Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome.
While metagenomics focuses on studying the genomic content and on identifying which microbes are present within a community, metatranscriptomics can be used to study the diversity of the active genes within such community, to quantify their expression levels and to monitor how these levels change in different conditions (e.g., physiological vs. pathological conditions in an organism). The advantage of metatranscriptomics is that it can provide information about differences in the active functions of microbial communities that would otherwise appear to have similar make-up.
The microbiome has been defined as a microbial community occupying a well-defined habitat. These communities are ubiquitous and can play a key role in maintenance of the characteristics of their environment, and an imbalance in these communities can negatively affect the activities of the setting in which they reside. To study these communities, and to then determine their impact and correlation with their niche, different omics approaches have been used. While metagenomics can help researchers generate a taxonomic profile of the sample, metatrascriptomics provides a functional profile by analysing which genes are expressed by the community. It is possible to infer what genes are expressed under specific conditions, and this can be done using functional annotations of expressed genes.
Since metatranscriptomics focuses on what genes are expressed, it enables the characterization of the active functional profile of the entire microbial community. The overview of the gene expression in a given sample is obtained by capturing the total mRNA of the microbiome and performing whole-metatranscriptomics shotgun sequencing.
Although microarrays can be exploited to determine the gene expression profiles of some model organisms, next-generation sequencing and third-generation sequencing are the preferred techniques in metatranscriptomics.
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A microbiome () is the community of microorganisms that can usually be found living together in any given habitat. It was defined more precisely in 1988 by Whipps et al. as "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity". In 2020, an international panel of experts published the outcome of their discussions on the definition of the microbiome.
The Human Microbiome Project (HMP) was a United States National Institutes of Health (NIH) research initiative to improve understanding of the microbiota involved in human health and disease. Launched in 2007, the first phase (HMP1) focused on identifying and characterizing human microbiota. The second phase, known as the Integrative Human Microbiome Project (iHMP) launched in 2014 with the aim of generating resources to characterize the microbiome and elucidating the roles of microbes in health and disease states.
Microbiota are the range of microorganisms that may be commensal, mutualistic, or pathogenic found in and on all multicellular organisms, including plants. Microbiota include bacteria, archaea, protists, fungi, and viruses, and have been found to be crucial for immunologic, hormonal, and metabolic homeostasis of their host. The term microbiome describes either the collective genomes of the microbes that reside in an ecological niche or within the microbes themselves.
This course will take place from 3rd to 7th June 2024.It will introduce the workflows and techniques that are used for the analysis of bulk and single cell RNA-seq data. It will empower students to
This course will train doctoral students to use bioinformatic tools to analyse amplicon and metagenomic sequences. In addition, we will also touch upon meta-transcriptomics and meta-proteomics.
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The hollow fiber ultrafiltration (HFUF)-based microbial concentration method is widely applied for monitoring pathogenic viruses and microbial indicators in environmental water samples. However, the HFUF-based method can co-concentrate substances that inte ...