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FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions

Iwayama, Koji, Aisaka, Yuri, Kutsuna, Natsumaro, Nagano, Atsushi J.
Bioinformatics 2017 v.33 no.11 pp. 1672-1680
Oryza sativa, bioinformatics, computer software, environmental factors, environmental impact, prediction, rice, statistical models, transcriptome, transcriptomics
Motivation: 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. Results: 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. Availability and Implementation: Freely available from CRAN ( Contact: Supplementary information: Supplementary data are available at Bioinformatics online