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PubMedDate not supplied

Post-genomic virology: the impact of bioinformatics, microarrays and proteomics on investigating host and pathogen interactions.

Post-genomic research encompasses many diverse aspects of modern science. These include the two broad subject areas of computational biology (bioinformatics) and functional genomics. Laboratory based functional genomics aims to measure and assess either the messenger RNA (mRNA) levels (transcriptome studies) or the protein content (proteome studies) of cells and tissues. All of these methods have been applied recently to the study of host and pathogen interactions for both bacteria and viruses. A basic overview of the technology is given in this review together with approaches to data analysis. The wealth of information produced from even these preliminary studies has shown the generalities, subtleties and specificities of host-pathogen interactions. Such research should ultimately result in new methods for diagnosing and treating infectious diseases.

Amino Acid Sequence↗
PubMed2004

The role of analytical sciences in medical systems biology.

Medical systems biology has generated widespread interest because of its bold conception and exciting potential, but the field is still in its infancy. Although there has been tremendous progress achieved recently in generating, integrating and analysing data in the medical and pharmaceutical field, many challenges remain, especially with respect to the crucial core technologies required for analytical characterization. This review briefly summarizes these aspects for metabolomics, proteomics, data handling and multivariate biostatistics.

Biomarkers↗
PubMed2006

Proteomics and transfusion medicine: future perspectives.

Limited number of important discoveries have greatly contributed to the progresses achieved in the blood transfusion; ABO histo-blood groups, citrate as anticoagulant, fractionation of plasma proteins, plastic bags and apheresis machines. Three major types of blood products are transfused to patients: red cell concentrates, platelet concentrates and fresh frozen plasma. Several parameters of these products change during storage process and they have been well studied over the years. However, several aspects have completely been ignored; in particular those related to peptide and protein changes. This review presents what has been done using proteomic tools and the potentials of proteomics for transfusion medicine.

ABO Blood-Group System↗
PubMed2005

WebGestalt: an integrated system for exploring gene sets in various biological contexts.

High-throughput technologies have led to the rapid generation of large-scale datasets about genes and gene products. These technologies have also shifted our research focus from 'single genes' to 'gene sets'. We have developed a web-based integrated data mining system, WebGestalt (http://genereg.ornl.gov/webgestalt/), to help biologists in exploring large sets of genes. WebGestalt is composed of four modules: gene set management, information retrieval, organization/visualization, and statistics. The management module uploads, saves, retrieves and deletes gene sets, as well as performs Boolean operations to generate the unions, intersections or differences between different gene sets. The information retrieval module currently retrieves information for up to 20 attributes for all genes in a gene set. The organization/visualization module organizes and visualizes gene sets in various biological contexts, including Gene Ontology, tissue expression pattern, chromosome distribution, metabolic and signaling pathways, protein domain information and publications. The statistics module recommends and performs statistical tests to suggest biological areas that are important to a gene set and warrant further investigation. In order to demonstrate the use of WebGestalt, we have generated 48 gene sets with genes over-represented in various human tissue types. Exploration of all the 48 gene sets using WebGestalt is available for the public at http://genereg.ornl.gov/webgestalt/wg_enrich.php.

Computer Graphics↗
PubMed2005

SPS' Digest: the Swiss Proteomics Society selection of proteomics articles.

Despite the consolidation of the specialized proteomics literature around a few established journals, such as Proteomics, Molecular and Cellular Proteomics, and the Journal of Proteome Research, a lot of information is still spread in many different publications from different fields, such as analytical sciences, MS, bioinformatics, etc. The purpose of SPS' Digest is to gather a selection of proteomics articles, to categorize them, and to make the list available on a periodic basis through a web page and email alerts.

Animals↗
PubMed2001

FindTarget: software for subtractive genome analysis.

In silico subtractive/differential genome analysis is a powerful approach for identifying genus- or species-specific genes, or groups of genes that are responsible for a unique phenotype. By this method, one searches for genes present in one group of bacteria and absent in another group. A software package has been developed, named FindTarget, that has a user-friendly web interface to facilitate differential genome analysis. The user chooses the genomes to compare, the similarity criteria and the thresholds to decide if a gene has a counterpart in another genome. The searches are based on BLASTP comparisons of proteomes. FindTarget also includes access to sequences, coloured multiple alignments, phylogenetic trees of conserved proteins and links to public annotated databases which provide a means for validation of the results. To illustrate this approach, a FindTarget search for genes putatively involved in the specificity of cell envelope synthesis of Gram-negative bacteria is presented. The results show that most of the identified genes are clearly involved in cell wall processes, underlining the power of such an approach in general and that of FindTarget in particular.

Bacterial Proteins↗
PubMed2004

Large-scale proteomic analysis of membrane proteins.

