IntAct [ 87 ] and Biogrid [ 88 ] are libraries where PPIs are manually annotated from peer-reviewed literature [ 89 ]. In some cases, these integrate manual curation with algorithms to predict denovo PPIs and text mining to automatically extract PPIs together with functional interactions from the literature e.
PPIs are used to build networks: within a network, each protein is defined as a node, and the connection between nodes is defined by an experimentally observed physical interaction. PPI networks provide information on the function of important protein s based on the guilt-by-association principle, i. PPI networks can be built manually [ 96 ], allowing the merging of PPI data obtained from different sources: this approach is time-consuming, but allows the handling of the raw PPIs through custom filters and to create multi-layered networks.
Some web resources e. Human Integrated Protein—Protein Interaction rEference [ 97 ] allow the generation of automated PPI networks starting from a protein or a list of proteins i. These various platforms differ by their source of PPIs, rules for governing the merging and scoring pipelines. Finally, certain servers integrate PPIs with additional types of data including predicted interactions and co-expression data, generating hybrid networks e.
GeneMania [ 98 ]. Taken all together, if on one hand these multiple resources are user friendly, on the other they are not harmonized and poorly customizable leading to inconsistent results among each other. Therefore, users should thoroughly familiarize themselves with the parameters of the software, to properly extract and interpret data. SwissProt or RefSeq. To avoid redundancy, reduce the range of different identifiers protein IDs and harmonize the annotation efforts, multiple databases were merged.
We have summarized critical considerations in Table 4 , and all web resources included in this section are shown in Supplementary Table S1c. Table 4. General critical considerations on applying bioinformatics to proteomics. Changes in the gene sequence and experimental protein sequencing confirmation will result in updates to the protein sequence in protein databases. Different bioinformatics tools are updated to different versions of the protein sequence databases.
Functional annotation is an analytical technique commonly applied to different types of big data e. This type of analysis, which is currently gaining notable interest and relevance, relies on the existence of manually curated libraries that annotate and classify genes and proteins on the basis of their function, as reported in the literature [ ].
The most renowned and comprehensive is the Gene Ontology GO library that provides terms i. Other libraries provide alternative types of annotation, including pathway annotation such as the Kyoto Encyclopedia of Genes and Genomes [ ], Reactome [ ] and Pathway Commons [ ]. Conversely, regulatory annotation can be found, for example, in TRANScription FACtor [ ], a library where genes are catalogued based on the transcription factors they are regulated by the version is freely available; any subsequent version is accessible upon fee.
Functional annotation is based on a statistical assessment called enrichment. The latter will show a certain distribution of GO terms, reflecting the frequency of association between the catalogued BPs, MFs and CCs, and the genes in the entire genome. Conversely, the sample set is a list of genes of interest grouped together based on experimental data.
The enrichment analysis compares the distribution of GO terms in the sample set list of genes of interest versus that observed in the reference set genome : if a certain GO term is more frequent in the sample set than in the reference set, it is enriched, indicating functional specificity. Of note, the reference set should be tailored to the specific analysis e. Scheme of a typical functional enrichment analysis.
A sample and reference set are compared to highlight the most frequent i. There is a wide variety of online portals that aid performing functional enrichment [ ] e. Each of these portals downloads groups of GO terms in its virtual space from GO and it is critical for the end user to verify the frequency at which portals perform updates.
It is also important to note that any portal might be used for initial analysis; however, one should keep in mind that using the most updated portal as well as replicating analyses with a minimum of three different analytical tools is probably best practice in assessments of this kind.
We have summarized critical considerations in Table 5 , and all web resources included in this section are shown in Supplementary Table S1d. Table 5. General critical considerations on applying bioinformatics to functional annotation analyses. Use a minimum of three different portals to replicate and validate functional annotations.
GO terms are related through family trees: general terms are umbrella terms located at the top of the tree. More specific terms are found gradually moving down towards the roots. General terms are overrepresented among the results of functional enrichment. In addition to genomics, transcriptomics and proteinomics, other areas of biomedical science are moving towards the omics scale, albeit not yet achieving the same level of complexity, depth and resolution.
