@article{Casimiro-Soriguer2024,

title = {Transcriptome Analysis of Stigmas of Vicia faba L. Flowers},

author = {Inés Casimiro-Soriguer and David Aguilar-Benitez and Natalia Gutierrez and Ana M. Torres},

doi = {10.3390/plants13111443},

issn = {2223-7747},

year  = {2024},

date = {2024-06-00},

journal = {Plants},

volume = {13},

number = {11},

publisher = {MDPI AG},

abstract = {Pollination in angiosperms depends on complex communication between pollen grains and stigmas, classified as wet or dry, depending on the presence or absence of secretions at the stigma surface, respectively. In species with wet stigma, the cuticle is disrupted and the presence of exudates is indicative of their receptivity. Most stigma studies are focused on a few species and families, many of them with self-incompatibility systems. However, there is scarce knowledge about the stigma composition in Fabaceae, the third angiosperm family, whose stigmas have been classified as semidry. Here we report the first transcriptome profiling and DEGs of Vicia faba L. styles and stigmas from autofertile (flowers able to self-fertilize in the absence of manipulation, whose exudate is released spontaneously) and autosterile (flowers that need to be manipulated to break the cuticle and release the exudates to be receptive) inbred lines. From the 76,269 contigs obtained from the de novo assembly, only 45.1% of the sequences were annotated with at least one GO term. A total of 115,920, 75,489, and 70,801 annotations were assigned to Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories, respectively, and 5918 differentially expressed genes (DEGs) were identified between the autofertile and the autosterile lines. Among the most enriched metabolic pathways in the DEGs subset were those related with amino acid biosynthesis, terpenoid metabolism, or signal transduction. Some DEGs have been related with previous QTLs identified for autofertility traits, and their putative functions are discussed. The results derived from this work provide an important transcriptomic reference for style-stigma processes to aid our understanding of the molecular mechanisms involved in faba bean fertilization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Espinosa_2024,

title = {Advancements in long-read genome sequencing technologies and algorithms},

author = {Elena Espinosa and Rocio Bautista and Rafael Larrosa and Oscar Plata},

url = {http://dx.doi.org/10.1016/j.ygeno.2024.110842},

doi = {10.1016/j.ygeno.2024.110842},

issn = {0888-7543},

year  = {2024},

date = {2024-05-01},

journal = {Genomics},

volume = {116},

number = {3},

pages = {110842},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Cordoba-Caballero2024,

title = {Exploring miRNA–target gene pair detection in disease with coRmiT},

author = {Jose Cordoba-Caballero and James R Perkins and Federico García-Criado and Diana Gallego and Alicia Navarro-Sánchez and Mireia Moreno-Estellés and Concepción Garcés and Fernando Bonet and Carlos Romá-Mateo and Rocio Toro and Belén Perez and Pascual Sanz and Matthias Kohl and Elena Rojano and Pedro Seoane and Juan A G Ranea},

doi = {10.1093/bib/bbae060},

issn = {1477-4054},

year  = {2024},

date = {2024-01-22},

volume = {25},

number = {2},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               A wide range of approaches can be used to detect micro RNA (miRNA)–target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}