CEBS update: curated toxicology database with enhanced tools for data integration.

The Chemical Effects in Biological Systems database (CEBS) contains extensive toxicology study results and metadata from the Division of the National Toxicology Program (NTP) and other studies of environmental health interest. This resource grants public access to search and collate data from over 10 250 studies for 12 750 test articles (chemicals, environmental agents). CEBS has made considerable strides over the last 5 years to integrate growing internal data repositories into data warehouses and data marts to better serve the public with high quality curated datasets. This effort includes harmonizing legacy terms and metadata to current standards, mapping test articles to external identifiers, and aligning terms to OBO (Open Biological and Biomedical Ontology) Foundry ontologies. The data are made available through the CEBS Homepage (https://cebs.niehs.nih.gov/cebs/), guided search applications, flat files on FTP (file transfer protocol), and APIs (application programming interface) for user access and to provide a bridge for computational tools. The user interface is intuitive with a single search bar to query keywords related to study metadata, publications, and data availability. Results are consolidated to single pages for each test article with NTP conclusions, publications, individual studies, data collections, and links to related test articles and projects available together.

ProteomicsDB: toward a FAIR open-source resource for life-science research.

ProteomicsDB (https://www.ProteomicsDB.org) is a multi-omics and multi-organism resource for life science research. In this update, we present our efforts to continuously develop and expand ProteomicsDB. The major focus over the last two years was improving the findability, accessibility, interoperability and reusability (FAIR) of the data as well as its implementation. For this purpose, we release a new application programming interface (API) that provides systematic access to essentially all data in ProteomicsDB. Second, we release a new open-source user interface (UI) and show the advantages the scientific community gains from such software. With the new interface, two new visualizations of protein primary, secondary and tertiary structure as well an updated spectrum viewer were added. Furthermore, we integrated ProteomicsDB with our deep-neural-network Prosit that can predict the fragmentation characteristics and retention time of peptides. The result is an automatic processing pipeline that can be used to reevaluate database search engine results stored in ProteomicsDB. In addition, we extended the data content with experiments investigating different human biology as well as a newly supported organism.

Proteomic analysis of human keratinocytes under UVA-induced stress / Análise proteômica de queratinócitos humanos sob estresse induzido por luz UVA

The solar ultraviolet (UV) radiation that reaches the Earth is composed of 95% of UVA (320 to 400 nm) and 5% of UVB (280 to 320 nm) radiation. UVB is carcinogenic, generating potentially mutagenic DNA lesions. The solar UVA radiation also causes DNA damage, but this fact does not fully account for its biological impact. UVA is absorbed by non-DNA cellular chromophores, generating reactive oxygen species such as singlet oxygen. Knowing the proteome mediates stress responses in cells, here we investigated the cellular effects of a non-cytotoxic dose of UVA radiation, equivalent to about 20 minutes of midday sun exposure, on the proteome of human keratinocytes. Using a combination of mass spectrometry-based proteomics, bioinformatics, and conventional biochemical assays, we analyzed two aspects of UVA-induced stress: spatial remodeling of the proteome in subcellular compartments 30 minutes after stress and long-term changes in protein levels and secretion (24 hours and 7 days postirradiation). In the first part of this thesis, we quantified and assigned subcellular localization for over 3000 proteins, of which about 600 potentially redistribute upon UVA exposure. Protein redistributions were accompanied by redox modulations, mitochondrial fragmentation and DNA damage. In the second part of the work, our results showed that primary human keratinocytes enter senescence upon exposure to a single dose of UVA, mounting antioxidant and inflammatory responses. Cells under UVA-induced senescence further elicit paracrine responses in neighboring premalignant HaCaT epithelial cells via inflammatory mediators. Altogether, these results reiterate the role of UVA radiation as a potent metabolic stressor in the skin