There are macromolecules that bind and functionally affect the metabolism of the DNA e. ENCODE collects results of experiments conducted to identify signature patterns, such as DNA methylation, histone modification and binding to transcription factors, suppressors and polymerases.
Since signature patterns differ between cells and tissues, data are generated and collected based on cell type [ ]. Not only does ENCODE play a major role in increasing our general knowledge of the physiology and metabolism of DNA, but it also promises to provide insight into health and disease, by aiding the integration and interpretation of genomics and transcriptomics data.
Omics collections are also curated for drugs. There are databases and meta-databases e. These are useful to find existing drugs for a specific target e. An additional database, part of the so-called ConnectivityMap project, provides an interface to browse a collection of genome-wide transcriptional profiles from cell cultures treated with small bioactive molecules i. This resource is used as a high-throughput approach to evaluate modulation of gene expression influenced by certain drugs.
Another emerging omics effort is metabolomics, the study of metabolites produced during biochemical reactions. Metabolomic databases such as the human metabolome database [ ], METLIN [ ] and MetaboLights [ ] collect information on metabolites identified in biological samples through chromatography, NMR and MS paired with associated metadata. Of note, efforts such as the Metabolomics Standard Initiative [ ] and the COordination of Standards in MetabolOmicS within the Framework Programme 7 EU Initiative [ ] are currently addressing the problem of standardization of metabolomics data.
Therefore, they are measured in cases and controls to develop accurate diagnostics and understand relevant molecular pathways underpinning specific conditions or traits [ ]. Some critical limitations apply to this field currently, including i the need for improvement of analytical techniques to both detect metabolites and processing results, ii the ongoing production of reference and population-specific metabolomes and iii the fact that we still do not completely understand the biological role of all detectable metabolites [ , ].
Nevertheless, some promising studies have emerged: for example, profiling of lipids in plasma samples of Mexican Americans identified specific lipidic species correlated with the risk of hypertension [ ]; or else, serum profiling of ovarian cancer was used to implement a support diagnostics to accurately detect early stages of the disease [ ]. The rise of a high number of bioinformatics tools has fostered initiatives aimed at generating portals to list them and support their effective use.
For example, EBI has a bioinformatics service portal listing a variety of databases and tools tailored for specific quests or topics [ ]; Bioconductor provides analysis tools and ad hoc scripts developed by statisticians for a variety of analyses and bioinformatics solutions; GitHUB is a free repository, easing collaboration and sharing of tools and informatics functions; OMICtools is a library of software, databases and platforms for big-data processing and analysis; Expert Protein Analysis System is a library particularly renowned for proteomics tools.
This flourishing of analytic tools and software is remarkable, and increases the speed at which data can be processed and analysed. However, with this abundance of possibilities, caution is warranted, as no single tool is comprehensive and none is infallible. All web resources included in this section are shown in Supplementary Table S1e.
Advances in biomedical sciences over the past century have lent phenomenal contributions to our understanding of the human condition, providing an explanation of the causes, or even curing, a number of diseases—especially when monogenic e. Nevertheless, two major challenges remain unresolved in complex disorders, i. Regardless of the improvements in the efficiency of data generation, the research community still struggles when stepping into the translational processes.
Genomics, transcriptomics and proteinomics are still mainly separate fields that generate a monothematic type of knowledge.
Nevertheless, we are witnessing the rise of inter-disciplinary data integration strategies to be applied to the study of multifactorial disorders [ ]: the genome, transcriptome and proteome are, in fact, not isolated biological entities, and multi omics data should be concomitantly used and integrated to map risk pathways to disease Figure 6. Overview on a global approach for the study of health and disease. Ideally, for individual samples, comprehensive metadata 0 should be recorded.
