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The branches of science known informally as omics are various disciplines in biology whose names end in the suffix -omics, such as genomics, proteomics, metabolomics, metagenomics, phenomics and transcriptomics. Omics aims at the collective characterization and quantification of pools of biological molecules that translate into the structure, function, and dynamics of an organism or organisms.[1]
The related suffix -ome is used to address the objects of study of such fields, such as the genome, proteome or metabolome respectively. The suffix -ome as used in molecular biology refers to a totality of some sort; it is an example of a "neo-suffix" formed by abstraction from various Greek terms in -ωμα, a sequence that does not form an identifiable suffix in Greek.
Functional genomics aims at identifying the functions of as many genes as possible of a given organism. It combines different -omics techniques such as transcriptomics and proteomics with saturated mutant collections.[2]
The Oxford English Dictionary (OED) distinguishes three different fields of application for the -ome suffix:
in medicine, forming nouns with the sense "swelling, tumour"
in botany or zoology, forming nouns in the sense "a part of an animal or plant with a specified structure"
in cellular and molecular biology, forming nouns with the sense "all constituents considered collectively"
The -ome suffix originated as a variant of -oma, and became productive in the last quarter of the 19th century. It originally appeared in terms like sclerome[3] or rhizome.[4] All of these terms derive from Greek words in -ωμα,[5] a sequence that is not a single suffix, but analyzable as -ω-μα, the -ω- belonging to the word stem (usually a verb) and the -μα being a genuine Greek suffix forming abstract nouns.
The OED suggests that its third definition originated as a back-formation from mitome,[6] Early attestations include biome (1916)[7] and genome (first coined as German Genom in 1920[8]).[9]
The association with chromosome in molecular biology is by false etymology. The word chromosome derives from the Greekstemsχρωμ(ατ)- "colour" and σωμ(ατ)- "body".[9] While σωμα "body" genuinely contains the -μα suffix, the preceding -ω- is not a stem-forming suffix but part of the word's root. Because genome refers to the complete genetic makeup of an organism, a neo-suffix -ome suggested itself as referring to "wholeness" or "completion".[10]
Many "omes" beyond the original "genome" have become useful and have been widely adopted by research scientists. "Proteomics" has become well-established as a term for studying proteins at a large scale. "Omes" can provide an easy shorthand to encapsulate a field; for example, an interactomics study is clearly recognisable as relating to large-scale analyses of gene-gene, protein-protein, or protein-ligand interactions. Researchers are rapidly taking up omes and omics, as shown by the explosion of the use of these terms in PubMed since the mid-1990s.[12]
Comparative genomics: Study of the relationship of genome structure and function across different biological species or strains.
Functional genomics: Describes gene and protein functions and interactions (often uses transcriptomics).
Metagenomics: Study of metagenomes, i.e., genetic material recovered directly from environmental samples.
Neurogenomics: Study of genetic influences on the development and function of the nervous system.
Pangenomics: Study of the entire collection of genes or genomes found within a given species.[13]
Personal genomics: Branch of genomics concerned with the sequencing and analysis of the genome of an individual. Once the genotypes are known, the individual's genotype can be compared with the published literature to determine likelihood of trait expression and disease risk. Helps in Personalized Medicine
Electromics: Branch of genomics concerned with the role of exogenous electric fields in potentiating the gene expression profiles of cells, tissues, and organoids.[14]
Epigenomics
The epigenome is the supporting structure of the genome, including protein and RNA binders, alternative DNA structures, and chemical modifications on DNA.
Epigenomics: Modern technologies include chromosome conformation by Hi-C, various ChIP-seq and other sequencing methods combined with proteomic fractionations, and sequencing methods that find chemical modification of cytosines, like bisulfite sequencing.
Nucleomics: Study of the complete set of genomic components which form "the cell nucleus as a complex, dynamic biological system, referred to as the nucleome".[15][16] The 4D Nucleome Consortium officially joined the IHEC (International Human Epigenome Consortium) in 2017.
Microbiomics
The microbiome is defined as a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The microbiome not only refers to the microorganisms involved but also encompass their theatre of activity, which results in the formation of specific ecological niches. The microbiome, which forms a dynamic and interactive micro-ecosystem prone to change in time and scale, is integrated in macro-ecosystems including eukaryotic hosts, and here crucial for their functioning and health.[17]
The lipidome is the entire complement of cellular lipids, including the modifications made to a particular set of lipids, produced by an organism or system.
Lipidomics: Large-scale study of pathways and networks of lipids. Mass spectrometry techniques are used.
Proteomics
The proteome is the entire complement of proteins, including the modifications made to a particular set of proteins, produced by an organism or system.
Proteomics: Large-scale study of proteins, particularly their structures and functions. Mass spectrometry techniques are used.