A radiação ultravioleta (UV) solar que atinge a superfície terrestre é composta por 95% de radiação UVA (320 a 400 nm) e 5% de radiação UVB (280 a 320 nm). A radiação UVB é carcinogênica e gera lesões potencialmente mutagênicas no DNA. A radiação UVA solar também gera danos no DNA, mas a genotoxicidade dessa radiação não explica inteiramente o seu impacto biológico. Atualmente, sabe-se que a radiação UVA é absorvida por cromóforos celulares, gerando espécies reativas de oxigênio, como o oxigênio singlete. Sabendo que o proteoma é um mediador de respostas ao estresse celular, nós investigamos os efeitos celulares de uma dose não-citotóxica de radiação UVA, equivalente a cerca de 20 minutos de exposição ao sol, no proteoma de queratinócitos humanos. Utilizando espectrometria de massas, bioinformática e ensaios bioquímicos convencionais, nós analisamos dois aspectos do estresse induzido por radiação UVA: o remodelamento espacial do proteoma 30 minutos depois do estresse e alterações nos níveis e na secreção de proteínas no longo prazo (24 horas e 7 dias depois da irradiação). Na primeira parte desta tese, nós quantificamos e atribuímos classificações de localização subcelular a mais de 3000 proteínas. Dentre essas proteínas, 600 tem potencialmente a sua distribuição subcelular alterada em resposta à radiação. As redistribuições subcelulares são acompanhadas de modulações redox, fragmentação mitocondrial e danos no DNA. Na segunda parte da tese, os nossos resultados mostraram que queratinócitos humanos primários entram em senescência sob exposição a uma única dose de radiação UVA, montando respostas antioxidantes e pró-inflamatórias. Células sob senescência induzida por UVA, por sua vez, desencadeiam respostas parácrinas em queratinócitos pré-tumorais (células HaCaT) por meio de mediadores inflamatórios. Em conjunto, esses resultados reiteram o papel da radiação UVA como um potente estressor metabólico em células da pele

Análise proteômica de células BEAS-2B expostas a benzo[α]pireno e nicotinamida ribosídeo / Proteomic Analysis of BEAS-2B cells exposed to benzo[α]pyrene and nicotinamide riboside

Responsável por milhões de óbitos anuais e um grande custo para a saúde pública, o câncer é a segunda maior causa de mortes no mundo. Dentre seus diversos tipos, o câncer de pulmão, além da alta incidência, é um dos mais letais. A exposição a substâncias tóxicas provenientes da combustão de matéria orgânica, assim como o consumo de cigarro, são os principais responsáveis pela alta incidência de câncer de pulmão. Dentre estas substâncias, está o benzo[α]pireno (B[α]P), um carcinógeno completo, ou seja, capaz de iniciar e promover o processo de carcinogênese. Resultados anteriores obtidos pelo grupo demonstraram que células BEAS-2B expostas a 1 µM de B[α]P apresentaram alterações das concentrações de metabólitos intracelulares, indução de estresse redox e hipermetilação do DNA. A exposição a 1 µM de nicotinamida ribosídeo (NR), um dos precursores de NAD+, foi capaz de proteger as células BEAS-2B contra a transformação induzida por B[α]P, além de impedir totalmente que células não expostas a B[α]P formassem colônias em soft-agar. A utilização da proteômica neste trabalho permitiu verificar a abundância das proteínas nos quatro diferentes grupos de exposição: Controle, B[α]P, B[α]P + NR e NR. Após 120 h de exposição as células foram coletadas, as proteínas extraídas e preparadas para análise. Foram descobertas 3024 proteínas posteriormente analisadas com o objetivo de elucidar vias possivelmente envolvidas na proteção contra o processo de transfomação maligna. Os grupos NR e Controle demonstram ser mais parecidos em relação ao seu conteúdo, enquanto os grupos B[α]P e B[α]P + NR foram mais semelhantes entre si. A análise de proteínas exclusivas revelou menos processos relacionados ao reparo de DNA no grupo tratado apenas com B[α]P quando comparado com B[α]P + NR. A análise estatística do total de proteínas utilizando o teste ANOVA (p < 0,05, N = 5) revelou 564 proteínas diferencialmente expressas entre os grupos. A clusterização nos permitiu observar a diferença na abundância de proteínas entre os quatro tratamentos. As proteínas estão envolvidas em funções como a regulação do metabolismo, resposta a estresse, transdução de sinal, regulação de expressão gênica e morte celular. Um dos clusters (cluster 1), contendo 59 proteínas, revelou poucos processos na análise de enriquecimento, mas as proteínas contidas nele apresentam funções como controle da divisão celular, apoptose e proteção ao estresse redox. Nele podemos observar que, no geral, o tratamento com B[α]P aumentou a abundância de algumas proteínas, o que foi revertido no grupo B[α]P + NR. O tratamento apenas com NR diminuiu a abundância das proteínas contidas nesse cluster. Outro cluster (cluster 4) apresentou 51 proteínas de abundância diminuída durante a exposição ao B[α]P, o que se reverteu no grupo B[α]P + NR. As proteínas desse cluster estão envolvidas em etapas importantes da via glicolítica, de crescimento, adesão, migração e invasão celular. Apesar de ser descrito que a exposição a NR pode aumentar a eficiência do reparo de DNA, os resultados apresentados nesse trabalho indicam que o efeito protetor pode estar relacionado com a modulação do ciclo celular ou alterações na adesão celular

