Iwayama Koji, Aisaka Yuri, Kutsuna Natsumaro, Nagano Atsushi J
Research Institute for Food and Agriculture, Ryukoku University, Otsu, Shiga, Japan.
LPixel Inc, Hongo, Bunkyo-ku, Tokyo, Japan.
Bioinformatics. 2017 Jun 1;33(11):1672-1680. doi: 10.1093/bioinformatics/btx049.
Considerable attention has been given to the quantification of environmental effects on organisms. In natural conditions, environmental factors are continuously changing in a complex manner. To reveal the effects of such environmental variations on organisms, transcriptome data in field environments have been collected and analyzed. Nagano et al. proposed a model that describes the relationship between transcriptomic variation and environmental conditions and demonstrated the capability to predict transcriptome variation in rice plants. However, the computational cost of parameter optimization has prevented its wide application.
: We propose a new statistical model and efficient parameter optimization based on the previous study. We developed and released FIT, an R package that offers functions for parameter optimization and transcriptome prediction. The proposed method achieves comparable or better prediction performance within a shorter computational time than the previous method. The package will facilitate the study of the environmental effects on transcriptomic variation in field conditions.
Freely available from CRAN ( https://cran.r-project.org/web/packages/FIT/ ).
Supplementary data are available at Bioinformatics online.
环境对生物体的影响量化已受到广泛关注。在自然条件下，环境因素以复杂的方式不断变化。为揭示这种环境变化对生物体的影响，已收集并分析了田间环境中的转录组数据。长野等人提出了一个描述转录组变异与环境条件之间关系的模型，并展示了预测水稻植株转录组变异的能力。然而，参数优化的计算成本阻碍了其广泛应用。
我们基于先前的研究提出了一种新的统计模型和高效的参数优化方法。我们开发并发布了FIT，一个R包，它提供了参数优化和转录组预测的功能。所提出的方法在比先前方法更短的计算时间内实现了相当或更好的预测性能。该包将有助于研究田间条件下环境对转录组变异的影响。
可从CRAN（https://cran.r-project.org/web/packages/FIT/ ）免费获取。
补充数据可在《生物信息学》在线获取。