Chemoproteomics: An array of techniques used to study protein-small molecule interactions
Immunoproteomics: Study of large sets of proteins (proteomics) involved in the immune response
Nutriproteomics: Identifying the molecular targets of nutritive and non-nutritive components of the diet. Uses proteomics mass spectrometry data for protein expression studies
Proteogenomics: An emerging field of biological research at the intersection of proteomics and genomics. Proteomics data used for gene annotations.
Structural genomics: Study of the three-dimensional structure of every protein encoded by a given genome using a combination of experimental and modeling approaches.
Glycomics
Glycomics is the comprehensive study of the glycome i.e. sugars and carbohydrates.
Foodomics
Foodomics was defined by Alejandro Cifuentes in 2009 as "a discipline that studies the food and nutrition domains through the application and integration of advanced omics technologies to improve consumer’s well-being, health, and knowledge."[21][22]
Transcriptomics
Transcriptome is the set of all RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA, produced in one or a population of cells.
Transcriptomics: Study of transcriptomes, their structures and functions.
Metabolomics
The metabolome is the ensemble of small molecule found within a biological matrix.
Metabolomics: Scientific study of chemical processes involving metabolites. It is a "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles
Metabonomics: The quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification
Nutrition, pharmacology, and toxicology
Nutritional genomics: A science studying the relationship between human genome, nutrition and health.
Nutrigenetics studies the effect of genetic variations on the interaction between diet and health with implications to susceptible subgroups
Nutrigenomics: Study of the effects of foods and food constituents on gene expression. Studies the effect of nutrients on the genome, proteome, and metabolome
Pharmacogenomics investigates the effect of the sum of variations within the human genome on drugs;
Pharmacomicrobiomics investigates the effect of variations within the human microbiome on drugs and vice versa.
Toxicogenomics: a field of science that deals with the collection, interpretation, and storage of information about gene and protein activity within particular cell or tissue of an organism in response to toxic substances.
Culture
Inspired by foundational questions in evolutionary biology, a Harvard team around Jean-Baptiste Michel and Erez Lieberman Aiden created the American neologism culturomics for the application of big data collection and analysis to cultural studies.[23]
Psychogenomics: Process of applying the powerful tools of genomics and proteomics to achieve a better understanding of the biological substrates of normal behavior and of diseases of the brain that manifest themselves as behavioral abnormalities. Applying psychogenomics to the study of drug addiction, the ultimate goal is to develop more effective treatments for these disorders as well as objective diagnostic tools, preventive measures, and eventually cures.
Stem cell genomics: Helps in stem cell biology. Aim is to establish stem cells as a leading model system for understanding human biology and disease states and ultimately to accelerate progress toward clinical translation.
Connectomics: The study of the connectome, the totality of the neural connections in the brain.
Microbiomics: The study of the genomes of the communities of microorganisms that live in a specific environmental niche.
Cellomics: The quantitative cell analysis and study using bioimaging methods and bioinformatics.
Tomomics: A combination of tomography and omics methods to understand tissue or cell biochemistry at high spatial resolution, typically using imaging mass spectrometry data.[24]
Ethomics: The high-throughput machine measurement of animal behaviour.[25]
Videomics (or vide-omics): A video analysis paradigm inspired by genomics principles, where a continuous image sequence (or video) can be interpreted as the capture of a single image evolving through time through mutations revealing 'a scene'.
Multiomics: Integration of different omics in a single study or analysis pipeline.[26]
Unrelated words in -omics
The word "comic" does not use the "omics" suffix; it derives from Greek "κωμ(ο)-" (merriment) + "-ικ(ο)-" (an adjectival suffix), rather than presenting a truncation of "σωμ(ατ)-".
Similarly, the word "economy" is assembled from Greek "οικ(ο)-" (household) + "νομ(ο)-" (law or custom), and "economic(s)" from "οικ(ο)-" + "νομ(ο)-" + "-ικ(ο)-". The suffix -omics is sometimes used to create names for schools of economics, such as Reaganomics.
↑ Hans Winkler (1920). Verbreitung und Ursache der Parthenogenesis im Pflanzen – und Tierreiche. Verlag Fischer, Jena. p.165. Ich schlage vor, für den haploiden Chromosomensatz, der im Verein mit dem zugehörigen Protoplasma die materielle Grundlage der systematischen Einheit darstellt den Ausdruck: das Genom zu verwenden ... " In English: " I propose the expression Genom for the haploid chromosome set, which, together with the pertinent protoplasm, specifies the material foundations of the species ...
Genomics is an interdisciplinary field of 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.
Proteomics is the large-scale study of proteins. Proteins are vital parts of living organisms, with many functions such as the formation of structural fibers of muscle tissue, enzymatic digestion of food, or synthesis and replication of DNA. In addition, other kinds of proteins include antibodies that protect an organism from infection, and hormones that send important signals throughout the body.
Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic approach to biological research.
Functional genomics is a field of molecular biology that attempts to describe gene functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects. Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional "candidate-gene" approach.
The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.
Regulome refers to the whole set of regulatory components in a cell. Those components can be regulatory elements, genes, mRNAs, proteins, and metabolites. The description includes the interplay of regulatory effects between these components, and their dependence on variables such as subcellular localization, tissue, developmental stage, and pathological state.
Proteogenomics is a field of biological research that utilizes a combination of proteomics, genomics, and transcriptomics to aid in the discovery and identification of peptides. Proteogenomics is used to identify new peptides by comparing MS/MS spectra against a protein database that has been derived from genomic and transcriptomic information. Proteogenomics often refers to studies that use proteomic information, often derived from mass spectrometry, to improve gene annotations. The utilization of both proteomics and genomics data alongside advances in the availability and power of spectrographic and chromatographic technology led to the emergence of proteogenomics as its own field in 2004.
The Multi-Omics Profiling Expression Database (MOPED) was an expanding multi-omics resource that supports rapid browsing of transcriptomics and proteomics information from publicly available studies on model organisms and humans. As to date (2021) it has ceased activities and is unaccessible online.
In the field of cellular biology, single-cell analysis and subcellular analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.
Foodomics was defined in 2009 as "a discipline that studies the Food and Nutrition domains through the application and integration of advanced -omics technologies to improve consumer's well-being, health, and knowledge". Foodomics requires the combination of food chemistry, biological sciences, and data analysis.
Pan-cancer analysis aims to examine the similarities and differences among the genomic and cellular alterations found across diverse tumor types. International efforts have performed pan-cancer analysis on exomes and the whole genomes of cancers, the latter including their non-coding regions. In 2018, The Cancer Genome Atlas (TCGA) Research Network used exome, transcriptome, and DNA methylome data to develop an integrated picture of commonalities, differences, and emergent themes across tumor types.
Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.
Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome.
Multiomics, multi-omics, integrative omics, "panomics" or "pan-omics" is a biological analysis approach in which the data sets are multiple "omes", such as the genome, proteome, transcriptome, epigenome, metabolome, and microbiome ; in other words, the use of multiple omics technologies to study life in a concerted way. By combining these "omes", scientists can analyze complex biological big data to find novel associations between biological entities, pinpoint relevant biomarkers and build elaborate markers of disease and physiology. In doing so, multiomics integrates diverse omics data to find a coherently matching geno-pheno-envirotype relationship or association. The OmicTools service lists more than 99 softwares related to multiomic data analysis, as well as more than 99 databases on the topic.
Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant. A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.
CITE-Seq is a method for performing RNA sequencing along with gaining quantitative and qualitative information on surface proteins with available antibodies on a single cell level. So far, the method has been demonstrated to work with only a few proteins per cell. As such, it provides an additional layer of information for the same cell by combining both proteomics and transcriptomics data. For phenotyping, this method has been shown to be as accurate as flow cytometry by the groups that developed it. It is currently one of the main methods, along with REAP-Seq, to evaluate both gene expression and protein levels simultaneously in different species.
Translatomics is the study of all open reading frames (ORFs) that are being actively translated in a cell or organism. This collection of ORFs is called the translatome. Characterizing a cell's translatome can give insight into the array of biological pathways that are active in the cell. According to the central dogma of molecular biology, the DNA in a cell is transcribed to produce RNA, which is then translated to produce a protein. Thousands of proteins are encoded in an organism's genome, and the proteins present in a cell cooperatively carry out many functions to support the life of the cell. Under various conditions, such as during stress or specific timepoints in development, the cell may require different biological pathways to be active, and therefore require a different collection of proteins. Depending on intrinsic and environmental conditions, the collection of proteins being made at one time varies. Translatomic techniques can be used to take a "snapshot" of this collection of actively translating ORFs, which can give information about which biological pathways the cell is activating under the present conditions.
Deterministic Barcoding in Tissue for Spatial Omics Sequencing (DBiT-seq) was developed at Yale University by Rong Fan and colleagues in 2020 to create a multi-omics approach for studying spatial gene expression heterogenicity within a tissue sample. This method can be used for the co-mapping mRNA and protein levels at a near single-cell resolution in fresh or frozen formaldehyde-fixed tissue samples. DBiT-seq utilizes next generation sequencing (NGS) and microfluidics. This method allows for simultaneous spatial transcriptomic and proteomic analysis of a tissue sample. DBiT-seq improves upon previous spatial transcriptomics applications such as High-Definition Spatial Transcriptomics (HDST) and Slide-seq by increasing the number of detectable genes per pixel, increased cellular resolution, and ease of implementation.
Precision diagnostics is a branch of precision medicine that involves precisely managing a patient's healthcare model and diagnosing specific diseases based on customized omics data analytics.
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