Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species.
Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species.
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DOI：
10.1186/s12859-021-04164-x
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发表时间：
2021-05-24
影响因子：
3
通讯作者：
Veksler-Lublinsky I
中科院分区：
文献类型：
--
作者：
Ben Or G;Veksler-Lublinsky I
MicroRNA (miRNA) 是一种小型非编码 RNA，通过与信使 RNA (mRNA) 上的互补序列进行碱基配对，在转录后调节基因表达。由于高通量实验方法应用中涉及的技术挑战，直接真实 miRNA 靶标的数据集仅存在于少数模式生物体。基于机器学习 (ML) 的目标预测模型已在其中一些数据集上成功进行训练和测试。需要进一步将训练后的模型应用于无法获得实验训练数据的生物体。然而，目前尚不清楚 miRNA 与靶标相互作用的特征是如何进化的，以及某些特征在进化过程中是否保持不变，这引发了关于当前可用 ML 方法的一般跨物种适用性的问题。我们研究了 miRNA 与靶标相互作用规则的演变，并使用数据科学和机器学习方法来研究这些规则是否可以在物种之间转移。我们分析了四个物种（人类、小鼠、蠕虫、牛）的 8 个 miRNA 与靶标直接相互作用的数据集。使用机器学习分类器，我们实现了数据集内分类的高精度，并发现所有数据集最有影响力的特征显着重叠。为了探索数据集之间的关系，我们测量了它们的 miRNA 种子序列的差异并评估了跨数据集分类的性能。我们发现这两项指标都与比较物种之间的进化距离一致。 miRNA 靶向规则在物种之间的可转移性取决于几个因素，最相关的因素是种子家族的组成和进化距离。此外，我们的特征重要性结果表明，一些 miRNA 目标特征已经进化，而其他特征在物种进化过程中保持固定。我们的研究结果为未来开发目标预测工具奠定了基础，这些工具可应用于可获得最少实验数据的“非模型”生物。该代码可在 https://github.com/gbenor/TPVOD 上免费获取。在线版本补充材料可在 10.1186/s12859-021-04164-x 获取。
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona fide miRNA targets exist only for a few model organisms. Machine learning (ML)-based target prediction models were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms in which experimental training data are unavailable. However, it is largely unknown how the features of miRNA–target interactions evolve and whether some features have remained fixed during evolution, raising questions regarding the general, cross-species applicability of currently available ML methods. We examined the evolution of miRNA–target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. We analyzed eight datasets of direct miRNA–target interactions in four species (human, mouse, worm, cattle). Using ML classifiers, we achieved high accuracy for intra-dataset classification and found that the most influential features of all datasets overlap significantly. To explore the relationships between datasets, we measured the divergence of their miRNA seed sequences and evaluated the performance of cross-dataset classification. We found that both measures coincide with the evolutionary distance between the compared species. The transferability of miRNA–targeting rules between species depends on several factors, the most associated factors being the composition of seed families and evolutionary distance. Furthermore, our feature-importance results suggest that some miRNA–target features have evolved while others remained fixed during the evolution of the species. Our findings lay the foundation for the future development of target prediction tools that could be applied to “non-model” organisms for which minimal experimental data are available. The code is freely available at https://github.com/gbenor/TPVOD. The online version supplementary material available at 10.1186/s12859-021-04164-x.
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影响因子：
64.8
作者：
Baek, Daehyun;Villen, Judit;Shin, Chanseok;Camargo, Fernando D.;Gygi, Steven P.;Bartel, David P.
通讯作者：
Bartel, David P.
Biopython：用于计算分子生物学和生物信息学的免费 Python 工具。
DOI：
10.1093/bioinformatics/btp163
发表时间：
2009-06-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Cock PJ;Antao T;Chang JT;Chapman BA;Cox CJ;Dalke A;Friedberg I;Hamelryck T;Kauff F;Wilczynski B;de Hoon MJ
通讯作者：
de Hoon MJ
影响因子：
64.5
作者：
Hafner M;Landthaler M;Burger L;Khorshid M;Hausser J;Berninger P;Rothballer A;Ascano M Jr;Jungkamp AC;Munschauer M;Ulrich A;Wardle GS;Dewell S;Zavolan M;Tuschl T
通讯作者：
Tuschl T
影响因子：
14.9
作者：
Chang, Le;Zhou, Guangyan;Xia, Jianguo
通讯作者：
Xia, Jianguo
影响因子：
64.5
作者：
Helwak A;Kudla G;Dudnakova T;Tollervey D
通讯作者：
Tollervey D