Fungi.guru: Comparative genomic and transcriptomic resource for the fungi kingdom

Comparative transcriptome database for Camellia sinensis reveals genes related to the cold sensitivity and albino mechanism of 'Anji Baicha'

Tea, a globally popular beverage, contains various beneficial secondary metabolites. Tea plants (Camellia sinensis) exhibit diverse genetic traits across cultivars, impacting yield, adaptability, morphology, and secondary metabolite composition. Many tea cultivars have been the subject of much research interest, which have led to the accumulation of publicly available RNA-seq data. As such, it has become possible to systematically summarize the characteristics of different cultivars at the transcriptomic level, identify functional genes, and infer gene functions through co-expression analysis. Here, the transcriptomes of 9 tea cultivars were assembled, and comparative analysis was conducted on the coding sequences of 13 cultivars. To give access to this data, we present TeaNekT (https://teanekt.sbs.ntu.edu.sg/), a web resource that facilitates the prediction of gene functions of various tea cultivars. We used TeaNekT to perform a cross-cultivar comparison of co-expressed gene clusters and tissue-specific gene expression. We observed that 'Anji Baicha' possesses the highest number of cultivar-specific genes and the second-highest number of expanded genes. These genes in 'Anji Baicha' tend to be enriched in functions associated with cold stress response, chloroplast thylakoid structure, and nitrogen metabolism. Notably, we identified three significantly expanded homologous genes in 'Anji Baicha' encoding the ICE1, SIZ1, and MAPKK2, which are closely associated with the cold sensitivity of 'Anji Baicha'. Additionally, one significantly expanded homologous gene in 'Anji Baicha' encoding regulatory factor RIQ may play a crucial role in the abnormal chloroplast structure and absence of thylakoid membranes in 'Anji Baicha'.

Bacteria.guru: Comparative Transcriptomics and Co-Expression Database for Bacterial Pathogens

While bacteria can be beneficial to our health, their deadly pathogenic potential has been an ever-present concern exacerbated by the emergence of drug-resistant strains. As such, there is a pressing urgency for an enhanced understanding of their gene function and regulation, which could mediate the development of novel antimicrobials. Transcriptomic analyses have been established as insightful and indispensable to the functional characterization of genes and identification of new biological pathways, but in the context of bacterial studies, they remain limited to species-specific datasets. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and researched bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, coexpression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid in discovering multi-faceted antibiotic targets and, overall, facilitate future bacterial research. AVAILABILITY: The database and coexpression networks are freely available from https://bacteria.guru/. Sample annotations can be found in the supplemental data.

LSTrAP-Kingdom: an automated pipeline to generate annotated gene expression atlases for kingdoms of life

MOTIVATION

There are now more than two million RNA sequencing experiments for plants, animals, bacteria and fungi publicly available, allowing us to study gene expression within and across species and kingdoms. However, the tools allowing the download, quality control and annotation of this data for more than one species at a time are currently missing.

RESULTS

To remedy this, we present the Large-Scale Transcriptomic Analysis Pipeline in Kingdom of Life (LSTrAP-Kingdom) pipeline, which we used to process 134 521 RNA-seq samples, achieving ∼12 000 processed samples per day. Our pipeline generated quality-controlled, annotated gene expression matrices that rival the manually curated gene expression data in identifying functionally related genes.

AVAILABILITY AND IMPLEMENTATION

LSTrAP-Kingdom is available from: https://github.com/wirriamm/plants-pipeline and is fully implemented in Python and Bash.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.