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In genetics, shotgun sequencing is a method used for sequencing random DNA strands. It is named by analogy with the rapidly expanding, quasi-random shot grouping of a shotgun.
Genomics is an interdisciplinary field of molecular biology focusing on the structure, function, evolution, mapping, and editing of genomes. A genome is an organism's complete set of DNA, including all of its genes as well as its hierarchical, three-dimensional structural configuration. In contrast to genetics, which refers to the study of individual genes and their roles in inheritance, genomics aims at the collective characterization and quantification of all of an organism's genes, their interrelations and influence on the organism. Genes may direct the production of proteins with the assistance of enzymes and messenger molecules. In turn, proteins make up body structures such as organs and tissues as well as control chemical reactions and carry signals between cells. Genomics also involves the sequencing and analysis of genomes through uses of high throughput DNA sequencing and bioinformatics to assemble and analyze the function and structure of entire genomes. Advances in genomics have triggered a revolution in discovery-based research and systems biology to facilitate understanding of even the most complex biological systems such as the brain.
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.
The Global Ocean Sampling Expedition (GOS) is an ocean exploration genome project whose goal is to assess genetic diversity in marine microbial communities and to understand their role in nature's fundamental processes. The two-year journey, which used Craig Venter's personal yacht, originated in Halifax, Canada, circumnavigated the globe and terminated in the U.S. in January 2006. The expedition sampled water from Halifax, Nova Scotia to the Eastern Tropical Pacific Ocean. During 2007, sampling continued along the west coast of North America.
16S ribosomal RNA is the RNA component of the 30S subunit of a prokaryotic ribosome. It binds to the Shine-Dalgarno sequence and provides most of the SSU structure.
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.
The Earth Microbiome Project (EMP) was an initiative founded by Janet Jansson, Jack Gilbert, and Rob Knight in 2010 to collect natural samples and analyze microbial life 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 microorganisms 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.
Microbial phylogenetics is the study of the manner in which various groups of microorganisms are genetically related. This helps to trace their evolution. To study these relationships biologists rely on comparative genomics, as physiology and comparative anatomy are not possible methods.
AMPHORA is an open-source bioinformatics workflow. AMPHORA2 uses 31 bacterial and 104 archaeal phylogenetic marker genes for inferring phylogenetic information from metagenomic datasets. Most of the marker genes are single copy genes, therefore AMPHORA2 is suitable for inferring the accurate taxonomic composition of bacterial and archaeal communities from metagenomic shotgun sequencing data.
Viral metagenomics uses metagenomic technologies to detect viral genomic material from diverse environmental and clinical samples. Viruses are the most abundant biological entity and are extremely diverse; however, only a small fraction of viruses have been sequenced and only an even smaller fraction have been isolated and cultured. Sequencing viruses can be challenging because viruses lack a universally conserved marker gene so gene-based approaches are limited. Metagenomics can be used to study and analyze unculturable viruses and has been an important tool in understanding viral diversity and abundance and in the discovery of novel viruses. For example, metagenomics methods have been used to describe viruses associated with cancerous tumors and in terrestrial ecosystems.
METAGENassist is a freely available web server for comparative metagenomic analysis. Comparative metagenomic studies involve the large-scale comparison of genomic or taxonomic census data from bacterial samples across different environments. Historically this has required a sound knowledge of statistics, computer programming, genetics and microbiology. As a result, only a small number of researchers are routinely able to perform comparative metagenomic studies. To circumvent these limitations, METAGENassist was developed to allow metagenomic analyses to be performed by non-specialists, easily and intuitively over the web. METAGENassist is particularly notable for its rich graphical output and its extensive database of bacterial phenotypic information.
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.
Microbial dark matter (MDM) 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 analogous to the dark matter of physics and cosmology due to its elusiveness in research and importance to our understanding of biological diversity. Microbial dark matter can be found ubiquitously and abundantly across multiple ecosystems, but remains difficult to study due to difficulties in detecting and culturing these species, posing challenges to research efforts. 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. However, microbial dark matter research remains comparatively undeveloped and is hypothesized to provide insight into processes radically different from known biology, new understandings of microbial communities, and increasing understanding of how life survives in extreme environments.
Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome.
