PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices.
PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices.
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作者：
Lachmann A;Rizzo KA;Bartal A;Jeon M;Clarke DJB;Ma'ayan A
通过RNA测序(RNA-SEQ)测量的基因-基因共表达相关性可用于基于这些数据中的协方差结构来预测基因注释。在我们之前的工作中，我们证明了来自数千个不同研究的统一排列的RNA-seq共表达数据对基因注释和蛋白质-蛋白质相互作用都具有很高的预测性。然而，预测的效果取决于基因注释和相互作用是细胞类型和组织特有的还是不可知性的。组织和细胞类型特定的基因-基因共表达数据有助于做出更准确的预测，因为许多基因在不同的细胞环境中以独特的方式发挥功能。然而，确定最佳的组织和细胞类型来划分全球基因-基因共表达矩阵是具有挑战性的。在这里，我们介绍并验证了一种名为从分层哺乳动物基因共表达预测基因洞察力(PrismEXP)的方法，该方法用于基于RNA-seq基因-基因共表达数据的改进的基因注释预测。使用来自ARCHS4的统一排列的数据，我们应用PrismEXP来预测各种基因注释，包括路径成员、基因本体论术语以及人类和小鼠的表型。在所有测试域上，使用PrismEXP进行的预测优于使用全局跨组织共表达相关矩阵方法进行的预测，并且使用一个注释域的训练可以用于预测其他域中的注释。通过展示PrismEXP预测在多个用例中的效用，我们展示了PrismEXP如何被用于增强无监督的机器学习方法，以更好地理解未被研究的基因和蛋白质的角色。为了使PrismEXP可访问，它通过一个用户友好的Web界面、一个Python包和一个Appyter提供。可用性。PrismEXP基于Web的应用程序具有预先计算的PrismEXP预测，可从以下网站获得：；PrismEXP还可以Appyter：和Python Package：的形式提供。
Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic. Tissue and cell type-specific gene-gene co-expression data can be useful for making more accurate predictions because many genes perform their functions in unique ways in different cellular contexts. However, identifying the optimal tissues and cell types to partition the global gene-gene co-expression matrix is challenging. Here we introduce and validate an approach called PRediction of gene Insights from Stratified Mammalian gene co-EXPression (PrismEXP) for improved gene annotation predictions based on RNA-seq gene-gene co-expression data. Using uniformly aligned data from ARCHS4, we apply PrismEXP to predict a wide variety of gene annotations including pathway membership, Gene Ontology terms, as well as human and mouse phenotypes. Predictions made with PrismEXP outperform predictions made with the global cross-tissue co-expression correlation matrix approach on all tested domains, and training using one annotation domain can be used to predict annotations in other domains. By demonstrating the utility of PrismEXP predictions in multiple use cases we show how PrismEXP can be used to enhance unsupervised machine learning methods to better understand the roles of understudied genes and proteins. To make PrismEXP accessible, it is provided via a user-friendly web interface, a Python package, and an Appyter. AVAILABILITY. The PrismEXP web-based application, with pre-computed PrismEXP predictions, is available from: ; PrismEXP is also available as an Appyter: ; and as Python package: .
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DOI：
10.1093/bioinformatics/btw216
发表时间：
2016-07-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Lachmann A;Giorgi FM;Lopez G;Califano A
通讯作者：
Califano A
影响因子：
14.9
作者：
Kuleshov MV;Jones MR;Rouillard AD;Fernandez NF;Duan Q;Wang Z;Koplev S;Jenkins SL;Jagodnik KM;Lachmann A;McDermott MG;Monteiro CD;Gundersen GW;Ma'ayan A
通讯作者：
Ma'ayan A
DOI：
10.1093/bioinformatics/btp026
发表时间：
2009-03-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Lachmann A;Ma'ayan A
通讯作者：
Ma'ayan A
影响因子：
14.9
作者：
Köhler S;Vasilevsky NA;Engelstad M;Foster E;McMurry J;Aymé S;Baynam G;Bello SM;Boerkoel CF;Boycott KM;Brudno M;Buske OJ;Chinnery PF;Cipriani V;Connell LE;Dawkins HJ;DeMare LE;Devereau AD;de Vries BB;Firth HV;Freson K;Greene D;Hamosh A;Helbig I;Hum C;Jähn JA;James R;Krause R;F Laulederkind SJ;Lochmüller H;Lyon GJ;Ogishima S;Olry A;Ouwehand WH;Pontikos N;Rath A;Schaefer F;Scott RH;Segal M;Sergouniotis PI;Sever R;Smith CL;Straub V;Thompson R;Turner C;Turro E;Veltman MW;Vulliamy T;Yu J;von Ziegenweidt J;Zankl A;Züchner S;Zemojtel T;Jacobsen JO;Groza T;Smedley D;Mungall CJ;Haendel M;Robinson PN
通讯作者：
Robinson PN
影响因子：
9.9
作者：
Drew K;Lee C;Huizar RL;Tu F;Borgeson B;McWhite CD;Ma Y;Wallingford JB;Marcotte EM
通讯作者：
Marcotte EM