Milone, D. H. | bioRxiv Science

bioRxiv2020

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

BackgroundDeep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system which combines 3D printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. ResultsWe developed a novel deep learning based root extraction method which leverages the latest advances in convolutional neural networks for image segmentation, and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. ConclusionsAltogether, our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies as well as the screening of clock-related mutants, revealing novel root traits.

plant biology↗

bioRxiv2020

High precision in microRNA prediction: a novel genome-wide approach based on convolutional deep residual networks

MotivationMicroRNAs (miRNAs) are small non-coding RNAs that have a key role in the regulation of gene expression. The importance of miRNAs is widely acknowledged by the community nowadays, and the precise prediction of novel candidates with computational methods is still very needed. This could be done by searching homologous with sequence alignment tools, but this will be restricted only to sequences very similar to the known miRNA precursors (pre-miRNAs). Further-more, other important properties of pre-miRNAs, such as the secondary structure, are not taken into account by these methods. Many machine learning approaches were proposed in the last years to fill this gap, but these methods were tested in very controlled conditions, which are not fulfilled, for example, when predicting in newly sequenced genomes, where no miRNAs are known. If these methods are used under real conditions, the precision achieved is far from the one published. ResultsThis work provides a novel approach for dealing with the computational prediction of pre-miRNAs: a convolutional deep residual neural network. The proposed model has been tested on several complete genomes of animals and plants, achieving a precision up to 5 times higher than other approaches at the same recall rates. Also, a novel validation methodology is used to ensure that the performance reported can be achieved when using the method on new unknown species. AvailabilityTo provide fast an easy access to mirDNN, a web demo is available here. It can process fasta files with multiple sequences to calculate the prediction scores, and can generate the nucleotide importance plots. The full source code of this project is available here and here. Contactcyones@sinc.unl.edu.ar

bioinformatics↗