ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.
ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.
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DOI：
10.1371/journal.pcbi.1009376
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发表时间：
2021-09
影响因子：
4.3
通讯作者：
Arner E
中科院分区：
文献类型：
--
作者：
Umarov R;Li Y;Arakawa T;Takizawa S;Gao X;Arner E
调控元件通过转录起始（启动子）和通过增强远端区域的转录（增强子）来控制基因表达。调控元件的准确识别是基因组注释和理解基因表达模式的基础。虽然有许多尝试来开发计算启动子和增强子鉴定方法，但仍然缺乏分析长基因组序列的可靠工具。预测方法通常在全基因组范围内表现不佳，因为阴性的数量远远高于训练集中的数量。为了解决这个问题，我们提出了一个动态的负集更新计划与两个模型的方法，使用一个模型扫描的基因组和其他一个用于测试候选位置。所开发的方法实现了良好的基因组水平的性能，并保持强大的性能时，适用于其他脊椎动物物种，无需重新训练。此外，人类基因组上未注释的预测调节区域富含疾病相关变异，这表明它们可能是真正的调节元件，而不是假阳性。我们使用报告分析验证了高分“假阳性”预测，并且所有测试的候选者都成功验证，证明了我们的方法发现新的人类调控区的能力。调控元件（启动子和增强子）的鉴定对于理解基因表达模式是重要的。启动子和增强子的集合对于非模式生物体是不完整的，并且甚至对于人类基因组，仍然存在未注释的区域，例如已知基因的替代启动子或仅在一小部分细胞中或在特定条件下表达的启动子。尽管实验技术的发展，调控区注释仍然是昂贵和费力的和计算方法可以加快这一过程提供候选人的验证。我们开发了一个易于使用的工具，能够在真核生物基因组中的调控区域注释。所开发的方法减少了通过在训练集中包括困难的样本而产生的假阳性的数量。该方法由两个深度学习模型组成，其中一个模型扫描基因组并识别推定的调控区域，而另一个模型在识别区域内精确定位转录起始位点（TSS）位置。使用报告基因测定验证预测区域，发现人类基因组中以前未知的调控区域。训练后的模型实现了良好的全基因组性能，并得到了有意义的提取生物特征的支持。
Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other vertebrate species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring “false positive” predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions. Identification of regulatory elements (promoters and enhancers) is important for understanding gene expression patterns. The set of promoters and enhancers is not complete for non-model organisms and even for the human genome there are still unannotated regions, such as alternative promoters for the known genes or promoters that are only expressed in a small fraction of cells or under specific conditions. Despite the development of experimental techniques, the regulatory regions annotation remains expensive and laborious and computational methods can speed up this process by providing candidates for the validation. We developed an easy-to-use tool capable of regulatory regions annotation in eukaryotic genomes. The developed method reduces the number of false positives made by including difficult samples in the training set. The method consists of two deep learning models, where one model scans the genome and identifies putative regulatory regions while the other model pinpoints the Transcription Start Site (TSS) location within the identified region. The predicted regions were validated using reporter assay, finding previously unknown regulatory regions in the human genome. The trained model achieved good genome-wide performance and was supported by meaningful extracted biological features.
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DOI：
10.1093/bioinformatics/btr064
发表时间：
2011-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Grant CE;Bailey TL;Noble WS
通讯作者：
Noble WS
影响因子：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen
影响因子：
16.6
作者：
Andersson, Robin;Andersen, Peter Refsing;Sandelin, Albin
通讯作者：
Sandelin, Albin
DOI：
10.4161/15384101.2014.949201
发表时间：
2014
期刊：
Cell cycle (Georgetown, Tex.)
影响因子：
--
作者：
Mundade R;Ozer HG;Wei H;Prabhu L;Lu T
通讯作者：
Lu T
影响因子：
16.6
作者：
Kopp, Wolfgang;Monti, Remo;Akalin, Altuna
通讯作者：
Akalin, Altuna