To date, 1 , 2 and 3 are being studied mainly as compartmentalized fields. A strategy to start integrating these fields currently relies on functional annotation analyses 4 that provide a valuable platform to start shedding light on disease or risk pathways 5. The influence of other elements such as epigenomics, pharmacogenomics, metabolomics and environmental factors on traits is important to have a better and more comprehensive understanding of their pathobiology.
The assessment and integration of all such data will allow for the true development of successful personalized medicine 6. The gradually darker shades of green and increased font sizes indicate the expected gradual increase in the translational power of global data integration. Integration is defined as the process through which different kinds of omics data— multi omics, including mutations defined through genomics, mRNA levels through transcriptomics, protein abundance and type through proteomics, and also methylation profiles through epigenomics, metabolite levels through metabolomics, metadata such as clinical outcomes, histological profiles and series of digital imaging assays and many others—are combined to create a global picture with higher informative power comparatively to the single isolated omics [ ].
One of the fields at the forefront for omics data integration is cancer biology where the integrative approach is already translated to the bedside: here, implementation of data integration allowed, for example, tumour classification and subsequently prediction of aggressiveness and outcome, thus supporting the selection of personalized therapies [ ]. The ColoRectal Cancer Subtyping Consortium applied data integration to a large scale, internationally collected sets of multi omics data transcriptomics, genomics, methylation, microRNA and proteomics —to classify the subtypes of colorectal cancer in biologically relevant groups—that were applied to support therapeutic decisions and predict patient outcomes [ ].
In the former case, integration of DNA and RNA data has led to an improvement in matching genetic variations with their immediate effect, e. Sometimes individual research groups set up custom pipelines to achieve data integration. For example, early attempts to couple microRNA and metabolome profiles in a tumour cell line led to the isolation of specific microRNA s acting as modifier s of cancer-associated genes [ ].
Such endeavours rely on the availability of multidisciplinary experts within individual research groups and sufficient computational infrastructure supporting data storage and analysis. Having such teams allows the development of customized pipelines tailored to the specific needs; however, their efforts are not necessarily available to the wider scientific community unless shared through ad hoc repositories e. Emergence of scalable cloud computing platforms Google Cloud, Amazon Web Services, Microsoft Azure makes data storage and processing more affordable to teams that do not have sufficient in-house computing infrastructure, although such platforms require special investment.
There are also public efforts leading to the inception of a number of promising initiatives: BioSample BioSD is a promising tool for performing weighted harmonization among multi omics.
Here, experiments and data sets stored within EBI databases can be queried to simultaneously access multiple types of data from the same sample, clearly representing a valuable means of simplifying data integration [ ].
GeneAnalytics is a platform for querying genes against a number of curated repositories to gather knowledge about their associations with tissues, cells, diseases, pathways, GO, phenotypes, drugs and compounds [ ]. This is, however, only available upon a subscription fee. The picture is still incomplete without additional integration of other omics such as epigenomics and metabolomics: although platforms to allow integration of epigenetic with transcriptomic data e.
BioWardrobe [ ] are being developed, endeavours to support data optimization and sharing are welcomed. For example, the European Open Science Cloud promoted and supported by the European Commission represents a data repository where, through the support of expert stewards, data are standardized and stored to foster collaborative data-sharing across disciplines [ ]. There are still significant biological and technical challenges impacting data integration leading to difficulties in overlapping or merging data sets and the chance to overlook potential interesting results.
These limitations include the following: i inefficient and inconsistent nomenclatures across different databases or sources e. Also, currently, sampling material for an experiment limits the study of the biochemical life of the cell to a single snapshot exclusively accounting for the moment and condition at sampling.
To start addressing issues like this, it would be ideal, in the near future, to develop tools to visualize the dynamics of CCs in 3D and include temporospatial variables that influence the behaviour of intracellular phenomena. Moreover, techniques are still in development to analyse the phenotype of cells in a high-throughput fashion, correlating changes in the genome, gene expression and the proteome to cellular phenotypes, i.