Proteomic analysis of membrane proteins is a promising approach for the identification of novel drug targets and/or disease biomarkers. Despite notable technological developments, obstacles related to extraction and solublization of membrane proteins are encountered. A critical discussion of the different preparative methods of membrane proteins is offered in relation to downstream proteomic applications, mainly gel-based analyses and mass spectrometry. Frequently, unknown proteins are identified by high-throughput profiling of membrane proteins. In search for novel membrane proteins, analysis of protein sequences using computational tools is performed to predict the presence of transmembrane domains. This review also presents these bioinformatic tools with the human proteome as a case study. Along with technological innovations, advancements in the areas of sample preparation and computational prediction of membrane proteins will lead to exciting discoveries.

Mass Spectrometry↗
PubMed2003

A new integrated statistical approach to the diagnostic use of two-dimensional maps.

Two-dimensional (2-D) electrophoresis is a very useful technique for the analysis of proteins in biological tissues. The complexity of the 2-D maps obtained causes many difficulties in the comparison of different samples. A new method is proposed for comparing different 2-D maps, based on five steps: (i) the digitalisation of the image; (ii) the transformation of the digitalised map in a fuzzy entity, in order to consider the variability of the 2-D electrophoretic separation; (iii) the calculation of a similarity index for each pair of maps; (iv) the analysis by multidimensional scaling of the previously obtained similarity matrix; (v) the analysis by classification or cluster analysis techniques of the resulting map co-ordinates. The method adopted was first tested on some simulated samples in order to evaluate its sensitivity to small changes in the spots position and size. The optimal setting of the method parameters was also investigated. Finally, the method was successfully applied to a series of real samples corresponding to the electrophoretic bidimensional analysis of sera from normal and nicotine-treated rats. Multidimensional scaling allowed the separation of the two classes of samples without any misclassification.

Animals↗
PubMed2005

New science-based endpoints to accelerate oncology drug development.

Although several new oncology drugs have reached the market, more than 80% of drugs for all indications entering clinical development do not get marketing approval, with many failing late in development often in Phase III trials, because of unexpected safety issues or difficulty determining efficacy, including confounded outcomes. These factors contribute to the high costs of oncology drug development and clearly show the need for faster, more cost-effective strategies for evaluating oncology drugs and better definition of patients who will benefit from treatment. Remarkable advances in the understanding of neoplastic progression at the cellular and molecular levels have spurred the discovery of molecularly targeted drugs. This progress along with advances in imaging and bioassay technologies are the basis for describing and evaluating new biomarker endpoints as well as for defining other biomarkers for identifying patient populations, potential toxicity, and providing evidence of drug effect and efficacy. Definitions and classifications of these biomarkers for use in oncology drug development are presented in this paper. Science-based and practical criteria for validating biomarkers have been developed including considerations of mechanistic plausibility, available methods and technology, and clinical feasibility. New promising tools for measuring biomarkers have also been developed and are based on genomics and proteomics, direct visualisation by microscopy (e.g., confocal microscopy and computer-assisted image analysis of cellular features), nanotechnologies, and direct and remote imaging (e.g., fluorescence endoscopy and anatomical, functional and molecular imaging techniques). The identification and evaluation of potential surrogate endpoints and other biomarkers require access to and analysis of large amounts of data, new technologies and extensive research resources. Further, there is a requirement for a convergence of research, regulatory and drug developer thinking - an effort that will not be accomplished by individual scientists or research institutions. Research collaborations are needed to foster development of these new endpoints and other biomarkers and, in the United States (US), include ongoing efforts among the Food and Drug Administration (FDA), National Cancer Institute (NCI), academia, and industry.

Antineoplastic Agents↗
PubMedDate not supplied

Peptide identification using peptide amino acid attribute vectors.

We describe the theoretical basis for a peptide identification method wherein peptides are represented as vectors based on their amino acid composition and grouped into clusters. Unknown peptides are identified by finding the database cluster and peptide entries with the shortest Euclidian distance. We demonstrate that the amino acid composition of peptides is virtually as informative as the sequence and allows rapid peptide identification more accurately than peptide mass alone.

Algorithms↗
PubMed2006

Retention prediction of peptides based on uninformative variable elimination by partial least squares.

A quantitative structure-retention relationship analysis was performed on the chromatographic retention data of 90 peptides, measured by gradient elution reversed-phase liquid chromatography, and a large set of molecular descriptors computed for each peptide. Such approach may be useful in proteomics research in order to improve the correct identification of peptides. A principal component analysis on the set of 1726 molecular descriptors reveals a high information overlap in the descriptor space. Since variable selection is advisable, the retention of the peptides is modeled with uninformative variable elimination partial least squares, besides classic partial least squares regression. The Kennard and Stone algorithm was used to select a calibration set (63 peptides) from the available samples. This set was used to build the quantitative structure-retention relationship models. The remaining 27 peptides were used as independent external test set to evaluate the predictive power of the constructed models. The UVE-PLS model consists of 5 components only (compared to 7 components in the best PLS model), and has the best predictive properties, i.e., the average error on the retention time is less than 30 s. When compared also to stepwise regression and an empirical model, the obtained UVE-PLS model leads to better and much better predictions, respectively.

Algorithms↗
PubMed2005

Algorithmic approaches for computing elementary modes in large biochemical reaction networks.

