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PubMed2003

PANDORA: keyword-based analysis of protein sets by integration of annotation sources.

Recent advances in high-throughput methods and the application of computational tools for automatic classification of proteins have made it possible to carry out large-scale proteomic analyses. Biological analysis and interpretation of sets of proteins is a time-consuming undertaking carried out manually by experts. We have developed PANDORA (Protein ANnotation Diagram ORiented Analysis), a web-based tool that provides an automatic representation of the biological knowledge associated with any set of proteins. PANDORA uses a unique approach of keyword-based graphical analysis that focuses on detecting subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA currently supports SwissProt keywords, NCBI Taxonomy, InterPro entries and the hierarchical classification terms from ENZYME, SCOP and GO databases. The integrated study of several annotation sources simultaneously allows a representation of biological relations of structure, function, cellular location, taxonomy, domains and motifs. PANDORA is also integrated into the ProtoNet system, thus allowing testing thousands of automatically generated clusters. We illustrate how PANDORA enhances the biological understanding of large, non-uniform sets of proteins originating from experimental and computational sources, without the need for prior biological knowledge on individual proteins.

Computational Biology↗

PubMed2004

Omic space: coordinate-based integration and analysis of genomic phenomic interactions.

MOTIVATION: With the recent progress in genomics, various data sets of omic interactions describing networks of omic elements have become available. In order to obtain reliable hypotheses from the data, it is effective to integrate interactions from different sorts of data sets. In order to facilitate a coordinate-based integration and analysis of omic interactions, we introduce the concept of an omic space comprising a comprehensive set of omic planes. Genomic, transcriptomic, proteomic, metabolomic, phenomic and other omic planes are defined by two orthogonal genomic-coordinate axes. RESULTS: We show that the omic space concept helps us to assimilate biological findings comprehensively into hypotheses or models combining higher-order phenomena and lower-order mechanisms by demonstrating that a comprehensive ranking of correspondences among interactions in the space can be used effectively for estimating candidates of responsible gene pairs for epistatic interacting loci of tumors in mice. We also show that the omic space offers a convenient framework for database integration, by presenting a system named the 'Genome <==> Phenome Superhighway' (GPS) that serves as a framework for integration and visualization of omic interactions based on omic spaces of some model species including Homo sapiens, Mus musculus, Caenorhabditis elegans and Arabidopsis thaliana. AVAILABILITY: For the GPS web site, see http://omicspace.riken.jp/gps/.

Algorithms↗

PubMed2003

From words to literature in structural proteomics.

Technical advances on several frontiers have expanded the applicability of existing methods in structural biology and helped close the resolution gaps between them. As a result, we are now poised to integrate structural information gathered at multiple levels of the biological hierarchy - from atoms to cells - into a common framework. The goal is a comprehensive description of the multitude of interactions between molecular entities, which in turn is a prerequisite for the discovery of general structural principles that underlie all cellular processes.

Animals↗

PubMed2026

Circulating Biomarkers Related to Mitral Valve Prolapse: Current Evidence and Mechanistic Perspective.

PURPOSE OF REVIEW: Although imaging remains central to diagnosis and risk stratification, circulating biomarkers provide complementary information reflecting myocardial stress, fibrosis, extracellular matrix remodeling, inflammation, and metabolic dysregulation. The purpose of this review is to critically evaluate current and emerging circulating biomarkers in MVP and to assess whether these biomarkers may improve individualized management of this disease. RECENT FINDINGS: Natriuretic peptides are the most validated biomarkers in MVP, consistently predicting adverse outcomes and providing complementary prognostic information that may help inform the timing of surgical intervention, particularly in asymptomatic patients with significant mitral regurgitation. In contrast, evidence supporting fibrosis and inflammation mediators, proteomics, metabolomics, and circulating microRNAs remains emerging. In arrhythmic MVP, no circulating biomarker is currently recommended for routine risk stratification. Future research should prioritize phenotype-specific MVP registries, biomarker-CMR integration, multi-omics profiling, and biomarker-guided interventional trials.