Another unsolved problem is that of merging data generated through different omics as it is not straightforward and requires refining steps to elaborate the data sets before integration. For example, it has been demonstrated that the transcriptome does not completely mirror the proteome of a cell [ ].
Therefore, to integrate the information coming from the transcriptome and the proteome specific to a cellular phase or BP e. Finally, a major challenge to fully complete the picture is represented by environmental factors that, although recognized for critically influencing all levels of omics, still cannot be investigated through robust and reliable methods [ ].
In an attempt to overcome this important issue, statisticians and epidemiologists are developing new approaches, such as Mendelian randomization through which genetic markers are used as decoys for environmental factors to be studied in association with traits or diseases [ ]. There are still a number of issues associated with data generation and sharing, and three main levels of scrutiny apply here.
Second, raw data represent a private type of data, making it an absolute requirement to anonymize the samples through de-identification codes along with associated metadata e.
To take genetics as an example, methods to share data might be differently regulated based on the type of data being shared: it is widely accepted to share summary statistics of a data set for further meta-analyses where, for example, allele frequency data are not released to prevent identification of individuals ; more difficult is the sharing of data sets containing individual-level raw data, as consent and approval to do so should be covered by the original IRB.
Then there is a third layer of complexity in data management that merits discussion. However, since the latter topic goes beyond the goals of the current review and discussion, we suggest the following reference for more details [ ]. In summary, there is clearly enormous potential in the integration and use of multi omics data for a better understanding of the molecular mechanisms, processes and pathways discriminating health and disease.
The success of this new model of science will depend on the gradual shift from a reductionist to a global approach, sustained by a lively and proactive flow of data across and between different fields of expertise, and funding programmes promoting and supporting this endeavour [ ]. It is reassuring that governments are starting to acknowledge the importance of translating this comprehensive biomedical knowledge to the bedside and thus fostering the implementation of plans supporting the logistics and regulatory actions for such transformation to take place.
Together, this will eventually aid the development of measures for disease prevention, early diagnosis, disease monitoring and treatment, thus making precision medicine a forthcoming possibility. Key Points We present an overview on the basics and exponential growth of genomics, transcriptomics and proteinomics. We summarize the principal bioinformatics and biostatistics tools for omics analysis.
Genetics, functional biology, bioinformatics and biostatistics established specific jargons, impacting communication and data interpretation. We particularly aim at targeting a broad range of scientific professionals including students seeking knowledge outside their field of expertise.
We provide a critical view of strengths and weaknesses of these omic approaches. Claudia Manzoni is a postdoc at the University of Reading whose expertise is functional biology; she has developed an interest in systems biology and bioinformatics as tool to support, guide and improve cell biology research.
Demis A. Jana Vandrovcova is a postdoc at the University College London with experience in genetics, transcriptomics and overall bioinformatics. Her research activities are currently focusing on the application of genetics and transcriptomics to understand pathogenesis of neurodegenerative conditions.
Nicholas W. Patrick A. His research focus is the understanding of the molecular pathways underpinning Parkinson's Disease, performed through a combination of functional and systems biology. Raffaele Ferrari is a geneticist at the University College London with an interest in bioinformatics and systems biology. The main goal of his research is the dissection of the genetic underpinnings of Dementias with particular interest in Frontotemporal Dementia; he conducted the largest genome-wide association study for Frontotemporal Dementia so far and manages the International FTD-Genomics Consortium IFGC.
The authors would like to acknowledge generous research support from the Michael J. RNA codewords and protein synthesis. Science ; : — Google Scholar. International HapMap Consortium.
The International HapMap project. Nature ; : — International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome.
Protein D. Searls DB. The roots of bioinformatics. PLoS Comput Biol ; 6 : e Turing AM. The chemical basis of morphogenesis. Philos Trans R Soc Lond ; : 37 — Von Bertalanffy L.
An outline of general system theory. Br J Philos Sci ; 1 : — Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid. Nature ; : — 8. Clinical cancer advances annual report on progress against cancer from the American Society of Clinical Oncology.
