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

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'.

LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

Despite the abundance of species with transcriptomic data, a significant number of species still lack sequenced genomes, making it difficult to study gene function and expression in these organisms. While de novo transcriptome assembly can be used to assemble protein-coding transcripts from RNA-sequencing (RNA-seq) data, the datasets used often only feature samples of arbitrarily selected or similar experimental conditions, which might fail to capture condition-specific transcripts. We developed the Large-Scale Transcriptome Assembly Pipeline for de novo assembled transcripts (LSTrAP-denovo) to automatically generate transcriptome atlases of eukaryotic species. Specifically, given an NCBI TaxID, LSTrAP-denovo can (1) filter undesirable RNA-seq accessions based on read data, (2) select RNA-seq accessions via unsupervised machine learning to construct a sample-balanced dataset for download, (3) assemble transcripts via over-assembly, (4) functionally annotate coding sequences (CDS) from assembled transcripts and (5) generate transcriptome atlases in the form of expression matrices for downstream transcriptomic analyses. LSTrAP-denovo is easy to implement, written in Python, and is freely available at https://github.com/pengkenlim/LSTrAP-denovo/.

OrthoRefine: automated enhancement of prior ortholog identification via synteny

BACKGROUND

Identifying orthologs continues to be an early and imperative step in genome analysis but remains a challenging problem. While synteny (conservation of gene order) has previously been used independently and in combination with other methods to identify orthologs, applying synteny in ortholog identification has yet to be automated in a user-friendly manner. This desire for automation and ease-of-use led us to develop OrthoRefine, a standalone program that uses synteny to refine ortholog identification.

RESULTS

We developed OrthoRefine to improve the detection of orthologous genes by implementing a look-around window approach to detect synteny. We tested OrthoRefine in tandem with OrthoFinder, one of the most used software for identification of orthologs in recent years. We evaluated improvements provided by OrthoRefine in several bacterial and a eukaryotic dataset. OrthoRefine efficiently eliminates paralogs from orthologous groups detected by OrthoFinder. Using synteny increased specificity and functional ortholog identification; additionally, analysis of BLAST e-value, phylogenetics, and operon occurrence further supported using synteny for ortholog identification. A comparison of several window sizes suggested that smaller window sizes (eight genes) were generally the most suitable for identifying orthologs via synteny. However, larger windows (30 genes) performed better in datasets containing less closely related genomes. A typical run of OrthoRefine with ~ 10 bacterial genomes can be completed in a few minutes on a regular desktop PC.

CONCLUSION

OrthoRefine is a simple-to-use, standalone tool that automates the application of synteny to improve ortholog detection. OrthoRefine is particularly efficient in eliminating paralogs from orthologous groups delineated by standard methods.

Co-PATHOgenex web application for assessing complex stress responses in pathogenic bacteria

IMPORTANCE

Unveiling gene co-expression networks in bacterial pathogens has the potential for gaining insights into their adaptive strategies within the host environment. Here, we developed Co-PATHOgenex, an interactive and user-friendly web application that enables users to construct networks from gene co-expressions using custom-defined thresholds (https://avicanlab.shinyapps.io/copathogenex/). The incorporated search functions and visualizations within the tool simplify the usage and facilitate the interpretation of the analysis output. Co-PATHOgenex also includes stress stimulons for various bacterial species, which can help identify gene products not previously associated with a particular stress condition.