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Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples
Genome Biology ( IF 12.3 ) Pub Date : 2020-03-30 , DOI: 10.1186/s13059-020-01978-5
Jacob Schreiber 1 , Jeffrey Bilmes 1, 2 , William Stafford Noble 1, 3
Affiliation  

Recent efforts to describe the human epigenome have yielded thousands of epigenomic and transcriptomic datasets. However, due primarily to cost, the total number of such assays that can be performed is limited. Accordingly, we applied an imputation approach, Avocado, to a dataset of 3814 tracks of data derived from the ENCODE compendium, including measurements of chromatin accessibility, histone modification, transcription, and protein binding. Avocado shows significant improvements in imputing protein binding compared to the top models in the ENCODE-DREAM challenge. Additionally, we show that the Avocado model allows for efficient addition of new assays and biosamples to a pre-trained model.

中文翻译:

完成 ENCODE3 纲要可对各种分析和人类生物样本进行准确估算

最近描述人类表观基因组的努力已经产生了数以千计的表观基因组和转录组数据集。然而,主要由于成本,可以进行的此类测定的总数是有限的。因此,我们将插补方法 Avocado 应用于来自 ENCODE 纲要的 3814 条数据轨道的数据集,包括染色质可及性、组蛋白修饰、转录和蛋白质结合的测量。与 ENCODE-DREAM 挑战中的顶级模型相比,鳄梨在输入蛋白质结合方面显示出显着改进。此外,我们表明鳄梨模型允许将新的检测和生物样本有效地添加到预训练模型中。
更新日期:2020-03-30
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