J Clin Oncol ; 30 : 88 — Topological sensitivity analysis for systems biology. Genome Res ; 22 : — The sequence of the human genome. Clin Chem ; 61 : — 8. A HapMap harvest of insights into the genetics of common disease. J Clin Invest ; : — Williams TN.
Human red blood cell polymorphisms and malaria. Curr Opin Microbiol ; 9 : — Finding the missing heritability of complex diseases. Human genome sequencing in health and disease. Annu Rev Med ; 63 : 35 — Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet ; 11 : 31 — Guidelines for human gene nomenclature. Genomics ; 79 : — The vertebrate genome annotation browser 10 years on. Nucleic Acids Res ; 42 : D — 9.
Horaitis O , Cotton RG. The challenge of documenting mutation across the genome: the human genome variation society approach. Hum Mutat ; 23 : — DNA sequencing with chain-terminating inhibitors. Bumgarner R. Overview of DNA microarrays: types, applications, and their future.
Ten years of next-generation sequencing technology. Trends Genet ; 30 : — How to interpret a genome-wide association study.
JAMA ; : — Nucleic Acids Res ; 44 : D —7 Schneider V. Pop G. The UK10K project identifies rare variants in health and disease. Nature ; : 82 — Marx V. The DNA of a nation. Nature ; : — 5. Collins FS , Varmus H. A new initiative on precision medicine. Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson's disease.
Nat Genet ; 46 : — A QTL influencing F cell production maps to a gene encoding a zinc-finger protein on chromosome 2p Nat Genet ; 39 : — 9. Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of beta-thalassemia. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. An atlas of genetic correlations across human diseases and traits. Nat Genet ; 47 : — Missing heritability and strategies for finding the underlying causes of complex disease.
Nat Rev Genet ; 11 : — Common SNPs explain a large proportion of the heritability for human height. Nat Genet ; 42 : — 9. Large-scale whole-genome sequencing of the Icelandic population. Use of linkage analysis, genome-wide association studies, and next-generation sequencing in the identification of disease-causing mutations. Methods Mol Biol ; : — A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing.
Front Genet ; 6 : PLINK: a tool set for whole-genome association and population-based linkage analyses. Thus, the genome is constant, but the proteome varies and is dynamic within an organism.
In addition, RNAs can be alternately spliced cut and pasted to create novel combinations and novel proteins and many proteins modify themselves after translation by processes such as proteolytic cleavage, phosphorylation, glycosylation, and ubiquitination.
There are also protein-protein interactions, which complicate studying proteomes. Although the genome provides a blueprint, the final architecture depends on several factors that can change the progression of events that generate the proteome. Metabolomics is related to genomics and proteomics. Metabolomics involves studying small molecule metabolites in an organism. Metabolomics offers an opportunity to compare genetic makeup and physical characteristics, as well as genetic makeup and environmental factors.
The ultimate goal of proteomics is to identify or compare the proteins expressed from a given genome under specific conditions, study the interactions between the proteins, and use the information to predict cell behavior or develop drug targets.
Just as scientists analyze the genome using the basic DNA sequencing technique, proteomics requires techniques for protein analysis. The basic technique for protein analysis, analogous to DNA sequencing, is mass spectrometry. Advances in spectrometry have allowed researchers to analyze very small protein samples. Scientists have also used protein microarrays to study protein interactions.
Large-scale adaptations of the basic two-hybrid screen Figure have provided the basis for protein microarrays. Scientists use computer software to analyze the vast amount of data for proteomic analysis. Genomic- and proteomic-scale analyses are part of systems biology , which is the study of whole biological systems genomes and proteomes based on interactions within the system. The European Bioinformatics Institute and the Human Proteome Organization HUPO are developing and establishing effective tools to sort through the enormous pile of systems biology data.
Because proteins are the direct products of genes and reflect activity at the genomic level, it is natural to use proteomes to compare the protein profiles of different cells to identify proteins and genes involved in disease processes.