Responsible for millions of annual deaths and a great health expense, cancer is the second leading cause of death in the world. Among its many types, lung cancer, besides its high incidence, is also one of the most lethal. Exposure to toxic substances resulting from the combustion of organic matter, as well as cigarette consumption, are the mainly responsible for the high incidence of lung cancer. One of these substances is benzo[α]pyrene (B[α]P), a complete carcinogen, able to initiate and promote the carcinogenesis process. Results obtained previously demonstrated that BEAS-2B cells exposed to 1 µM BaP presented alterations in the levels of intracellular metabolites, induction of oxidative stress, and hypermethylation of DNA. The exposure to 1 µM nicotinamide riboside (NR), one of the precursors of NAD+, was able to protect BEAS-2B cells against the transformation induced by B[α]P, moreover, it also totally prevented the colonies formation on soft agar in cells not exposed to B[α]P. The use of proteomics allowed us to verify the abundance of proteins in the four different exposure groups: Control, B[α]P, B[α]P + NR e NR. After 120h of exposure, the cells were collected followed by the extraction of the proteins. A total of 3024 proteins were identified and analyzed aiming to elucidate possible pathways involved in the protective effect against the malignant transformation induced by B[α]P. The NR and Control groups showed to be more similar, while B[α]P and B[α]P + NR were more similar. The analysis of exclusive proteins revealed fewer processes related to DNA repair in B[α]P when compared with B[α]P + NR. The statistical analysis of the total proteins using the ANOVA test (p <0.5, N = 5) revealed 564 proteins differentially expressed between the groups. The heatmap showed the difference in protein abundance between the four treatments. Proteins are involved in functionssuch asthe regulation of metabolism, stress response, signal transduction, regulation of gene expression, and cell death. One of the clusters (cluster 1), containing 59 proteins, revealed a few processes in the enrichment analysis, but the proteins contained in it have functions such as control of cell division, apoptosis, and protection from redox stress. It is possible to observe, in general, treatment with B[α]P increased the abundance of some proteins, which was partially reversed in group B[α]P + NR. On the other hand, the NR treatment decreased the abundance of proteins contained in this cluster. Another cluster (cluster 4) showed 51 proteins of decreased abundance during exposure to B [α] P, which was partially reversed in group B[α]P + NR. The proteins in this cluster are involved in important stages of the glycolytic pathway, also in growth, adhesion, migration, and cell invasion. Although it has been described that exposure to NR can increase the efficiency of DNA repair, the results presented in this work indicate that the protective effect may be related to the modulation of the cell cycle or cell adehsion modifications

Proteomic characterization of Naja mandalayensis venom

Background Naja mandalayensis is a spitting cobra from Myanmar. To the best of our knowledge, no studies on this venom composition have been conducted so far. On the other hand, few envenomation descriptions state that it elicits mainly local inflammation in the victims' eyes, the preferred target of this spiting cobra. Symptoms would typically include burning and painful sensation, conjunctivitis, edema and temporary loss of vision. Methods We have performed a liquid-chromatography (C18-RP-HPLC) mass spectrometry (ESI-IT-TOF/MS) based approach in order to biochemically characterize N. mandalayensis venom. Results A wide variety of three-finger toxins (cardiotoxins) and metallopeptidases were detected. Less abundant, but still representative, were cysteine-rich secretory proteins, L-amino-acid oxidases, phospholipases A2, venom 5'-nucleotidase and a serine peptidase inhibitor. Other proteins were present, but were detected in a relatively small concentration. Conclusion The present study set the basis for a better comprehension of the envenomation from a molecular perspective and, by increasing the interest and information available for this species, allows future venom comparisons among cobras and their diverse venom proteins.(AU)

Body fluid identification by mass spectrometry.

