Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.
Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.
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DOI：
10.1167/tvst.11.4.16
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发表时间：
2022-04-01
影响因子：
3
通讯作者：
Cherry, Timothy J.
中科院分区：
文献类型：
--
作者：
VandenBosch, Leah S.;Luu, Kelsey;Timms, Andrew E.;Challam, Shriya;Wu, Yue;Lee, Aaron Y.;Cherry, Timothy J.
先前的研究已经证明了视网膜疾病中特定顺式调节变体的重要性;然而，确定调节变体的功能影响仍然是一个重大挑战。在这项研究中，我们利用一种机器学习方法，对来自成年人视网膜的表观基因组数据进行训练，以系统地量化顺式调节变体的预测影响。我们使用人类视网膜DNA可及性数据（ATAC-seq）来确定一组18.9k高置信度的推定顺式调控元件。这些元素中的80%用于利用基于缺口k-mer支持向量机的方法来训练机器学习模型。应用计算机模拟饱和诱变和变体评分来预测顺式调节元件内所有潜在单核苷酸变体的功能影响。在20%保留数据集中测试影响分数，并与等位基因群体频率、系统发育保守性、转录因子（TF）结合基序和现有的大规模平行报告基因测定数据进行比较。我们生成了一个模型，该模型以95%的准确度区分人类视网膜调节元件和阴性测试序列。在3.7k人视网膜克雷斯的保持测试集中，对所有可能的单核苷酸变体进行评分。具有负面影响分数的变体与参考等位基因的较高系统发育保守性、预测的TF结合基序的破坏和大规模平行报告基因表达相关。我们证明了人类视网膜表观基因组数据的实用性，以训练机器学习模型，用于预测非编码调控序列变体的影响。我们的模型准确地评分序列和预测假定的转录因子结合基序。这种方法有可能加快在不明原因的视网膜疾病的背景下，致病性非编码序列变异的特征。该工作流程和所得到的数据集作为一个有前途的基因组工具，以促进视网膜中功能破坏性非编码突变的临床优先级。
Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learning approach, trained on epigenomic data from the adult human retina, to systematically quantify the predicted impact of cis-regulatory variants. We used human retinal DNA accessibility data (ATAC-seq) to determine a set of 18.9k high-confidence, putative cis-regulatory elements. Eighty percent of these elements were used to train a machine learning model utilizing a gapped k-mer support vector machine–based approach. In silico saturation mutagenesis and variant scoring was applied to predict the functional impact of all potential single nucleotide variants within cis-regulatory elements. Impact scores were tested in a 20% hold-out dataset and compared to allele population frequency, phylogenetic conservation, transcription factor (TF) binding motifs, and existing massively parallel reporter assay data. We generated a model that distinguishes between human retinal regulatory elements and negative test sequences with 95% accuracy. Among a hold-out test set of 3.7k human retinal CREs, all possible single nucleotide variants were scored. Variants with negative impact scores correlated with higher phylogenetic conservation of the reference allele, disruption of predicted TF binding motifs, and massively parallel reporter expression. We demonstrated the utility of human retinal epigenomic data to train a machine learning model for the purpose of predicting the impact of non-coding regulatory sequence variants. Our model accurately scored sequences and predicted putative transcription factor binding motifs. This approach has the potential to expedite the characterization of pathogenic non-coding sequence variants in the context of unexplained retinal disease. This workflow and resulting dataset serve as a promising genomic tool to facilitate the clinical prioritization of functionally disruptive non-coding mutations in the retina.
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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：
3.7
作者：
Holder LB;Haque MM;Skinner MK
通讯作者：
Skinner MK
在发育，重编程和肿瘤发生过程中，视网膜的动态表观遗传景观。
影响因子：
16.2
作者：
Aldiri I;Xu B;Wang L;Chen X;Hiler D;Griffiths L;Valentine M;Shirinifard A;Thiagarajan S;Sablauer A;Barabas ME;Zhang J;Johnson D;Frase S;Zhou X;Easton J;Zhang J;Mardis ER;Wilson RK;Downing JR;Dyer MA;St. Jude Children’s Research Hospital—Washington University Pediatric Cancer Genome Project
通讯作者：
St. Jude Children’s Research Hospital—Washington University Pediatric Cancer Genome Project
影响因子：
2.7
作者：
Howard, ML;Davidson, EH
通讯作者：
Davidson, EH
影响因子：
2.6
作者：
Klein, David C.;Hainer, Sarah J.
通讯作者：
Hainer, Sarah J.