Most pharmaceutical drug trials target proteins. Researchers use information that they obtain from proteomics to identify novel drugs and to understand their mechanisms of action. Scientists are challenged when implementing proteomic analysis because it is difficult to detect small protein quantities.
Although mass spectrometry is good for detecting small protein amounts, variations in protein expression in diseased states can be difficult to discern. Proteins are naturally unstable molecules, which makes proteomic analysis much more difficult than genomic analysis. The most prominent disease researchers are studying with proteomic approaches is cancer. The traditional method is yeast two-hybrid analysis.
New methods include protein microarrays, immunoaffinity, and chromatography followed by mass spectrometry, dual polarisation interferometry, Microscale Thermophoresis, and experimental methods such as phage display and computational methods.
It allows for the analysis of biomolecules and large organic molecules which tend to be fragile and fragment when ionized by more conventional ionization methods. One of the most promising developments to come from the study of human genes and proteins has been the identification of potential new drugs for the treatment of disease.
This relies on genome and proteome information to identify proteins associated with a disease, which computer software can then use as targets for new drugs. For example, if a certain protein is implicated in a disease, its 3-D structure provides the information to design drugs to interfere with the action of the protein. A molecule that fits the active site of an enzyme, but cannot be released by the enzyme, will inactivate the enzyme. Understanding the proteome, the structure and function of each protein and the complexities of protein—protein interactions will be critical for developing the most effective diagnostic techniques and disease treatments in the future.
Moreover, an interesting use of proteomics is using specific protein biomarkers to diagnose disease. A number of techniques allow testing for proteins produced during a particular disease, which helps to diagnose the disease quickly.
Metabolomics is the scientific study of chemical processes involving metabolites. The metabolome represents the collection of all metabolites, which are the end products of cellular processes, in a biological cell, tissue, organ, or organism.
Thus, while mRNA gene expression data and proteomic analyses do not tell the whole story of what might be happening in a cell, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology and functional genomics is to integrate proteomic, transcriptomic, and metabolomic information to give a more complete picture of living organisms.
The idea that biological fluids reflect the health of an individual has existed for a long time. GC-MS is a method that combines the features of gas-liquid chromatography and mass spectrometry to identify different substances within a test sample. Concurrently, NMR spectroscopy, which was discovered in the s, was also undergoing rapid advances.
In , Seeley et al. As sensitivity has improved with the evolution of higher magnetic field strengths and magic-angle spinning, NMR continues to be a leading analytical tool to investigate metabolism.
Gas Chromatography—mass spectrometry : Gas chromatography—mass spectrometry GC-MS is a method that combines the features of gas-liquid chromatography and mass spectrometry to identify different substances within a test sample.
David Wishart of the University of Alberta, Canada, completed the first draft of the human metabolome, consisting of a database of approximately metabolites, drugs and food components. The word was coined in analogy with transcriptomics and proteomics.
Like the transcriptome and the proteome, the metabolome is dynamic, changing from second to second. Although the metabolome can be defined readily enough, it is not currently possible to analyse the entire range of metabolites by a single analytical method. Metabolites are the intermediates and products of metabolism. Within the context of metabolomics, a metabolite is usually defined as any molecule less than 1 kDa in size.
However, there are exceptions to this, depending on the sample and detection method. Macromolecules such as lipoproteins and albumin are reliably detected in NMR-based metabolomics studies of blood plasma. In contrast, in human-based metabolomics it is more common to describe metabolites as being either endogenous produced by the host organism or exogenous.
The metabolome forms a large network of metabolic reactions, where outputs from one enzymatic chemical reaction are inputs to other chemical reactions. Such systems have been described as hypercycles. Separation methods: Gas chromatography, especially when interfaced with mass spectrometry GC-MS , is one of the most widely used and powerful methods.
It offers very high chromatographic resolution, but requires chemical derivatization for many biomolecules: only volatile chemicals can be analysed without derivatization.
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