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Accessing online health content of high quality and reliability presents challenges. Laypersons cannot easily differentiate trustworthy content from misinformed or manipulated content. This article describes complementary approaches for members of the general public and health professionals to find trustworthy content with as little bias as possible. These include the Khresmoi health search engine (K4E), the Health On the Net Code of Conduct (HONcode) and health trust indicator Web browser extensions.

The SWISS-2DPAGE database (http: //www.expasy.ch/ch2d/ch2d-top.html ) consists of two-dimensional polyacrylamide gel electrophoresis images, as well as textual descriptions of the proteins that have been identified on them. The current release contains 15 reference maps from human biological samples, as well as from Saccharomyces cerevisiae , Escherichia coli and Dictyostelium discoideum origin. These reference maps have 2088 identified spots, corresponding to 410 separate protein entries in the database, in addition to virtual entries for each SWISS-PROT sequence.

SWISS-2DPAGE (http://www.expasy.ch/ch2d/) is an annotated two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) database established in 1993. The current release contains 21 reference maps from human and mouse biological samples, as well as from Saccharomyces cerevisiae, Escherichia coli and Dictyostelium discoideum origin. These reference maps now have 2480 identified spots, corresponding to 528 separate protein entries in the database, in addition to virtual entries for each SWISS-PROT sequence. During the last year, the SWISS-2DPAGE has undergone major changes. Six new maps have been added, and new functions to access the data have been provided through the ExPASy server. Finally, an important change concerns the database funding source.

Millions of life scientists across the world rely on bioinformatics data resources for their research projects. Data resources can be very expensive, especially those with a high added value as the expert-curated knowledgebases. Despite the increasing need for such highly accurate and reliable sources of scientific information, most of them do not have secured funding over the near future and often depend on short-term grants that are much shorter than their planning horizon. Additionally, they are often evaluated as research projects rather than as research infrastructure components. In this work, twelve funding models for data resources are described and applied on the case study of the Universal Protein Resource (UniProt), a key resource for protein sequences and functional information knowledge. We show that most of the models present inconsistencies with open access or equity policies, and that while some models do not allow to cover the total costs, they could potentially be used as a complementary income source. We propose the Infrastructure Model as a sustainable and equitable model for all core data resources in the life sciences. With this model, funding agencies would set aside a fixed percentage of their research grant volumes, which would subsequently be redistributed to core data resources according to well-defined selection criteria. This model, compatible with the principles of open science, is in agreement with several international initiatives such as the Human Frontiers Science Program Organisation (HFSPO) and the OECD Global Science Forum (GSF) project. Here, we have estimated that less than 1% of the total amount dedicated to research grants in the life sciences would be sufficient to cover the costs of the core data resources worldwide, including both knowledgebases and deposition databases.

The Health On the Net Foundation (HON) was born in 1996, during the beginning of the World Wide Web, from a collective decision by health specialists, led by the late Jean-Raoul Scherrer, who anticipated the need for online trustworthy health information. Because the Internet is a free space that everyone shares, a search for quality information is like a shot in the dark: neither will reliably hit their target. Thus, HON was created to promote deployment of useful and reliable online health information, and to enable its appropriate and efficient use. Two decades on, HON is the oldest and most valued quality marker for online health information. The organization has maintained its reputation through dynamic measures, innovative endeavors and dedication to upholding key values and goals. This paper provides an overview of the HON Foundation, and its activities, challenges, and achievements over the years.

Bioinformatics tools for proteomics, also called proteome informatics tools, span today a large panel of very diverse applications ranging from simple tools to compare protein amino acid compositions to sophisticated software for large-scale protein structure determination. This review considers the available and ready to use tools that can help end-users to interpret, validate and generate biological information from their experimental data. It concentrates on bioinformatics tools for 2-DE analysis, for LC followed by MS analysis, for protein identification by PMF, by peptide fragment fingerprinting and by de novo sequencing and for data quantitation with MS data. It also discloses initiatives that propose to automate the processes of MS analysis and enhance the quality of the obtained results.