Virome refers to the assemblage of viruses that is often investigated and described by metagenomic sequencing of viral nucleic acids that are found associated with a particular ecosystem, organism or holobiont. The word is frequently used to describe environmental viral shotgun metagenomes. Viruses, including bacteriophages, are found in all environments, and studies of the virome have provided insights into nutrient cycling, development of immunity, and a major source of genes through lysogenic conversion. Also, the human virome has been characterized in nine organs of 31 Finnish individuals using qPCR and NGS methodologies.
Pharmacomicrobiomics, proposed by Prof. Marco Candela for the ERC-2009-StG project call, and publicly coined for the first time in 2010 by Rizkallah et al., is defined as the effect of microbiome variations on drug disposition, action, and toxicity. Pharmacomicrobiomics is concerned with the interaction between xenobiotics, or foreign compounds, and the gut microbiome. It is estimated that over 100 trillion prokaryotes representing more than 1000 species reside in the gut. Within the gut, microbes help modulate developmental, immunological and nutrition host functions. The aggregate genome of microbes extends the metabolic capabilities of humans, allowing them to capture nutrients from diverse sources. Namely, through the secretion of enzymes that assist in the metabolism of chemicals foreign to the body, modification of liver and intestinal enzymes, and modulation of the expression of human metabolic genes, microbes can significantly impact the ingestion of xenobiotics.
Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining.
Clinical metagenomic next-generation sequencing (mNGS) is the comprehensive analysis of microbial and host genetic material in clinical samples from patients by next-generation sequencing. It uses the techniques of metagenomics to identify and characterize the genome of bacteria, fungi, parasites, and viruses without the need for a prior knowledge of a specific pathogen directly from clinical specimens. The capacity to detect all the potential pathogens in a sample makes metagenomic next generation sequencing a potent tool in the diagnosis of infectious disease especially when other more directed assays, such as PCR, fail. Its limitations include clinical utility, laboratory validity, sense and sensitivity, cost and regulatory considerations.
References
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- ↑ Nee, S. (2004). "More than meets the eye". Nature. 429 (6994): 804–805. Bibcode:2004Natur.429..804N. doi:10.1038/429804a. PMID 15215837. S2CID 1699973.
- ↑ Frias-Lopez, Jorge; Yanmei Shi; Gene W. Tyson; Maureen L. Coleman; Stephan C. Schuster; Sallie W. Chisholm; band Edward F. DeLong (March 11, 2008). "Microbial community gene expression in ocean surface waters" (PDF). PNAS. 105 (10): 3805–3810. doi: 10.1073/pnas.0708897105 . PMC 2268829 . PMID 18316740 . Retrieved April 3, 2008.
- ↑ Poinar, Hendrik N.; Carsten Schwarz; Ji Qi; Beth Shapiro; Ross D. E. MacPhee; Bernard Buigues; Alexei Tikhonov; Daniel Huson; Lynn P. Tomsho; Alexander Auch; Markus Rampp; Webb Miller; Stephan C. Schuster (2007). "Metagenomics to Paleogenomics: Large-Scale Sequencing of Mammoth DNA". Science. 331 (6016): 392–394. doi:10.1126/science.331.6016.392. PMID 21273464 . Retrieved April 3, 2008.
- ↑ Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, Wu D, Paulsen I, Nelson KE, Nelson W, Fouts DE, Levy S, Knap AH, Lomas MW, Nealson K, White O, Peterson J, Hoffman J, Parsons R, Baden-Tillson H, Pfannkoch C, Rogers YH, Smith HO (April 2004). "Environmental Genome Shotgun Sequencing of the Sargasso Sea". Science. 304 (5667): 66–74. Bibcode:2004Sci...304...66V. CiteSeerX 10.1.1.124.1840 . doi:10.1126/science.1093857. PMID 15001713. S2CID 1454587.
- ↑ "MEGAN6 — Algorithms in Bioinformatics". uni-tuebingen.de. Retrieved December 21, 2020.Huson, Daniel H; S. Beier; I. Flade; A. Gorska; M. El-Hadidi; H. Ruscheweyh; R. Tappu (2016). "MEGAN Community Edition - Interactive exploration and analysis of large-scale microbiome sequencing data". PLOS Computational Biology. 12 (6): e1004957. Bibcode:2016PLSCB..12E4957H. doi: 10.1371/journal.pcbi.1004957 . PMC 4915700 . PMID 27327495.