Metagenomics is the study of genetic material recovered directly from environmental or clinical samples by a method called sequencing. The broad field may also be referred to as environmental genomics, ecogenomics, community genomics or microbiomics.
Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data. These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics. As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery.
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. The program received $170 million in funding by the NIH Common Fund from 2007 to 2016.
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.
Metaproteomics is an umbrella term for experimental approaches to study all proteins in microbial communities and microbiomes from environmental sources. Metaproteomics is used to classify experiments that deal with all proteins identified and quantified from complex microbial communities. Metaproteomics approaches are comparable to gene-centric environmental genomics, or metagenomics.
The Earth Microbiome Project (EMP) is an initiative founded by Janet Jansson, Jack Gilbert and Rob Knight in 2010 to collect natural samples and to analyze the microbial community around the globe.
Biological dark matter is an informal term for unclassified or poorly understood genetic material. This genetic material may refer to genetic material produced by unclassified microorganisms. By extension, biological dark matter may also refer to the un-isolated microorganism whose existence can only be inferred from the genetic material that they produce. Some of the genetic material may not fall under the three existing domains of life: Bacteria, Archaea and Eukaryota; thus, it has been suggested that a possible fourth domain of life may yet be discovered, although other explanations are also probable. Alternatively, the genetic material may refer to non-coding DNA and non-coding RNA produced by known organisms.
In metagenomics, binning is the process of grouping reads or contigs and assigning them to individual genome. Binning methods can be based on either compositional features or alignment (similarity), or both.
Mark J. Pallen is a research leader at the Quadram Institute and Professor of Microbial Genomics at the University of East Anglia. In recent years, he has been at the forefront of efforts to apply next-generation sequencing to problems in microbiology and ancient DNA research.
Karen Nelson is a Jamaican-born American microbiologist who was formerly president of the J. Craig Venter Institute (JCVI). On July 6, 2021 she joined Thermo Fisher Scientific as Chief Scientific Officer.
Microbial dark matter comprises the vast majority of microbial organisms that microbiologists are unable to culture in the laboratory, due to lack of knowledge or ability to supply the required growth conditions. Microbial dark matter is unrelated to the dark matter of physics and cosmology, but is so-called for the difficulty in effectively studying it as a result of its inability to be cultured by current methods. It is difficult to estimate its relative magnitude, but the accepted gross estimate is that as little as one percent of microbial species in a given ecological niche are culturable. In recent years, more effort has been directed towards deciphering microbial dark matter by means of recovering genome DNA sequences from environmental samples via culture independent methods such as single cell genomics and metagenomics. These studies have enabled insights into the evolutionary history and the metabolism of the sequenced genomes, providing valuable knowledge required for the cultivation of microbial dark matter lineages.
Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome.
Poribacteria are a candidate phylum of bacteria originally discovered in the microbiome of marine sponges (Porifera). Poribacteria are Gram-negative primarily aerobic mixotrophs with the ability for oxidative phosphorylation, glycolysis, and autotrophic carbon fixation via the Wood – Ljungdahl pathway. Poribacterial heterotrophy is characterised by an enriched set of glycoside hydrolases, uronic acid degradation, as well as several specific sulfatases. This heterotrophic repertoire of poribacteria was suggested to be involved in the degradation of the extracellular sponge host matrix.
PICRUSt is a bioinformatics software package. The name is an abbreviation for Phylogenetic Investigation of Communities by Reconstruction of Unobserved States.
Hologenomics is the omics study of hologenomes. A hologenome is the whole set of genomes of a holobiont, an organism together with all co-habitating microbes, other life forms, and viruses. While the term hologenome originated from the hologenome theory of evolution, which postulates that natural selection occurs on the holobiont level, hologenomics uses an integrative framework to investigate interactions between the host and its associated species. Examples include gut microbe or viral genomes linked to human or animal genomes for host-microbe interaction research. Hologenomics approaches have also been used to explain genetic diversity in the microbial communities of marine sponges.
Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining.
Metabolic gene clusters or biosynthetic gene clusters are tightly linked sets of mostly non-homologous genes participating in a common, discrete metabolic pathway. The genes are in physical vicinity to each other on the genome, and their expression is often coregulated. Metabolic gene clusters are common features of bacterial and most fungal genomes. They are less often found in other organisms. They are most widely known for producing secondary metabolites, the source or basis of most pharmaceutical compounds, natural toxins, chemical communication, and chemical warfare between organisms. Metabolic gene clusters are also involved in nutrient acquisition, toxin degradation, antimicrobial resistance, and vitamin biosynthesis. Given all these properties of metabolic gene clusters, they play a key role in shaping microbial ecosystems, including microbiome-host interactions. Thus several computational genomics tools have been developed to predict metabolic gene clusters.
Nikos Kyrpides is a Greek-American bioscientist who has worked on the origins of life, information processing, bioinformatics, microbiology, metagenomics and microbiome data science. He is a senior staff scientist at the Berkeley National Laboratory, head of the Prokaryote Super Program and leads the Microbiome Data Science program at the US Department of Energy Joint Genome Institute.
Natalie Anne Prystajecky a Canadian biologist and the Environmental Microbiology program at the British Columbia Centre for Disease Control Public Health Laboratory. She holds a Clinical Assistant Professor position at the University of British Columbia. During the COVID-19 pandemic Prystajecky was involved with the development COVID-19 testing capabilities.
Diversity-generating retroelements (DGRs) are a family of retroelements that were first found in Bordetella phage (BPP-1), and since been found in bacteria, Archaea, Archaean viruses, temperate phages, and lytic phages. DGRs benefit their host by mutating particular regions of specific target proteins, for instance, phage tail fiber in BPP-1, lipoprotein in legionella pneumophila, and TvpA in Treponema denticola . An error-prone reverse transcriptase is responsible for generating these hypervariable regions in target proteins. In mutagenic retrohoming, a mutagenized cDNA is reverse transcribed from a template region (TR), and is replaced with a segment similar to the template region called variable region (VR). Accessory variability determinant (Avd) protein is another component of DGRs, and its complex formation with the error-prone RT is of importance to mutagenic rehoming.