Protein identification by tandem mass spectrometry (MS/MS) is key to most proteomics projects and has been widely explored in bioinformatics research. Obtaining good and trustful identification results has important implications for biological and clinical work. Although well matured, automated software identification of proteins from MS/MS data still faces a number of obstacles due to the complexity of the proteome or procedural issues of mass spectrometry data acquisition. Expected or unexpected modifications of the peptide sequences, polymorphisms, errors in databases, missed or non-specific cleavages, unusual fragmentation patterns, and single MS/MS spectra of multiple peptides of the same m/z are so many pitfalls for identification algorithms. A lot of research work has been carried out in recent years that yielded new strategies to handle a number of these issues. Multiple MS/MS identification algorithms are now available or have been theoretically described. The difficulty resides in choosing the most adapted method for each type of spectra being identified. This review presents an overview of the state-of-the-art bioinformatics approaches to the identification of proteins by MS/MS to help the reader doing the spadework of finding the right tools among the many possibilities offered. © 2005 Wiley Periodicals, Inc. Mass Spec Rev 25:235–254, 2006

The central dogma of molecular biology has provided a meaningful principle for data integration in the field of genomics. In this context, integration reflects the known transitions from a chromosome to a protein sequence: transcription, intron splicing, exon assembly and translation. There is no such clear principle for integrating proteomics data, since the laws governing protein folding and interactivity are not quite understood. In our effort to bring together independent pieces of information relative to proteins in a biologically meaningful way, we assess the bias of bioinformatics resources and consequent approximations in the framework of small-scale studies. We analyse proteomics data while following both a data-driven (focus on proteins smaller than 10 kDa) and a hypothesis-driven (focus on whole bacterial proteomes) approach. These applications are potentially the source of specialized complements to classical biological ontologies.

In recent years, proteomics research has gained importance due to increasingly powerful techniques in protein purification, mass spectrometry and identification, and due to the development of extensive protein and DNA databases from various organisms. Nevertheless, current identification methods from spectrometric data have difficulties in handling modifications or mutations in the source peptide. Moreover, they have low performance when run on large databases (such as genomic databases), or with low quality data, for example due to bad calibration or low fragmentation of the source peptide. We present a new algorithm dedicated to automated protein identification from tandem mass spectrometry (MS/MS) data by searching a peptide sequence database. Our identification approach shows promising properties for solving the specific difficulties enumerated above. It consists of matching theoretical peptide sequences issued from a database with a structured representation of the source MS/MS spectrum. The representation is similar to the spectrum graphs commonly used by de novo sequencing software. The identification process involves the parsing of the graph in order to emphasize relevant sections for each theoretical sequence, and leads to a list of peptides ranked by a correlation score. The parsing of the graph, which can be a highly combinatorial task, is performed by a bio-inspired algorithm called Ant Colony Optimization algorithm.

Most combinatorial optimization problems cannot be solved exactly. A class of methods, called metaheuristics, has proved its efficiency to give good approximated solutions in a reasonable time. Cooperative metaheuristics are a sub-set of metaheuristics, which implies a parallel exploration of the search space by several entities with information exchange between them. The importance of information exchange in the optimization process is related to the building block hypothesis of evolutionary algorithms, which is based on these two questions: what is the pertinent information of a given potential solution and how this information can be shared? A classification of cooperative metaheuristics methods depending on the nature of cooperation involved is presented and the specific properties of each class, as well as a way to combine them, is discussed. Several improvements in the field of metaheuristics are also given. In particular, a method to regulate the use of classical genetic operators and to define new more pertinent ones is proposed, taking advantage of a building block structured representation of the explored space. A hierarchical approach resting on multiple levels of cooperative metaheuristics is finally presented, leading to the definition of a complete concerted cooperation strategy. Some applications of these concepts to difficult proteomics problems, including automatic protein identification, biological motif inference and multiple sequence alignment are presented. For each application, an innovative method based on the cooperation concept is given and compared with classical approaches. In the protein identification problem, a first level of cooperation using swarm intelligence is applied to the comparison of mass spectrometric data with biological sequence database, followed by a genetic programming method to discover an optimal scoring function. The multiple sequence alignment problem is decomposed in three steps involving several evolutionary processes to infer different kind of biological motifs and a concerted cooperation strategy to build the sequence alignment according to their motif content.

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry has become a valuable tool in proteomics. With the increasing acquisition rate of mass spectrometers, one of the major issues is the development of accurate, efficient and automatic peptide mass fingerprinting (PMF) identification tools. Current tools are mostly based on counting the number of experimental peptide masses matching with theoretical masses. Almost all of them use additional criteria such as isoelectric point, molecular weight, PTMs, taxonomy or enzymatic cleavage rules to enhance prediction performance. However, these identification tools seldom use peak intensities as parameter as there is currently no model predicting the intensities based on the physicochemical properties of peptides. In this work, we used standard datamining methods such as classification and regression methods to find correlations between peak intensities and the properties of the peptides composing a PMF spectrum. These methods were applied on a dataset comprising a series of PMF experiments involving 157 proteins. We found that the C4.5 method gave the more informative results for the classification task (prediction of the presence or absence of a peptide in a spectra) and M5' for the regression methods (prediction of the normalized intensity of a peptide peak). The C4.5 result correctly classified 88% of the theoretical peaks; whereas the M5' peak intensities had a correlation coefficient of 0.6743 with the experimental peak intensities. These methods enabled us to obtain decision and model trees that can be directly used for prediction and identification of PMF results. The work performed permitted to lay the foundations of a method to analyze factors influencing the peak intensity of PMF spectra. A simple extension of this analysis could lead to improve the accuracy of the results by using a larger dataset. Additional peptide characteristics or even PMF experimental parameters can also be taken into account in the datamining process to analyze their influence on the peak intensity. Furthermore, this datamining approach can certainly be extended to the tandem mass spectrometry domain or other mass spectrometry derived methods.