Standard methods for body fluid identification typically rely on detection of the functional proteins specific to or enriched in them, such as hemoglobin in blood, alkaline phosphatase and PSA in semen, or α-amylase in saliva. While these markers can be relatively specific, the multiple methods used to identify them frequently rely on nonspecific chemical, enzymatic, or antibody reactions that usually require the structural integrity of the markers and are not confirmatory because other proteins or substances can also give positive test results. Recent advances in proteomics and mass spectrometry offer the ability to simultaneously detect multiple body fluid protein markers in a single, confirmatory test. Here, multiple markers for blood, saliva, and semen are identified by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS). Data demonstrate the ability to detect these body fluids at nanoliter to subnanoliter levels and to distinguish mixtures. Protein stability of mock samples assayed after 16 months showed no diminution of signal. Because multiple peptides from multiple protein markers are detected and effectively sequenced by MALDI MS/MS, the assay is confirmatory. As mass spectrometry detects whatever peptides are present in a sample, no a priori knowledge of an unknown stain is necessary to perform the test.

Protein biomarkers for in vitro testing of toxicology.

The vision of the toxicology in the 21st century movement is to overcome the currently used animal tests and identify molecular pathways of toxicity, using human in vitro systems with the aim to provide the most relevant mechanistic information for human risk assessment. It is expected to translate key surrogate biomarkers to novel types of toxicity-related high throughput screening of the many thousands of compounds which need to be tested during development phases of the pharmaceutical industry and with regard to the REACH legislation in Europe. Systems biology, an emerging and increasingly popular field of research, appears to be the discipline of choice to integrate results from transcriptomics, proteomics, epigenomics and metabonomics technologies used to analyze samples from toxicological models. The challenges, however, with respect to data generation, statistical treatment, bioinformatic integration and interpretation or in silico modeling remain formidable. One of the main difficulties is the fact that the sheer number of molecular species is inflated enormously in the course of translation from genes to proteins due to post-translational modifications. Moreover, at the level of proteins, time scales of cellular reactions to toxic insults can be very fast, ranging from milliseconds to seconds. Linear dynamic ranges of concentration differences between conditions can also differ by several orders of magnitude. So, the search for protein biomarkers of toxicity requires sophisticated strategies for time-resolved quantitative differential approaches. The statistical principles, normalization of primary data and principal component and cluster analysis have been well developed for genomics/transcriptomics and partly for proteomics, but have not been widely adapted to technologies like metabonomics. Also, the integration of functional data, in particular data from mass spectrometry, with the aim of modeling pathways of toxicity for human risk assessment, is still at an infant stage.

Rotation of random forests for genomic and proteomic classification problems.

Random Forests have been recently widely used for different kinds of classification problems. One of them is classification of gene expression samples that is known as a problem with extremely high dimensionality, and therefore demands suited classification techniques. Due to its strong robustness with respect to large feature sets, Random Forests show significant increase of accuracy in comparison to other ensemble-based classifiers that were widely used before its introduction. In this chapter, we present another ensemble of decision trees called Rotation Forest and evaluate its classification performance on different microarray datasets. Rotation Forest can also be applied to different already existing ensembles of classifiers like Random Forest to improve their accuracy and robustness. This study presents evaluation of Rotation Forest classification technique based on decision trees as base classifiers and was evaluated on 14 different datasets with genomic and proteomic data. It is evident that Rotation Forest as well as the proposed rotation of Random Forests outperform most widely used ensembles of classifiers including Random Forests on majority of datasets.

Comparative proteomic analysis of human oral fluids according to gender and age.

BACKGROUND: Recently, interest in finding disease bio-markers in human body fluids including oral fluids (OF), mainly saliva has increased. However, the physiologic differences in salivary proteins according to gender and age should be explored to establish a clinical diagnostic tool. OBJECTIVE: To compare OF protein expression according to gender and age, using proteomic approaches. MATERIALS AND METHODS: Oral fluids from 27 healthy volunteers (14 males, 13 females) was collected and divided into three age-groups. OF proteins were separated by means of 2D-SDS-PAGE. A total of 51 proteins in 37 protein spots were identified by ESI-MS/MS. RESULTS: Gender differences revealed six proteins with significant higher expression in females, including ß-2-microglobulin and transferrin. Age differences revealed decrease in expression of eight proteins with aging among males and seven proteins differentially expressed with aging among females including prolactin inducible protein, Ig-k light chain, transferrin, and calgranulin-B. CONCLUSION: Proteomic analysis of OF revealed differences in protein expression according to gender and age and therefore can highlight future use of this technique for diagnostic purposes in health and in disease.

