Biology subjects

Villanueva, E. M. F.

Publications and source records attributed to Villanueva, E. M. F..

3 recordsLinked to original sources

bioRxiv2021

Protist.guru: a comparative transcriptomics database for the protist kingdom

SummaryDuring the last few decades, the study of microbial ecology has been enabled by molecular and genomic data. DNA sequencing has revealed the surprising extent of microbial diversity and how microbial processes run global ecosystems. However, significant gaps in our understanding of the microbial world remain, and one example is that microbial eukaryotes, or protists, are still neglected. To address this gap, we used gene expression data from 15 distinct protist species to create protist.guru: an online database equipped with tools for identifying functional co-expression networks, gene families, and enriched gene clusters. Here, we show how our database can be used to reveal genes involved in essential pathways, such as the synthesis of secondary carotenoids in Haematococcus lacustris. We expect protist.guru to serve as a valuable resource for protistologists, as well as a catalyst for discoveries and new insights into the biological processes of microbial eukaryotes. AvailabilityThe database and co-expression networks are freely available from http://protist.guru/. The expression matrices and sample annotations are found in the supplementary data.

evolutionary biology↗

bioRxiv2020

Fungi.guru: comparative genomic and transcriptomic database for the Fungi kingdom

The fungi kingdom is composed of eukaryotic heterotrophs, which are responsible for balancing the ecosystem and play a major role as decomposers. They also produce a vast diversity of secondary metabolites, which have antibiotic or pharmacological properties. However, our lack of knowledge of gene function in fungi precludes us from tailoring them to our needs and tapping into their metabolic diversity. To remedy this, we gathered genomic and gene expression data of 19 most widely-researched fungi to build a database, fungi.guru, which contains tools for cross-species identification of conserved pathways, functional gene modules, and gene families. We exemplify how our database can elucidate the molecular function, biological process and cellular component of genes involved in various biological processes, by identifying a secondary metabolite pathway producing gliotoxin in Aspergillus fumigatus, the catabolic pathway of cellulose in Coprinopsis cinerea and the conserved DNA replication pathway in Fusarium graminearum and Pyricularia oryzae. The database is available at www.fungi.guru.

bioinformatics↗

bioRxiv2020

Prediction of novel components of bacterial ribosomes by crowd-sourced analysis of RNA sequencing data

Bacterial resistance to antibiotics is a growing problem that is projected to cause more deaths than cancer in 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the bacterial ribosomes, proteins that are involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. The data can be used to identify other vulnerabilities or bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowdsourced.

bioinformatics↗