Humans↗

PubMed2026

Spatial Multiomics Reveal Insights Into ADC Efficacy.

Antibody-drug conjugates (ADCs) have transformed the therapeutic landscape of solid tumors; however, responses remain heterogeneous and complex to predict. In addition, a growing number of multiple ADC targets are either approved or in late-stage clinical development, such as NECTIN-4, HER2, or TROP2 for metastatic urothelial cancer. Spatial multiomics-representing next-generation methods that couple high-plex RNA sequencing and multiplex protein imaging with precise x-y-z coordinates within tissues-offer a direct way to correlate (ADC) antigen expression, cell state information, and micro-anatomical context with patient treatment outcomes. In this review, we highlight suitability and technological advancements in current spatial transcriptomics and proteomics approaches to decode modes of action and resistance to ADCs and extract biological insights, particularly in metastatic urothelial cancer-and propose an integrative framework that combines spatial readouts with machine and/or deep learning-driven analytics to stratify patients, forecast on- and off-target toxicities, and guide next-generation linker-payload designs or combination therapies.

Humans↗

PubMedDate not supplied

Global analysis of the membrane subproteome of Pseudomonas aeruginosa using liquid chromatography-tandem mass spectrometry.

Pseudomonas aeruginosa is one of the most significant opportunistic bacterial pathogens in humans causing infections and premature death in patients with cystic fibrosis, AIDS, severe burns, organ transplants, or cancer. Liquid chromatography coupled online with tandem mass spectrometry was used for the large-scale proteomic analysis of the P. aeruginosa membrane subproteome. Concomitantly, an affinity labeling technique, using iodoacetyl-PEO biotin to tag cysteinyl-containing proteins, permitted the enrichment and detection of lower abundance membrane proteins. The application of these approaches resulted in the identification of 786 proteins. A total of 333 proteins (42%) had a minimum of one transmembrane domain (ranging from 1 to14) and 195 proteins were classified as hydrophobic based on their positive GRAVY values (ranging from 0.01 to 1.32). Key integral inner and outer membrane proteins involved in adaptation and antibiotic resistance were conclusively identified, including the detection of 53% of all predicted opr-type porins (outer integral membrane proteins) and all the components of the mexA-mexB-oprM transmembrane protein complex. This work represents one of the most comprehensive proteomic analyses of the membrane subproteome of P. aeruginosa and for prokaryotes in general.

Affinity Labels↗

PubMed2026

iModMix: integrative module analysis for multi-omics data.

SUMMARY: Integrative Module Analysis for Multi-omics Data (iModMix) is a biology-agnostic framework that enables the discovery of novel associations across any type of quantitative abundance data, including but not limited to transcriptomics, proteomics, and metabolomics. Instead of relying on pathway annotations or prior biological knowledge, iModMix constructs data-driven modules using graphical lasso to estimate sparse networks from omics features. These modules are summarized into eigenfeatures and correlated across datasets for horizontal integration, while preserving the distinct feature sets and interpretability of each omics type. iModMix operates directly on matrices containing expression or abundances for a wide range of features, including but not limited to genes, proteins, and metabolites. Because it does not rely on annotations (e.g., KEGG identifiers), it can seamlessly incorporate both identified and unidentified metabolites, addressing a key limitation of many existing metabolomics tools. iModMix is available as a user-friendly R Shiny application requiring no programming expertise (https://imodmix.moffitt.org), and as a Bioconductor R package for advanced users (https://bioconductor.org/packages/release/bioc/html/iModMix.html). The tool includes several public and in-house datasets to illustrate its utility in identifying novel multi-omics relationships in diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: iModMix is freely available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/iModMix.html), and the example dataset package (iModMixData) is also available from Bioconductor (https://bioconductor.org/packages/release/ data/experiment/html/iModMixData.html). The R package source code and Docker are available from GitHub: https://github.com/biodatalab/iModMix. Shiny application can be accessed at: https://imodmix.moffitt.org.

Multiomics↗

PubMed2003

A computational pipeline for protein structure prediction and analysis at genome scale.

