Consistently processed RNA sequencing data from 50 sources enriched for pediatric data.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Holly C Beale, Katrina Learned, Ellen T Kephart, A Geoffrey Lyle, Anouk van den Bout, Molly McCabe, Kathryn Echandia-Monroe, Mansi J Khare, Elise Y Huang, Sneha Jariwala, Reyna Antilla, Allison Cheney, Alex G Lee, Leanne C Sayles, Stanley G Leung, Yvonne A Vasquez, Lauren Sanders, David Haussler, Sofie R Salama, E Alejandro Sweet-Cordero, Olena M Vaske
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引用次数: 0

Abstract

Larger cohorts improve the power of tumor gene expression analysis, but the signal is muddied if datasets are processed using different methods or have inaccurate metadata. Here we present five compendia containing consistently processed gene expression data derived from 16,446 diverse RNA sequencing datasets. To create the compendia, we obtained access to RNA sequence data from repositories containing public data as well as clinical partners with access to non-published data. We then assessed the quality, quantified gene expression, harmonized clinical metadata, and released the expression values and metadata without access restrictions. These datasets have been used for diverse projects ranging from identifying similarities between tumor types to assessing how well cell lines recapitulate tumors. They have also been used for n-of-1 analysis to identify genes with unusual expression patterns in a single sample and to infer molecular diagnosis. The comparison to new data is enabled by our dockerized, freely available pipeline. The compendia have been cited in at least 20 publications.

始终如一地处理来自50个来源的RNA测序数据,丰富了儿科数据。
较大的队列提高了肿瘤基因表达分析的能力,但如果数据集使用不同的方法处理或具有不准确的元数据,信号就会变得模糊。在这里,我们提出了五个纲要,其中包含来自16,446个不同RNA测序数据集的一致处理的基因表达数据。为了创建该纲要,我们从包含公共数据的存储库中获取了RNA序列数据,并从临床合作伙伴那里获得了未发表的数据。然后我们评估质量,量化基因表达,协调临床元数据,并发布表达值和元数据,不受访问限制。这些数据集已用于各种项目,从识别肿瘤类型之间的相似性到评估细胞系对肿瘤的概括程度。它们也被用于n-of-1分析,以识别单个样本中异常表达模式的基因,并推断分子诊断。与新数据的比较是通过我们的码头化、免费提供的管道实现的。该纲要已被至少20种出版物引用。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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