Proteomic characterization of the thermostable toxins from Naja naja venom

Naja naja snake venom presents abundant thermostable peptides. Many of them possess useful pharmacological activity that may be employed for drug development. For the proteomic characterization of such toxins, in the present study, Naja naja venom solution was heated up to 100°C for 10, 30, 60, 120, 180 and 300 minutes and protein fractions of non-heated and heated venom were analyzed by two-dimensional nano-liquid chromatography coupled online with tandem mass spectrometry. After heating for 300 minutes, a total of 32 peptides were still detected in the supernatant. The identified peptides belong to the following groups: cardiotoxins, neurotoxins and cytotoxins. It was found that thermostable peptides are able to preserve their analgesic activity after a long heating time in formalin test. Mice injected with 15 µg/g of 60-minute heated venom or with 25 µg/g of 300-minute heated venom revealed even a better analgesic activity than those treated with lidocaine.(AU)

Assessing the statistical validity of proteomics based biomarkers.

A strategy is presented for the statistical validation of discrimination models in proteomics studies. Several existing tools are combined to form a solid statistical basis for biomarker discovery that should precede a biochemical validation of any biomarker. These tools consist of permutation tests, single and double cross-validation. The cross-validation steps can simply be combined with a new variable selection method, called rank products. The strategy is especially suited for the low-samples-to-variables-ratio (undersampling) case, as is often encountered in proteomics and metabolomics studies. As a classification method, principal component discriminant analysis is used; however, the methodology can be used with any classifier. A dataset containing serum samples from Gaucher patients and healthy controls serves as a test case. Double cross-validation shows that the sensitivity of the model is 89% and the specificity 90%. Potential putative biomarkers are identified using the novel variable selection method. Results from permutation tests support the choice of double cross-validation as the tool for determining error rates when the modelling procedure involves a tuneable parameter. This shows that even cross-validation does not guarantee unbiased results. The validation of discrimination models with a combination of permutation tests and double cross-validation helps to avoid erroneous results which may result from the undersampling.

A wavelet-based data pre-processing analysis approach in mass spectrometry.

Recently, mass spectrometry analysis has a become an effective and rapid approach in detecting early-stage cancer. To identify proteomic patterns in serum to discriminate cancer patients from normal individuals, machine-learning methods, such as feature selection and classification, have already been involved in the analysis of mass spectrometry (MS) data with some success. However, the performance of existing machine learning methods for MS data analysis still needs improving. The study in this paper proposes a wavelet-based pre-processing approach to MS data analysis. The approach applies wavelet-based transforms to MS data with the aim of de-noising the data that are potentially contaminated in acquisition. The effects of the selection of wavelet function and decomposition level on the de-noising performance have also been investigated in this study. Our comparative experimental results demonstrate that the proposed de-noising pre-processing approach has potentials to remove possible noise embedded in MS data, which can lead to improved performance for existing machine learning methods in cancer detection.

Proteomic classification of pancreatic adenocarcinoma tissue using protein chip technology.

BACKGROUND & AIMS: Pancreatic adenocarcinoma is a most devastating cancer that presents late and is rapidly progressive. This study aimed to identify unique, tissue-specific protein biomarkers capable of differentiating pancreatic adenocarcinoma (PC) from adjacent uninvolved pancreatic tissue (AP), benign pancreatic disease (B), and nonmalignant tumor tissue (NM). METHODS: Tissue samples representing PC (n = 31), AP (n = 44), and B (n = 19) tissue were analyzed on hydrophobic protein chip arrays by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Training models were developed using logistic regression and validated using the 10-fold cross-validation approach. RESULTS: The hydrophobic protein chip array revealed 13 protein peaks differentially expressed between PC and AP (receiver operating characteristic [ROC] area under the curve [AUC], 0.64-0.85), 8 between PC and B (ROC AUC, 0.67-0.78), and 12 between PC and NM tissue (ROC AUC, 0.63-0.81). Logistic regression and cross-validation identified overlapping panels of peaks to develop a training model that distinguished PC from AP (77.4% sensitivity, 84.1% specificity), B (83.9% sensitivity, 78.9% specificity), and NM tissue (58.1% sensitivity, 90.5% specificity). The final panels selected correctly classified 80.6% of PC and 88.6% of AP samples (ROC AUC, 0.92), 93.5% of PC and 89.5% of B samples (ROC AUC, 0.99), and 71.0% of PC and 92.1% of NM samples (ROC AUC, 0.91). CONCLUSIONS: This study used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to discover a number of protein panels that can distinguish effectively between pancreatic adenocarcinoma, benign, and adjacent pancreatic tissue. Identification of these proteins will add to our understanding of the biology of pancreatic cancer. Furthermore, these protein panels may have important diagnostic implications.

