Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP.
Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP.
复制标题
DOI：
10.1186/s12864-021-08159-z
复制
发表时间：
2022-01-07
期刊：
影响因子：
4.4
通讯作者：
Shin DG
中科院分区：
文献类型：
--
作者：
Wang H;Joshi P;Hong SH;Maye PF;Rowe DW;Shin DG
干扰素调节因子8（Interferon Regulatory Factor-8，IRF 8）和核因子活化的T细胞c1（Nuclear Factor-Activated T Cells c1，NFATc 1）是两种在破骨细胞分化中起重要作用的转录因子。由于ChIP-seq技术，科学家现在可以估计IRF 8和NFATc 1的潜在全基因组靶基因。然而，发现在不同研究中一致上调或下调的靶基因是困难的，因为它需要从可比的背景下分析大量的高通量表达研究。我们已经开发了一种基于机器学习的方法，称为基于队列的TF目标预测系统（cTAP）来克服这个问题。该方法假设涉及感兴趣的转录因子的途径具有与相关生物过程有关的标记基因的多个“功能组”。它使用两个概念，基因充分存在（GP）和基因缺失不足（GA），除了差异表达基因的log 2倍数变化进行预测。通过应用多个机器学习模型进行目标预测，这些模型从log 2倍数变化和来自标准化队列的基因表达数据的四种类型的Z分数中学习GP和GA的模式。然后将学习的模式与推定的转录因子靶相关联，以鉴定在组群内一致地表现出上调/下调基因调控模式的基因。我们将这种方法应用于11个公开的GEO数据集与破骨细胞生成。我们的实验鉴定了少量与破骨细胞分化相关的Up/Down IRF 8和NFATc 1靶基因。使用GP和GA的机器学习模型产生的NFATc 1和IRF 8靶基因不同于仅使用log 2倍数变化。我们的文献调查显示，所有预测的靶基因在骨重建中具有已知的作用，特别是与免疫系统和破骨细胞的形成和功能有关，这表明我们的方法具有信心和有效性。cTAP的动机是认识到生物学家倾向于使用数据集中的Z评分值进行分析。然而，有效地使用cTAP的前提是在可比的背景下组装相当大的基因表达数据集队列。随着公共基因表达数据库的增长，使用cTAP等基于队列的分析方法的需求将变得越来越重要。
Interferon regulatory factor-8 (IRF8) and nuclear factor-activated T cells c1 (NFATc1) are two transcription factors that have an important role in osteoclast differentiation. Thanks to ChIP-seq technology, scientists can now estimate potential genome-wide target genes of IRF8 and NFATc1. However, finding target genes that are consistently up-regulated or down-regulated across different studies is hard because it requires analysis of a large number of high-throughput expression studies from a comparable context. We have developed a machine learning based method, called, Cohort-based TF target prediction system (cTAP) to overcome this problem. This method assumes that the pathway involving the transcription factors of interest is featured with multiple “functional groups” of marker genes pertaining to the concerned biological process. It uses two notions, Gene-Present Sufficiently (GP) and Gene-Absent Insufficiently (GA), in addition to log2 fold changes of differentially expressed genes for the prediction. Target prediction is made by applying multiple machine-learning models, which learn the patterns of GP and GA from log2 fold changes and four types of Z scores from the normalized cohort’s gene expression data. The learned patterns are then associated with the putative transcription factor targets to identify genes that consistently exhibit Up/Down gene regulation patterns within the cohort. We applied this method to 11 publicly available GEO data sets related to osteoclastgenesis. Our experiment identified a small number of Up/Down IRF8 and NFATc1 target genes as relevant to osteoclast differentiation. The machine learning models using GP and GA produced NFATc1 and IRF8 target genes different than simply using a log2 fold change alone. Our literature survey revealed that all predicted target genes have known roles in bone remodeling, specifically related to the immune system and osteoclast formation and functions, suggesting confidence and validity in our method. cTAP was motivated by recognizing that biologists tend to use Z score values present in data sets for the analysis. However, using cTAP effectively presupposes assembling a sizable cohort of gene expression data sets within a comparable context. As public gene expression data repositories grow, the need to use cohort-based analysis method like cTAP will become increasingly important.
登录
查看更多内容
影响因子：
4.5
作者：
Chicco D
通讯作者：
Chicco D
影响因子：
10.5
作者：
Kim K;Punj V;Kim JM;Lee S;Ulmer TS;Lu W;Rice JC;An W
通讯作者：
An W
影响因子：
4.6
作者：
Irie A;Yamamoto K;Miki Y;Murakami M
通讯作者：
Murakami M
影响因子：
3
作者：
Kim SK;Nam JW;Rhee JK;Lee WJ;Zhang BT
通讯作者：
Zhang BT
影响因子：
3.7
作者：
Davidson, Rebecca K.;Himes, Evan R.;Li, Jiliang
通讯作者：
Li, Jiliang