MOTIVATION: Experimental techniques alone cannot keep up with the production rate of protein sequences, while computational techniques for protein structure predictions have matured to such a level to provide reliable structural characterization of proteins at large scale. Integration of multiple computational tools for protein structure prediction can complement experimental techniques. RESULTS: We present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is our threading-based protein structure prediction system PROSPECT. The pipeline consists of a dozen tools for identification of protein domains and signal peptide, protein triage to determine the protein type (membrane or globular), protein fold recognition, generation of atomic structural models, prediction result validation, etc. Different processing and prediction branches are determined automatically by a prediction pipeline manager based on identified characteristics of the protein. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. Genome-scale applications on Caenorhabditis elegans, Pyrococcus furiosus and three cyanobacterial genomes are presented. AVAILABILITY: The pipeline is available at http://compbio.ornl.gov/proteinpipeline/

Algorithms↗

PubMed2004

Analyzing Mycobacterium tuberculosis proteomes for candidate vaccine epitopes.

Secreted antigens of Mycobacterium tuberculosis (Mtb) induce strong T cell responses and interferon-gamma (IFN-gamma) secretion, both of which are integral in the defense against Mtb. We used web-based tools (SignaIP and Prosite) to identify putative secreted proteins from Mtb genomes CDC 1551 and H37Rv. We then used EpiMatrix, a proprietary pattern-matching algorithm, to do a preliminary analysis of these proteins for regions that contained a high number of class II MHC binding motif matches. The use of bioinformatics tools reduced the number of potential epitopes to be screened to 5% of the 1.3 million overlapping peptides. Peripheral blood mononuclear cells (PBMC) were obtained from healthy, asymptomatic tuberculin skin test-positive donors. Of the 17 highest-ranking peptide candidates that could be synthesized for this preliminary in vitro evaluation, 15 (88%) stimulated IFN-gamma response, and eight (47%) stimulated lymphocyte proliferation in vitro. IFN-gamma ELISpot assays were therefore a more sensitive test for T cell response to these peptides than were proliferation assays. One highly promiscuous epitope (MT2281-26-J, WRRRPLSSALLSFGLLLGGLPL) induced IFN-gamma secretion in PBMC from 11 of 25 Mtb immune subjects (44%). Overall, 15 epitopes, and MT2281-26-J in particular, are candidates for inclusion in a multi-epitope TB vaccine. These findings support the systematic application of bioinformatics tools to whole genomes when used in combination with in vitro methods for screening and confirming epitopes.

Amino Acid Sequence↗

PubMed2001

Alpha-cyano-4-hydroxycinnamic acid affinity sample preparation. A protocol for MALDI-MS peptide analysis in proteomics.

We present a new MALD1 sample preparation technique for peptide analysis using the matrix alpha-cyano-4-hydroxy-cinnamic acid (CHCA) and prestructured sample supports. The preparation integrates sample purification, based on the affinity of microcrystalline CHCA for peptides, thereby simplifying the analysis of crude peptide mixtures. Enzymatic digests can thus be prepared directly, without preceding purification. Prepared samples are homogeneous, facilitating automatic spectra acquisition. This method allows preparation of large numbers of samples with little effort and without the need for automation. These features make the described preparation suitable for cost-efficient high-throughput protein identification. Performance of the sample preparation is demonstrated with in situ proteolytic digests of human brain proteins separated by two-dimensional gel electrophoresis.

Brain Chemistry↗

PubMedDate not supplied

[Rationale for oncological surgery in multimodal cancer treatments].

The permanent dissemination of circulating cancer cells from infiltrative cancers renders the notion of localized cancer obsolete. This raises the question of the role of treatments such as surgery which are localized in time and space. Moreover, it is now proven that surgery increases the number of circulating cancers cells and produces transient immunodepression which promotes cancer cell implantation. In addition, the healing process stimulates growth factors, which also act on tumor growth. In spite of these deleterious effects, oncological surgery remains an important tool in the anti-cancer armamentarium. Optimal cytoreductive surgery facilitates the effect of adjuvant treatments by eliminating the vascular sanctuaries present in the center of any large tumor and by its effect as a striking-force very localized in time and space. Oncological surgery must evolve as an integrative part of our improving understanding of tumor biology, taking into account recent developments in functional imaging, genomics and proteomics. Modalities and timing must be reconsidered for oncological surgery to play its proper role as one of the many components contributing to multimodal cancer treatments. New modalities will undoubtedly be necessary with the development of future technologies.

