PDF(Pubmed)
Abstract：
BACKGROUND: The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts.
RESULTS: To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.
CONCLUSIONS: The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.
RESULTS: To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.
CONCLUSIONS: The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.
摘要：
背景：下一代测序（NGS）技术的不断发展导致了大规模基因组数据的产生。虽然基因组数据整合和分析的工具变得越来越可用，在许多生物学背景下，概念和分析的复杂性仍然是一个巨大的挑战。
结果：要解决此问题，我们描述了基因组数据集成最佳实践的六步教程，包括（1）设计数据矩阵；（2）针对数据描述制定特定的生物学问题，选择和预测；（3）选择适合目标问题的工具；（4）对数据进行预处理；（5）进行初步分析，最后(6)执行基因组数据集成。
结论：本教程已在杨树（PopulusL.）产生的公开基因组数据上进行了测试和演示，木本植物模型.我们还为无监督多块分析开发了一种新的图形输出，cimDiablo_v2，可在https://forgemia获得。inra.fr/umr-gdec/omics-整合在杨树上，并允许在基因组数据变异和相互作用中选择主驱动因素。
结果：要解决此问题，我们描述了基因组数据集成最佳实践的六步教程，包括（1）设计数据矩阵；（2）针对数据描述制定特定的生物学问题，选择和预测；（3）选择适合目标问题的工具；（4）对数据进行预处理；（5）进行初步分析，最后(6)执行基因组数据集成。
结论：本教程已在杨树（PopulusL.）产生的公开基因组数据上进行了测试和演示，木本植物模型.我们还为无监督多块分析开发了一种新的图形输出，cimDiablo_v2，可在https://forgemia获得。inra.fr/umr-gdec/omics-整合在杨树上，并允许在基因组数据变异和相互作用中选择主驱动因素。