The development of high throughput utilities to identify proteins is a major challenge in present research in the field of proteomics. One such utility, the molecular scanner, uses proteins separated by two-dimensional polyacrylamide gel electrophoresis that are digested in the gel and during transfer onto a collecting membrane. After adding a matrix, the membrane is inserted into a matrix-assisted laser desorption/ionization-time of flight mass spectrometer and a peptide mass fingerprint (PMF) is measured for every scanned site. Since the spacing between scanned sites is much smaller than the size of the most abundant protein spots, there is a certain redundancy in the data that was used in an earlier experiment with Escherichia coli [1] to improve mass calibration and PMF identification results. It was observed that the signal intensity of a peptide mass as a function of the position on the membrane showed similar patterns if peptides stemmed from the same protein. Taking account of these similarities a clustering algorithm was used to find lists of experimental masses with similar intensity distributions, which provided clearer identification of the corresponding proteins. Here, these methods are applied to a human plasma scan, where proteins were highly modified and less separated. The presence of very abundant proteins like albumin and immunoglobulins added another difficulty. The calibration of the initial PMFs was not satisfactory and masses had to be recalibrated. After discarding chemical noise, the membrane was partitioned into regions and for each region protein identification was carried out separately. A new scoring method was used, where the PMF score was multiplied by a factor that measures the similarity of matching peptides. This method proved to be more robust than the method developed in [1] if the region where a protein was found had an extended, nonspherical shape and strong overlap with regions of other proteins. Many proteins annotated on the SWISS-2D PAGE human plasma master gel could be clearly identified and many interesting properties were observed.

The molecular scanner combines protein separation using gel electrophoresis with peptide mass fingerprinting (PMF) techniques to identify proteins in a highly automated manner. Proteins separated in a 2-dimensional polyacrylamide gel (2-D PAGE) are digested in parallel and transferred onto a membrane keeping their relative positions. The membrane is then sprayed with a matrix and inserted into a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometer, which measures a peptide mass fingerprint at each site on the scanned grid. First, visualization of PMF data allows surveying all fingerprints at once and provides very useful information on the presence of chemical noise. Chemical noise is shown to be a potential source for erroneous identifications and is therefore purged from the mass fingerprints. Then, the correlation between neighboring spectra is used to recalibrate the peptide masses. Finally, a method that clusters peptide masses according to the similarity of the spatial distributions of their signal intensities is presented. This method allows discarding many of the false positives that usually go along with PMF identifications and allows identifying many weakly expressed proteins present in the gel.

Escherichia coli is a model organism for biochemical and biological studies as it is one of the best characterised prokaryote. Two-dimensional polyacrylamide gel electrophoresis, computer image analysis and different protein identification techniques gave rise, in 1995, to the Escherichia coli SWISS-2D PAGE database (http://www.expasy.ch/ch2d/). In the E. coli 3.5-10 SWISS-2D PAGE map, 40% of the E. coli proteome was displayed. The present study demonstrated that the use of narrow range pH gradients is able to potentially display up to a few copies of protein per E. coli cell. Moreover, the six new E. coli SWISS-2D PAGE maps (pH 4-5, 4.5-5.5, 5-6, 5.5-6.7, 6-9 and 6-11) presented here displayed altogether more than 70% of the entire E. coli proteome.

A number of two-dimensional electrophoresis (2-DE) reference maps from mouse samples have been established and could be accessed through the internet. An up-to-date list can be found in WORLD-2D PAGE (http://www.expasy.ch/ch2d/2d- index.html), an index of 2-DE databases and services. None of them were established from mouse white and brown adipose tissues, pancreatic islets, liver nuclei and skeletal muscle. This publication describes the mouse SWISS-2D PAGE database. Proteins present in samples of mouse (C57BI/6J) liver, liver nuclei, muscle, white and brown adipose tissue and pancreatic islets are assembled and described in an accessible uniform format. SWISS-2D PAGE can be accessed through the World Wide Web (WWW) network on the ExPASy molecular biology server (http://www.expasy.ch/ ch2d/).