Animals↗

PubMed2002

Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts.

MOTIVATION: Protein abundance is related to mRNA expression through many different cellular processes. Up to now, there have been conflicting results on how correlated the levels of these two quantities are. Given that expression and abundance data are significantly more complex and noisy than the underlying genomic sequence information, it is reasonable to simplify and average them in terms of broad proteomic categories and features (e.g. functions or secondary structures), for understanding their relationship. Furthermore, it will be essential to integrate, within a common framework, the results of many varied experiments by different investigators. This will allow one to survey the characteristics of highly expressed genes and proteins. RESULTS: To this end, we outline a formalism for merging and scaling many different gene expression and protein abundance data sets into a comprehensive reference set, and we develop an approach for analyzing this in terms of broad categories, such as composition, function, structure and localization. As the various experiments are not always done using the same set of genes, sampling bias becomes a central issue, and our formalism is designed to explicitly show this and correct for it. We apply our formalism to the currently available gene expression and protein abundance data for yeast. Overall, we found substantial agreement between gene expression and protein abundance, in terms of the enrichment of structural and functional categories. This agreement, which was considerably greater than the simple correlation between these quantities for individual genes, reflects the way broad categories collect many individual measurements into simple, robust averages. In particular, we found that in comparison to the population of genes in the yeast genome, the cellular populations of transcripts and proteins (weighted by their respective abundances, the transcriptome and what we dub the translatome) were both enriched in: (i) the small amino acids Val, Gly, and Ala; (ii) low molecular weight proteins; (iii) helices and sheets relative to coils; (iv) cytoplasmic proteins relative to nuclear ones; and (v) proteins involved in 'protein synthesis,' 'cell structure,' and 'energy production.' SUPPLEMENTARY INFORMATION: http://genecensus.org/expression/translatome

Algorithms↗

PubMed2004

Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting.

UNLABELLED: A set of new algorithms and software tools for automatic protein identification using peptide mass fingerprinting is presented. The software is automatic, fast and modular to suit different laboratory needs, and it can be operated either via a Java user interface or called from within scripts. The software modules do peak extraction, peak filtering and protein database matching, and communicate via XML. Individual modules can therefore easily be replaced with other software if desired, and all intermediate results are available to the user. The algorithms are designed to operate without human intervention and contain several novel approaches. The performance and capabilities of the software is illustrated on spectra from different mass spectrometer manufacturers, and the factors influencing successful identification are discussed and quantified. MOTIVATION: Protein identification with mass spectrometric methods is a key step in modern proteomics studies. Some tools are available today for doing different steps in the analysis. Only a few commercial systems integrate all the steps in the analysis, often for only one vendor's hardware, and the details of these systems are not public. RESULTS: A complete system for doing protein identification with peptide mass fingerprints is presented, including everything from peak picking to matching the database protein. The details of the different algorithms are disclosed so that academic researchers can have full control of their tools. AVAILABILITY: The described software tools are available from the Halmstad University website www.hh.se/staff/bioinf/ SUPPLEMENTARY INFORMATION: Details of the algorithms are described in supporting information available from the Halmstad University website www.hh.se/staff/bioinf/

Algorithms↗

PubMed2004

Modern criteria to establish human cancer etiology.

