeloDB Database URL: https://project.iith.ac.in/cgntlab/myelodb/.</p></div>","PeriodicalId":574,"journal":{"name":"Functional & Integrative Genomics","volume":"24 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Functional & Integrative Genomics","FirstCategoryId":"99","ListUrlMain":"https://link.springer.com/article/10.1007/s10142-023-01280-0","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

Abstract

Multiple myeloma (MM) is a common type of blood cancer affecting plasma cells originating from the lymphoid B-cell lineage. It accounts for about 10% of all hematological malignancies and can cause significant end-organ damage. The emergence of genomic technologies such as next-generation sequencing and gene expression analysis has opened new possibilities for early detection of multiple myeloma and identification of personalized treatment options. However, there remain significant challenges to overcome in MM research, including integrating multi-omics data, achieving a comprehensive understanding of the disease, and developing targeted therapies and biomarkers. The extensive data generated by these technologies presents another challenge for data analysis and interpretation. To bridge this gap, we have developed a multi-omics open-access database called MyeloDB. It includes gene expression profiling, high-throughput CRISPR-Cas9 screens, drug sensitivity resources profile, and biomarkers. MyeloDB contains 47 expression profiles, 3 methylation profiles comprising a total of 5630 patient samples and 25 biomarkers which were reported in previous studies. In addition to this, MyeloDB can provide significant insight of gene mutations in MM on drug sensitivity. Furthermore, users can download the datasets and conduct their own analyses. Utilizing this database, we have identified five novel genes, i.e., CBFB, MANF, MBNL1, SEPHS2, and UFM1 as potential drug targets for MM. We hope MyeloDB will serve as a comprehensive platform for researchers and foster novel discoveries in MM. MyeloDB Database URL: https://project.iith.ac.in/cgntlab/myelodb/.

Abstract Image

MyeloDB:多发性骨髓瘤的多组学资源
多发性骨髓瘤(MM)是一种常见的血癌,影响源自淋巴 B 细胞系的浆细胞。它约占所有血液恶性肿瘤的 10%,可造成严重的内脏损害。新一代测序和基因表达分析等基因组学技术的出现,为早期检测多发性骨髓瘤和确定个性化治疗方案提供了新的可能性。然而,多发性骨髓瘤研究仍需克服重大挑战,包括整合多组学数据、全面了解该疾病以及开发靶向疗法和生物标记物。这些技术产生的大量数据为数据分析和解读带来了另一个挑战。为了弥补这一差距,我们开发了一个多组学开放数据库,名为 MyeloDB。它包括基因表达谱分析、高通量 CRISPR-Cas9 筛选、药物敏感性资源概况和生物标记物。MyeloDB 包含 47 个表达图谱、3 个甲基化图谱(共 5630 个患者样本)和 25 个生物标记物,这些生物标记物在之前的研究中已有报道。此外,MyeloDB 还能提供 MM 基因突变对药物敏感性的重要影响。此外,用户还可以下载数据集并进行自己的分析。利用该数据库,我们发现了五个新基因,即 CBFB、MANF、MBNL1、SEPHS2 和 UFM1,它们是治疗 MM 的潜在药物靶点。我们希望 MyeloDB 成为研究人员的综合平台,促进 MM 领域的新发现。MyeloDB 数据库网址:https://project.iith.ac.in/cgntlab/myelodb/.
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
CiteScore
3.50
自引率
3.40%
发文量
92
审稿时长
2 months
期刊介绍: Functional & Integrative Genomics is devoted to large-scale studies of genomes and their functions, including systems analyses of biological processes. The journal will provide the research community an integrated platform where researchers can share, review and discuss their findings on important biological questions that will ultimately enable us to answer the fundamental question: How do genomes work?
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信