Improved prediction of smoking status via isoform-aware RNA-seq deep learning models.
Improved prediction of smoking status via isoform-aware RNA-seq deep learning models.
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DOI：
10.1371/journal.pcbi.1009433
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发表时间：
2021-10
影响因子：
4.3
通讯作者：
Castaldi PJ
中科院分区：
文献类型：
--
作者：
Wang Z;Masoomi A;Xu Z;Boueiz A;Lee S;Zhao T;Bowler R;Cho M;Silverman EK;Hersh C;Dy J;Castaldi PJ
尽管剪接是真核基因表达的基本特征，但大多数基于基因表达数据的预测模型并不利用与基因剪接相关的信息。吸烟是许多疾病的重要环境危险因素，对基因表达具有深远影响。我们以吸烟状况作为预测目标，利用 COPDGene 研究中 2,557 名受试者的 RNA 测序数据的基因、外显子和亚型水平定量，开发了深度神经网络预测模型。我们观察到，当使用先前发布的预测模型中的 5 个基因的数据时，使用外显子和亚型量化的模型明显优于基因水平模型。先前发布的模型在原始出版物中的测试集性能为 0.82，而我们基于外显子的模型（包括外显子到亚型映射层）实现了 0.88 的测试集 AUC（接收者操作特征下的面积），使用来自更大基因集的外显子定量将 AUC 提高到 0.94。同种型变异性是 RNA-seq 数据中潜在信息的重要来源，可用于改进临床预测模型。基于基因表达的预测模型已经成为针对某些情况（例如早期乳腺癌治疗）的医疗决策的一部分。大多数这些模型所基于的测量方法并不能捕获基因剪接的关键方面，但通过 RNA 测序，可以捕获选择性剪接的其中一些方面并利用它们来改进临床预测。在之前预测吸烟状况的模型的基础上，我们发现选择性剪接的测量方法显着提高了这些预测模型的准确性。
Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, and it has profound effects on gene expression. Using smoking status as a prediction target, we developed deep neural network predictive models using gene, exon, and isoform level quantifications from RNA sequencing data in 2,557 subjects in the COPDGene Study. We observed that models using exon and isoform quantifications clearly outperformed gene-level models when using data from 5 genes from a previously published prediction model. Whereas the test set performance of the previously published model was 0.82 in the original publication, our exon-based models including an exon-to-isoform mapping layer achieved a test set AUC (area under the receiver operating characteristic) of 0.88, which improved to an AUC of 0.94 using exon quantifications from a larger set of genes. Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models. Predictive models based on gene expression are already a part of medical decision making for selected situations such as early breast cancer treatment. Most of these models are based on measures that do not capture critical aspects of gene splicing, but with RNA sequencing it is possible to capture some of these aspects of alternative splicing and use them to improve clinical predictions. Building on previous models to predict cigarette smoking status, we show that measures of alternative splicing significantly improve the accuracy of these predictive models.
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影响因子：
2.7
作者：
Parker MM;Chase RP;Lamb A;Reyes A;Saferali A;Yun JH;Himes BE;Silverman EK;Hersh CP;Castaldi PJ
通讯作者：
Castaldi PJ
DOI：
10.1073/pnas.97.18.10101
发表时间：
2000-08-29
影响因子：
11.1
作者：
Alter, O;Brown, PO;Botstein, D
通讯作者：
Botstein, D
RNA-seq 和基于微阵列的临床终点预测模型的比较。
影响因子：
12.3
作者：
Zhang W;Yu Y;Hertwig F;Thierry-Mieg J;Zhang W;Thierry-Mieg D;Wang J;Furlanello C;Devanarayan V;Cheng J;Deng Y;Hero B;Hong H;Jia M;Li L;Lin SM;Nikolsky Y;Oberthuer A;Qing T;Su Z;Volland R;Wang C;Wang MD;Ai J;Albanese D;Asgharzadeh S;Avigad S;Bao W;Bessarabova M;Brilliant MH;Brors B;Chierici M;Chu TM;Zhang J;Grundy RG;He MM;Hebbring S;Kaufman HL;Lababidi S;Lancashire LJ;Li Y;Lu XX;Luo H;Ma X;Ning B;Noguera R;Peifer M;Phan JH;Roels F;Rosswog C;Shao S;Shen J;Theissen J;Tonini GP;Vandesompele J;Wu PY;Xiao W;Xu J;Xu W;Xuan J;Yang Y;Ye Z;Dong Z;Zhang KK;Yin Y;Zhao C;Zheng Y;Wolfinger RD;Shi T;Malkas LH;Berthold F;Wang J;Tong W;Shi L;Peng Z;Fischer M
通讯作者：
Fischer M
转录的替代起始和终止位点驱动着大多数人类组织中的大多数转录本同工型差异。
影响因子：
14.9
作者：
Reyes A;Huber W
通讯作者：
Huber W
DOI：
10.1126/science.aad9417
发表时间：
2016-04-29
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Li YI;van de Geijn B;Raj A;Knowles DA;Petti AA;Golan D;Gilad Y;Pritchard JK
通讯作者：
Pritchard JK