Proteomics is the large-scale study of proteins. Proteins are vital macromolecules of all 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.
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.
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.
Biomarker discovery is a medical term describing the process by which biomarkers are discovered. Many commonly used blood tests in medicine are biomarkers. There is interest in biomarker discovery on the part of the pharmaceutical industry; blood-test or other biomarkers could serve as intermediate markers of disease in clinical trials, and as possible drug targets.
This page provides an alphabetical list of articles and other pages about biotechnology.
An RNA spike-in is an RNA transcript of known sequence and quantity used to calibrate measurements in RNA hybridization assays, such as DNA microarray experiments, RT-qPCR, and RNA-Seq.
CLC bio was a bioinformatics software company that developed a software suite subsequently purchased by QIAGEN.
GeneCards is a database of human genes that provides genomic, proteomic, transcriptomic, genetic and functional information on all known and predicted human genes. It is being developed and maintained by the Crown Human Genome Center at the Weizmann Institute of Science, in collaboration with LifeMap Sciences.
QIAGEN Silicon Valley is a company based in Redwood City, California, USA, that develops software to analyze complex biological systems. QIAGEN Silicon Valley's first product, IPA, was introduced in 2003, and is used to help researchers analyze omics data and model biological systems. The software has been cited in thousands of scientific molecular biology publications and is one of several tools for systems biology researchers and bioinformaticians in drug discovery and institutional research.
AstridBio Ltd. is a privately held biotechnology company based in Szeged, Hungary. Started in 2003, AstridBio's focus is in biobanking software development, data management and analysis for genomics research. Its clients include academic research institutes, pharmaceutical and biotech companies.
Extracellular RNA (exRNA) describes RNA species present outside of the cells in which they were transcribed. Carried within extracellular vesicles, lipoproteins, and protein complexes, exRNAs are protected from ubiquitous RNA-degrading enzymes. exRNAs may be found in the environment or, in multicellular organisms, within the tissues or biological fluids such as venous blood, saliva, breast milk, urine, semen, menstrual blood, and vaginal fluid. Although their biological function is not fully understood, exRNAs have been proposed to play a role in a variety of biological processes including syntrophy, intercellular communication, and cell regulation. The United States National Institutes of Health (NIH) published in 2012 a set of Requests for Applications (RFAs) for investigating extracellular RNA biology. Funded by the NIH Common Fund, the resulting program was collectively known as the Extracellular RNA Communication Consortium (ERCC). The ERCC was renewed for a second phase in 2019.
The Expression Atlas is a database maintained by the European Bioinformatics Institute that provides information on gene expression patterns from RNA-Seq and Microarray studies, and protein expression from Proteomics studies. The Expression Atlas allows searches by gene, splice variant, protein attribute, disease, treatment or organism part. Individual genes or gene sets can be searched for. All datasets in Expression Atlas have its metadata manually curated and its data analysed through standardised analysis pipelines. There are two components to the Expression Atlas, the Baseline Atlas and the Differential Atlas:
Pathway is the term from molecular biology for a curated schematic representation of a well characterized segment of the molecular physiological machinery, such as a metabolic pathway describing an enzymatic process within a cell or tissue or a signaling pathway model representing a regulatory process that might, in its turn, enable a metabolic or another regulatory process downstream. A typical pathway model starts with an extracellular signaling molecule that activates a specific receptor, thus triggering a chain of molecular interactions. A pathway is most often represented as a relatively small graph with gene, protein, and/or small molecule nodes connected by edges of known functional relations. While a simpler pathway might appear as a chain, complex pathway topologies with loops and alternative routes are much more common. Computational analyses employ special formats of pathway representation. In the simplest form, however, a pathway might be represented as a list of member molecules with order and relations unspecified. Such a representation, generally called Functional Gene Set (FGS), can also refer to other functionally characterised groups such as protein families, Gene Ontology (GO) and Disease Ontology (DO) terms etc. In bioinformatics, methods of pathway analysis might be used to identify key genes/ proteins within a previously known pathway in relation to a particular experiment / pathological condition or building a pathway de novo from proteins that have been identified as key affected elements. By examining changes in e.g. gene expression in a pathway, its biological activity can be explored. However most frequently, pathway analysis refers to a method of initial characterization and interpretation of an experimental condition that was studied with omics tools or genome-wide association study. Such studies might identify long lists of altered genes. A visual inspection is then challenging and the information is hard to summarize, since the altered genes map to a broad range of pathways, processes, and molecular functions. In such situations, the most productive way of exploring the list is to identify enrichment of specific FGSs in it. The general approach of enrichment analyses is to identify FGSs, members of which were most frequently or most strongly altered in the given condition, in comparison to a gene set sampled by chance. In other words, enrichment can map canonical prior knowledge structured in the form of FGSs to the condition represented by altered genes.
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.
In molecular biology, a batch effect occurs when non-biological factors in an experiment cause changes in the data produced by the experiment. Such effects can lead to inaccurate conclusions when their causes are correlated with one or more outcomes of interest in an experiment. They are common in many types of high-throughput sequencing experiments, including those using microarrays, mass spectrometers, and single-cell RNA-sequencing data. They are most commonly discussed in the context of genomics and high-throughput sequencing research, but they exist in other fields of science as well.
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.
Precision diagnostics is a branch of precision medicine that involves managing a patient's healthcare model and diagnosing specific diseases based on omics data analytics.
