Biology subjects

Prochetto, S.

Publications and source records attributed to Prochetto, S..

2 recordsLinked to original sources

bioRxiv2024

Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning

Kranz syndrome is a set of leaf anatomical and functional characteristics of species using C4 photosynthesis. The current model for the evolution of C4 photosynthesis from a C3 ancestor proposes a series of gradual anatomical changes followed by a biochemical adaptation of the C4 cycle enzymatic machinery. In this work, leaf anatomical traits from closely related C3, C4 and intermediate species (Proto-Kranz, PK) were analyzed together with gene expression data to discover potential drivers for the establishment of Kranz anatomy using unsupervised machine learning. Species-specific Self-Organizing Maps (SOM) were developed to group features (genes and phenotypic traits) into clusters (neurons) according to their expression along the leaf developmental gradient. The analysis with SOM allowed us to identify candidate genes as enablers of key anatomical traits differentiation related to the area of mesophyll (M) and bundle sheath (BS) cells, vein density, and the interface between M and BS cells. At the same time, we identified a small subset of genes that displaced together with the change in the area of the BS cell along evolution suggesting a salient role in the origin of Kranz anatomy in grasses. HighlightKranz syndrome is a set of leaf anatomical and functional characteristics of species using C4 photosynthesis. We discovered, with a novel machine learning methodology, some key genes for Kranz differentiation.

plant biology↗

bioRxiv2023

sincFold: end-to-end learning of short- and long-range interactions for RNA folding

MotivationCoding and non-coding RNA molecules participate in many important biological processes. Non-coding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged thanks to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but still leaving a wide margin for improvement. ResultsIn this work we present sincFold an end-to-end deep learning approach that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared against classical methods and recent deep learning models, showing that it can outperform state-of-the-art methods. AvailabilityThe source code is available at https://github.com/sinc-lab/sincFold (v0.16) and the web access is provided at https://sinc.unl.edu.ar/web-demo/sincFold Contactlbugnon@sinc.unl.edu.ar

bioinformatics↗