The Cancer Etiology Branch of the National Cancer Institute hosted a workshop, "Validation of a causal relationship: criteria to establish etiology," to determine whether recent technological advances now make it possible to delineate improved or novel criteria for the rapid establishment for cancer causation. The workshop was held in Washington, D.C., December 11-12, 2003, and participants were among the international leaders in the fields of epidemiology, chemistry, biochemistry, microbiology, virology, environmental and chemical carcinogenesis, immunology, pathology, molecular pathology, genetics, oncology, and surgical oncology. There was a general consensus that the rapid identification of human carcinogens and their removal (when possible) or the establishment of specific preventive and therapeutic measures was the most desirable and effective way to have a rapid and positive impact in the fight against cancer. From a clinical perspective, it may be as important to target initiators, cocarcinogens and promoters, if by removing any one of them tumor growth can be prevented. Future studies should focus on interactions among and between different biological, chemical, and physical agents. Analyses of single agents can at times miss their carcinogenic potential when such agents are carcinogenic only in subgroups of individuals because of their genetic background, diet, exposure to other carcinogens, or microbial infection. Epidemiology, molecular pathology (including chemistry, biochemistry, molecular biology, molecular virology, molecular genetics, epigenetics, genomics, proteomics, and other molecular-based approaches), and animal and tissue culture experiments should all be seen as important integrating evidence in the determination of human carcinogenicity. Concerning the respective roles of epidemiology and molecular pathology, it was noted that epidemiology allows the determination of the overall effect of a given carcinogen in the human population (e.g., hepatitis B virus and hepatocellular carcinoma) but cannot prove causality in the individual tumor patient. Molecular pathology cannot determine the overall impact of a carcinogen in the population but can at times prove causality in the individual tumor patient [such as the detection of high-risk human papillomavirus (HPV) in a cervical carcinoma biopsy]. This is possible when molecular techniques have shown that the agent is required for transformation or malignant growth of human cells (such as antisense HPV strategies showing the requirement for the expression of HPV proteins for tumor cell growth) and when there is supportive experimental animal evidence. Ideally, epidemiology and molecular pathology information together with experimental evidence in animals should be available for the most reliable identification of human carcinogens. All sets of data are not always available, and a rapid identification of human carcinogens is in the best public health interest. Swift validation of a causal relationship when followed by a rapid deployment of preventive and therapeutic approaches should lead to a favorable public health impact (such as hepatitis B virus vaccination to prevent hepatocellular carcinoma).

Carcinogens↗

PubMed2026

Profiler: an open web platform for multi-omics analysis.

MOTIVATION: High-throughput multi-omics technologies produce increasingly large and heterogeneous datasets that are difficult to analyze without advanced computational expertise. Existing bioinformatics tools are often fragmented or limited to specific omics types, hindering reproducibility and accessibility. There is a critical need for an integrated, user-friendly, and scalable platform capable of supporting multi-omics analyses across different data modalities. RESULTS: We present Profiler, an open-source, modular platform that unifies data import, quality control, preprocessing, statistical testing, machine and deep learning, biomarker discovery, pathway and drug-target enrichment, and survival modeling within a single reproducible environment. Built in Python with Streamlit, Profiler is available as both a web-based platform deployed on high-performance computing and a desktop version for local execution, enabling flexible usage across computational infrastructures. Profiler supports diverse omics modalities, including proteomics, transcriptomics, lipidomics, and electroencephalogram data. Through applications to glioblastoma proteomic, pancancer, and multi-omics datasets, Profiler reproduced known molecular subtypes, revealed potential therapeutic targets, and generated fully traceable analysis reports within minutes. By integrating advanced analytics behind an intuitive interface, Profiler democratizes multi-omics analysis and provides a robust, scalable foundation for systems biology and precision medicine research. AVAILABILITY AND IMPLEMENTATION: Profiler is open-source and freely available via its web platform (https://prism-profiler.univ-lille.fr) and GitHub (web version: https://github.com/yanisZirem/Profiler_v1_requests_datatests, desktop version: https://github.com/yanisZirem/prism-profiler), and archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17478158).

Software↗

PubMed2001

The evolution of controlled multitasked gene networks: the role of introns and other noncoding RNAs in the development of complex organisms.