We have developed a new algorithm to identify proteins by means of peptide mass fingerprinting. Starting from the matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) spectra and environmental data such as species, isoelectric point and molecular weight, as well as chemical modifications or number of missed cleavages of a protein, the program performs a fully automated identification of the protein. The first step is a peak detection algorithm, which allows precise and fast determination of peptide masses, even if the peaks are of low intensity or they overlap. In the second step the masses and environmental data are used by the identification algorithm to search in protein sequence databases (SWISS-PROT and/or TrEMBL) for protein entries that match the input data. Consequently, a list of candidate proteins is selected from the database, and a score calculation provides a ranking according to the quality of the match. To define the most discriminating scoring calculation we analyzed the respective role of each parameter in two directions. The first one is based on filtering and exploratory effects, while the second direction focuses on the levels where the parameters intervene in the identification process. Thus, according to our analysis, all input parameters contribute to the score, however with different weights. Since it is difficult to estimate the weights in advance, they have been computed with a generic algorithm, using a training set of 91 protein spectra with their environmental data. We tested the resulting scoring calculation on a test set of ten proteins and compared the identification results with those of other peptide mass fingerprinting programs.

The peptide mass fingerprinting technique is commonly used for identifying proteins analyzed by mass spectrometry (MS) after enzymatic digestion. Our goal is to build a theoretical model that predicts the mass spectra of such digestion products in order to improve the identification and characterization of proteins using this technique. We present here the first step towards a full MS model. We have modeled MS spectra using the atomic composition of peptides and evaluated the influence that this composition may have on the MS signals. Peptides deduced from the SWISS-PROT protein sequence database were used for the calculation. To validate the model, the variability of the peptide mass distribution in SWISS-PROT was compared to two theoretical, randomly generated databases. Functions have been built that describe the behavior of the isotopic distribution according to the mass of peptides. The variability of these functions was analyzed. In particular, the influence of sulfur was studied. This work, while representing only a first step in the construction of an MS model, yields immediate practical results, as the new isotopic distribution model significantly improves peak detection in MS spectra used by protein identification algorithms.

This paper describes the set of two-dimensional electrophoresis (2-DE) resources currently available from the ExPASy proteomics Web server. These resources include the SWISS-2DPAGE database, 2-DE software packages, 2-DE technical and educational services, as well as indexes and search engines for 2-DE related sites over the Internet.

Identification and characterization of all proteins expressed by a genome in biological samples represent major challenges in proteomics. Today's commonly used high-throughput approaches combine two-dimensional electrophoresis (2-DE) with peptide mass fingerprinting (PMF) analysis. Although automation is often possible, a number of limitations still adversely affect the rate of protein identification and annotation in 2-DE databases: the sequential excision process of pieces of gel containing protein; the enzymatic digestion step; the interpretation of mass spectra (reliability of identifications); and the manual updating of 2-DE databases. We present a highly automated method that generates a fully annoated 2-DE map. Using a parallel process, all proteins of a 2-DE are first simultaneously digested proteolytically and electro-transferred onto a poly(vinylidene difluoride) membrane. The membrane is then directly scanned by MALDI-TOF MS. After automated protein identification from the obtained peptide mass fingerprints using PeptIdent software (http://www.expasy.ch/tools/peptident.html + ++), a fully annotated 2-D map is created on-line. It is a multidimensional representation of a proteome that contains interpreted PMF data in addition to protein identification results. This "MS-imaging" method represents a major step toward the development of a clinical molecular scanner.

Internet has become one of the most used communication media. This and the fact that no constraining information publishing policy exists have created an urgent need to control the quality of information circulating through this media. To this purpose, the Health On the Net Foundation has initiated the Code of Conduct (HONcode) for the health/medical domain. This initiative proposes guidelines to information providers, with the aim, on the one hand, of raising the quality of data available on the Net and, on the other hand, of helping to identify Internet sites that are maintained by qualified people and contain reliable data. The HONcode mainly includes the following ethical aspects: the author's credentials, the date of the last modification with respect to clinical documents, confidentiality of data, source data reference, funding and the advertising policy. This article presents the HONcode and its evolution since it was launched in 1996.

The World-Wide Web is an unstructured, multimedia and multilingual information network. While most efforts have addressed the structuring issue, very few attempts have been proposed to provide support for multilingual information retrieval. Yet, medical information is now available all over the world. The MARVIN (Multi-Agent Retrieval Vagabond on Information Network) softbot and its associated medical search engine MedHunt (Medical Hunter) are a solution for helping people, who only understand a few languages, to access multilingual information.