The concept of elementary (flux) modes provides a rigorous description of pathways in metabolic networks and proved to be valuable in a number of applications. However, the computation of elementary modes is a hard computational task that gave rise to several variants of algorithms during the last years. This work brings substantial progresses to this issue. The authors start with a brief review of results obtained from previous work regarding (a) a unified framework for elementary-mode computation, (b) network compression and redundancy removal and (c) the binary approach by which elementary modes are determined as binary patterns reducing the memory demand drastically without loss of speed. Then the authors will address herein further issues. First, a new way to perform the elementarity tests required during the computation of elementary modes which empirically improves significantly the computation time in large networks is proposed. Second, a method to compute only those elementary modes where certain reactions are involved is derived. Relying on this method, a promising approach for computing EMs in a completely distributed manner by decomposing the full problem in arbitrarity many sub-tasks is presented. The new methods have been implemented in the freely available software tools FluxAnalyzer and Metatool and benchmark tests in realistic networks emphasise the potential of our proposed algorithms.

Algorithms↗
PubMed2004

An improved model for prediction of retention times of tryptic peptides in ion pair reversed-phase HPLC: its application to protein peptide mapping by off-line HPLC-MALDI MS.

The proposed model is based on the measurement of the retention times of 346 tryptic peptides in the 560- to 4,000-Da mass range, derived from a mixture of 17 protein digests. These peptides were measured in HPLC-MALDI MS runs, with peptide identities confirmed by MS/MS. The model relies on summation of the retention coefficients of the individual amino acids, as in previous approaches, but additional terms are introduced that depend on the retention coefficients for amino acids at the N-terminal of the peptide. In the 17-protein mixture, optimization of two sets of coefficients, along with additional compensation for peptide length and hydrophobicity, yielded a linear dependence of retention time on hydrophobicity, with an R2 value about 0.94. The predictive capability of the model was used to distinguish peptides with close m/z values and for detailed peptide mapping of selected proteins. Its applicability was tested on columns of different sizes, from nano- to narrow-bore, and for direct sample injection, or injection via a pre-column. It can be used for accurate prediction of retention times for tryptic peptides on reversed-phase (300-A pore size) columns of different sizes with a linear water-ACN gradient and with TFA as the ion-pairing modifier.

Amino Acid Sequence↗
PubMed2026

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

MOTIVATION: The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. RESULTS: Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of ∼0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts. AVAILABILITY AND IMPLEMENTATION: The source code of ProMeta is available at GitHub (https://github.com/lihan97/ProMeta).

Proteomics↗
PubMed2026

Odon: an ultra-fast viewer for spatial proteomics.

MOTIVATION: Multiplexed spatial proteomics and spatial transcriptomics generate large, high-dimensional imaging datasets that are challenging to visualize efficiently, particularly at whole-slide and cohort scale. Visualization is an essential step for rapid detection of staining artefacts, such as protein aggregates or non-specific staining. RESULTS: Here, we present Odon, a native Rust desktop viewer designed for rapid, interactive exploration of multiplex imaging data on a standard laptop. Odon is primarily built around the OME-Zarr imaging format, and supports annotations via GeoJSON and GeoParquet, with secondary support for SpatialData, Xenium containers, and TIFF. Data can be stored locally or streamed directly from HTTP or S3-compatible object storage using viewport-driven tile loading. Odon incorporates a highly optimized rendering engine designed for viewport-driven tile loading and GPU-based compositing. In scripted benchmarks using synthetic multiplex OME-Zarr datasets, Odon showed lower peak memory use, lower affine-derived zoom-step error, and faster warm-start image loading than napari and QuPath under the tested conditions. Its GPU-based compositing pipeline also enables smooth rendering and interaction with >1 000 000 segmented cells. Odon further supports integrated visual analytics, including live thresholding and cell selection, and a mosaic mode for simultaneous viewing of hundreds of regions of interest in cohort and tissue microarray studies. Together, these features establish Odon as a high-performance platform for scalable visualization of spatial proteomics data. AVAILABILITY AND IMPLEMENTATION: Source code and compiled installers are available at https://github.com/alexcoulton/odon.

Proteomics↗
PubMed2005

Residue-rotamer-reduction algorithm for the protein side-chain conformation problem.

MOTIVATION: The protein side-chain conformation problem is a central problem in proteomics with wide applications in protein structure prediction and design. Computational complexity results show that the problem is hard to solve. Yet, instances from realistic applications are large and demand fast and reliable algorithms. RESULTS: We propose a new global optimization algorithm, which for the first time integrates residue reduction and rotamer reduction techniques previously developed for the protein side-chain conformation problem. We show that the proposed approach simplifies dramatically the topology of the underlining residue graph. Computations show that our algorithm solves problems using only 1-10% of the time required by the mixed-integer linear programming approach available in the literature. In addition, on a set of hard side-chain conformation problems, our algorithm runs 2-78 times faster than SCWRL 3.0, which is widely used for solving these problems. AVAILABILITY: The implementation is available as an online server at http://eudoxus.scs.uiuc.edu/r3.html

Algorithms↗