Eukaryotic phenotypic diversity arises from multitasking of a core proteome of limited size. Multitasking is routine in computers, as well as in other sophisticated information systems, and requires multiple inputs and outputs to control and integrate network activity. Higher eukaryotes have a mosaic gene structure with a dual output, mRNA (protein-coding) sequences and introns, which are released from the pre-mRNA by posttranscriptional processing. Introns have been enormously successful as a class of sequences and comprise up to 95% of the primary transcripts of protein-coding genes in mammals. In addition, many other transcripts (perhaps more than half) do not encode proteins at all, but appear both to be developmentally regulated and to have genetic function. We suggest that these RNAs (eRNAs) have evolved to function as endogenous network control molecules which enable direct gene-gene communication and multitasking of eukaryotic genomes. Analysis of a range of complex genetic phenomena in which RNA is involved or implicated, including co-suppression, transgene silencing, RNA interference, imprinting, methylation, and transvection, suggests that a higher-order regulatory system based on RNA signals operates in the higher eukaryotes and involves chromatin remodeling as well as other RNA-DNA, RNA-RNA, and RNA-protein interactions. The evolution of densely connected gene networks would be expected to result in a relatively stable core proteome due to the multiple reuse of components, implying that cellular differentiation and phenotypic variation in the higher eukaryotes results primarily from variation in the control architecture. Thus, network integration and multitasking using trans-acting RNA molecules produced in parallel with protein-coding sequences may underpin both the evolution of developmentally sophisticated multicellular organisms and the rapid expansion of phenotypic complexity into uncontested environments such as those initiated in the Cambrian radiation and those seen after major extinction events.

Animals↗

PubMed2006

Advances in plant proteomics.

With the avalanche of genomic information and improvements in analytical technology, proteomics is becoming increasingly important for the study of many different aspects of plant functions. Since proteins serve as important components of major signaling and biochemical pathways, studies at protein levels are essential to reveal molecular mechanisms underlying plant growth, development, and interactions with the environment. The plant proteome is highly complex and dynamic. Although great strides need to be taken towards the ultimate goal of characterizing all the proteins in a proteome, current technologies have provided immense opportunities for high-throughput proteomic studies that have gone beyond simple protein identification to analyzing various functional aspects, such as quantification, PTM, subcellular localization, and protein-protein interactions. In this review of plant proteomics, advances in protein fractionation, separation, and MS will be outlined. Focus will be on recent development in functional analysis of plant proteins, which paves the way towards the comprehensive integration with transcriptomics, metabolomics, and other large scale "-omics" into systems biology.

Algorithms↗

PubMed2025

Proteomics-enabled learning machine algorithms enhance the prediction of cardiovascular diseases in patients with type 2 diabetes mellitus.

BACKGROUND AND AIMS: Estimating the risk of cardiovascular disease (CVD) complications in type 2 diabetes mellitus (T2DM) patients is critical in the medical decision-making process. This study aimed to use a machine learning technique combined with proteomics to develop personalized models for predicting CVD in patients with T2DM. METHODS AND RESULTS: In total, 874 patients with T2DM and 2,920 Olink proteins obtained from the UK Biobank were used in this study. Proteins were screened using Cox regression and LASSO regression. A basic model containing clinical features and a full model combining proteome and clinical features were constructed using the random survival forest algorithm. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive performance of the models and compare them with other CVD predictive models. Compared with the basic model, the full model performed better in predicting CVD, with time-dependent AUCs of 0.81 (3 years), 0.74 (5 years) and 0.74 (10 years) (0.77, 0.69 and 0.67). We calculated the risk scores of the Framingham, ASCVD and Score2-Diabetes models. The results revealed that the prediction performance of the full model was also better than that of the abovementioned models. In terms of differentiation accuracy, the results of the net reclassification improvement index and integrated discrimination improvement index showed that the full model can identify high-risk individuals more accurately (accuracy rate: 79% vs. 69%). CONCLUSIONS: Proteomics can be used to predict cardiovascular complications in diabetic patients. It is also necessary to consider the applicability of the model due to the limitations of the sample size and the constraints of proteomics in clinical applications.

Humans↗