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In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics. [ABSTRACT FROM AUTHOR]

It is of interest to identify and annotate milk associated genes using expression profiling and RNA-Seq data from milk somatic cells. RNA-Seq data was pre-processed and mapping was done to identify differentially expressed genes (DEG). The functional insights about the up and down regulated genes were gleaned using the protein-protein interaction Network in the STRING database followed by CytoHubba analysis in Cytoscope. Gene ontology, annotation and pathway enrichment was completed using ShinyGO, David tool and QTL analysis. These analysis shows that 21 genes are linked with the secretion of milk., Competing Interests: No conflict of interest exists, (© 2022 Biomedical Informatics.)

Identification of up regulated virulence genes in M. tuberculosis H37Rv using genome wide expression profiles is of interest in drug discovery for the disease. Hence, we report 17 up-regulated PPIN (Protein-Protein Interaction Network) enriched potential virulence linked genes using expression data available at the Gene Expression Omnibus (GEO) database for further consideration., Competing Interests: There is no conflict of interest exists., (© 2021 Biomedical Informatics.)

Pseudomonas syringae pv. phaseolicola is a major plant pathogen causing halo blight disease and has world-wide importance. The emerging post-genomics field of agrigenomics, together with the availability of whole genome sequences of a number of pathogens and host organisms, offer the promise for identification of potential drug targets using sequence comparison approaches. On the other hand, lack of gene expression data for most of the phytopathogenic microbes still remains a formidable barrier. The present study aimed at the prediction of drug targets in Pseudomonas syringae pv. phaseolicola by exploiting the knowledge of Codon Usage bias for gene expression subtractively, supported by gene expression analysis and sequence comparisons. Based on screening of the Database of Essential Genes using blastx, 158 of the total 5172 genes of P. syringae pv. phaseolicola were enlisted as vitally essential genes. Similarity search for these 158 essential genes against available host-plant sequences (Phaseolous vulgaris) led to the identification of homologues of 21 genes in the host genome, thus leaving behind a subset of 137 genes. Expression analysis of these 137 genes using RSCU(gene,) validated by microarray gene expression data suggested 22 genes had higher expression levels in the cell, and therefore their products have been identified as putative drug targets. The gene ontology analysis of these 22 genes revealed their indispensable roles in pivotal metabolic pathways of P. syringae pv. phaseolicola. Upon comparison of the sequences of these genes with other soil bacteria, we identified two genes that were unique to P. syringae pv. phaseolicola. The products of these genes can potentially be utilized for drug development so as to control the halo blight disease and thereby accelerate translation research in the nascent field of agrigenomics.