Progress in protein structural class prediction and its impact to bioinformatics and proteomics.

The structural class is an important attribute used to characterize the overall folding type of a protein or its domain. Since the concept of protein structural class was developed about 3 decades ago based on a visual inspection of polypeptide chain topologies in a dataset of only 31 gloular proteins, the number of structure-known proteins has been increased rapidly. For example, as of 12-July-2005, the entries deposited into RCSB PDB Protein Data Bank for proteins, peptides, and viruses whose 3-dimensional structures were determined by X-ray and NMR techniques have been increased to 28,920. To properly cover more and more structure-known proteins, some modification and expansion from the original structural classification scheme have been developed. Meanwhile, many different approaches have been proposed for predicting the structural class of proteins. In this review, the new classification schemes are briefly introduced. The attention is focused on the progress in structural class prediction and its impact in stimulating the development of identifying the other attributes of proteins. It is interesting to point out that the development of the latter has actually in turn greatly enriched the power of the former. Also, some promising approaches for the further development of protein structural class prediction are also addressed.

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics.

BACKGROUND: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images. RESULTS: We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5-15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. CONCLUSIONS: The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.

An XML standard for the dissemination of annotated 2D gel electrophoresis data complemented with mass spectrometry results.

BACKGROUND: Many proteomics initiatives require a seamless bioinformatics integration of a range of analytical steps between sample collection and systems modeling immediately assessable to the participants involved in the process. Proteomics profiling by 2D gel electrophoresis to the putative identification of differentially expressed proteins by comparison of mass spectrometry results with reference databases, includes many components of sample processing, not just analysis and interpretation, are regularly revisited and updated. In order for such updates and dissemination of data, a suitable data structure is needed. However, there are no such data structures currently available for the storing of data for multiple gels generated through a single proteomic experiments in a single XML file. This paper proposes a data structure based on XML standards to fill the void that exists between data generated by proteomics experiments and storing of data. RESULTS: In order to address the resulting procedural fluidity we have adopted and implemented a data model centered on the concept of annotated gel (AG) as the format for delivery and management of 2D Gel electrophoresis results. An eXtensible Markup Language (XML) schema is proposed to manage, analyze and disseminate annotated 2D Gel electrophoresis results. The structure of AG objects is formally represented using XML, resulting in the definition of the AGML syntax presented here. CONCLUSION: The proposed schema accommodates data on the electrophoresis results as well as the mass-spectrometry analysis of selected gel spots. A web-based software library is being developed to handle data storage, analysis and graphic representation. Computational tools described will be made available at http://bioinformatics.musc.edu/agml. Our development of AGML provides a simple data structure for storing 2D gel electrophoresis data.

PubMatrix: a tool for multiplex literature mining.

BACKGROUND: Molecular experiments using multiplex strategies such as cDNA microarrays or proteomic approaches generate large datasets requiring biological interpretation. Text based data mining tools have recently been developed to query large biological datasets of this type of data. PubMatrix is a web-based tool that allows simple text based mining of the NCBI literature search service PubMed using any two lists of keywords terms, resulting in a frequency matrix of term co-occurrence. RESULTS: For example, a simple term selection procedure allows automatic pair-wise comparisons of approximately 1-100 search terms versus approximately 1-10 modifier terms, resulting in up to 1,000 pair wise comparisons. The matrix table of pair-wise comparisons can then be surveyed, queried individually, and archived. Lists of keywords can include any terms currently capable of being searched in PubMed. In the context of cDNA microarray studies, this may be used for the annotation of gene lists from clusters of genes that are expressed coordinately. An associated PubMatrix public archive provides previous searches using common useful lists of keyword terms. CONCLUSIONS: In this way, lists of terms, such as gene names, or functional assignments can be assigned genetic, biological, or clinical relevance in a rapid flexible systematic fashion. http://pubmatrix.grc.nia.nih.gov/

A comparative proteomics resource: proteins of Arabidopsis thaliana.

Using an integrative genome annotation pipeline (iGAP) for proteome-wide protein structure and functional domain assignment, we analyzed all the proteins of Arabidopsis thaliana. Three-dimensional structures at the level of the domain are assigned by fold recognition and threading based on a novel fold library that extends common domain classifications. iGAP is being applied to proteins from all available proteomes as part of a comparative proteomics resource. The database is accessible from the web.