A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data

Goal: Most common diseases are influenced by multiple gene interactions and interactions with the environment. Performing an exhaustive search to identify such interactions is computationally expensive and needs to address the multiple testing problem. A four-step framework is proposed for the efficient identification of n-Way interactions. Methods: The framework was applied on a Multiple Sclerosis dataset with 725 subjects and 147 tagging SNPs. The first two steps of the framework are quality control and feature selection. The next step uses clustering and binary encodes the features. The final step performs the n-Way interaction testing. Results: The feature space was reduced to 7 SNPs and using the proposed binary encoding, more 2-SNP and 3-SNP interactions were identified compared to using the initial encoding. Conclusions: The framework selects informative features and with the proposed binary encoding it is able to identify more n-way interactions by increasing the power of the statistical analysis.

Is systemic lupus erythematosus different in urban versus rural living environment? Data from the Cretan Lupus Epidemiology and Surveillance Registry

BACKGROUND

Examining urban-rural differences can provide insights into susceptibility or modifying factors of complex diseases, yet limited data exist on systemic lupus erythematosus (SLE).

OBJECTIVE

To study SLE risk, manifestations and severity in relation to urban versus rural residence.

METHODOLOGY

Cross-sectional analysis of the Crete Lupus Registry. Demographics, residency history and clinical data were obtained from interviews and medical records ( N=399 patients). Patients with exclusively urban, rural or mixed urban/rural residence up to enrolment were compared.

RESULTS

The risk of SLE in urban versus rural areas was 2.08 (95% confidence interval: 1.66-2.61). Compared with rural, urban residence was associated with earlier (by almost seven years) disease diagnosis - despite comparable diagnostic delay - and lower female predominance (6.8:1 versus 15:1). Rural patients had fewer years of education and lower employment rates. Smoking was more frequent among urban, whereas pesticide use was increased among rural patients. A pattern of malar rash, photosensitivity, oral ulcers and arthritis was more prevalent in rural patients. Residence was not associated with organ damage although moderate/severe disease occurred more frequently among rural-living patients (multivariable adjusted odds ratio: 2.17, p=0.011).

CONCLUSION

Our data suggest that the living environment may influence the risk, gender bias and phenotype of SLE, not fully accounted for by sociodemographic factors.

Laying in silico pipelines for drug repositioning: a paradigm in ensemble analysis for neurodegenerative diseases

When faced with time- and money-consuming problems, new practices in pharmaceutical R&D arose when trying to alleviate them. Drug repositioning has great promise and when combined with today's computational power and intelligence it becomes more precise and potent. This work showcases current approaches of creating a computational pipeline for drug repositioning, along with an extensive example of how researchers can influence therapeutic approaches and further understanding, through either single or multiple disease studies. This paradigm is based on three neurodegenerative diseases with pathophysiological similarities. It is our goal to provide the readers with all the information needed to enrich their research and note expectations along the way.

D-Map: Random Walking on Gene Network Inference Maps Towards differential Avenue Discovery

Differential rewiring of cellular interaction networks between disease and healthy state is of great importance. Through a systems level approach, malfunctioned mechanisms that are absent in the normal cases, may enlighten the key-players in terms of genes and their interaction chains related to disease. We have developed D-Map, a publicly available user-friendly web application, capable of generating and manipulating advanced differential networks by combining state-of-the-art inference reconstruction methods with random walk simulations. The inputs are expression profiles obtained from the Gene Expression Omnibus and a gene list under investigation. Differential networks may be visualized and interpreted through the use of D-Map interface, where display of the disease, the normal and the common state can be performed, interactively. A case study scenario concerning Alzheimer's disease, as well as breast, lung, and bladder cancer was conducted in order to demonstrate the usefulness of the proposed methodology to different disease types. Findings were consistent with the current bibliography, and the provided interaction lists may be further explored towards novel biological insights of the investigated diseases. The DMap web-application is available at: http://bioserver-3.bioacademy.gr/Bioserver/DMap/index.php.

mAPKL: R/ Bioconductor package for detecting gene exemplars and revealing their characteristics

BACKGROUND

So far many algorithms have been proposed towards the detection of significant genes in microarray analysis problems. Several of those approaches are freely available as R-packages though their engagement in gene expression analysis by non-bioinformaticians is usually a frustrating task. Besides, only some of those packages offer a complete suite of tools starting from initial data import and ending to analysis report. Here we present an R/Bioconductor package that implements a hybrid gene selection method along with a bunch of functions to facilitate a thorough and convenient gene expression profiling analysis.

RESULTS

mAPKL is an open-source R/Bioconductor package that implements the mAP-KL hybrid gene selection method. The advantage of this method is that selects a small number of gene exemplars while achieving comparable classification results to other well established algorithms on a variety of datasets and dataset sizes. The mAPKL package is accompanied with extra functionalities including (i) solid data import; (ii) data sampling following a user-defined proportion; (iii) preprocessing through several normalization and transformation alternatives; (iv) classification with the aid of SVM and performance evaluation; (v) network analysis of the significant genes (exemplars), including degree of centrality, closeness, betweeness, clustering coefficient as well as the construction of an edge list table; (vi) gene annotation analysis, (vii) pathway analysis and (viii) auto-generated analysis reporting.

CONCLUSIONS

Users are able to run a thorough gene expression analysis in a timely manner starting from raw data and concluding to network characteristics of the selected gene exemplars. Detailed instructions and example data are provided in the R package, which is freely available at Bioconductor under the GPL-2 or later license http://www.bioconductor.org/packages/3.1/bioc/html/mAPKL.html.

Structure-based virtual screening for drug discovery: principles, applications and recent advances

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.

KENeV: A web-application for the automated reconstruction and visualization of the enriched metabolic and signaling super-pathways deriving from genomic experiments

Gene expression analysis, using high throughput genomic technologies,has become an indispensable step for the meaningful interpretation of the underlying molecular complexity, which shapes the phenotypic manifestation of the investigated biological mechanism. The modularity of the cellular response to different experimental conditions can be comprehended through the exploitation of molecular pathway databases, which offer a controlled, curated background for statistical enrichment analysis. Existing tools enable pathway analysis, visualization, or pathway merging but none integrates a fully automated workflow, combining all above-mentioned modules and destined to non-programmer users.

We introduce an online web application, named KEGG Enriched Network Visualizer (KENeV), which enables a fully automated workflow starting from a list of differentially expressed genes and deriving the enriched KEGG metabolic and signaling pathways, merged into two respective, non-redundant super-networks. The final networks can be downloaded as SBML files, for further analysis, or instantly visualized through an interactive visualization module.

In conclusion, KENeV (available online at http://www.grissom.gr/kenev) provides an integrative tool, suitable for users with no programming experience, for the functional interpretation, at both the metabolic and signaling level, of differentially expressed gene subsets deriving from genomic experiments.

Painful vaso-occlusive crisis as a prodromal phase of acute chest syndrome. Is only one chest X-ray enough? A case report

The predominant pathophysiological feature of homozygous sickle cell anemia (SCA) is the vaso-occlusion. Vaso-occlusion can be associated with painful crises, which are the primary reason for those patients to seek medical care. Vaso-occlusion is responsible for the acute chest syndrome (ACS) with large morbidity and mortality or more rarely (and especially in adults) for priapism and acute neurological events (strokes). A 10-year-old boy with homozygous SCA was admitted to the Pediatric Emergencies with painful vaso-occlusive crisis and fever. Initially he had normal chest X-ray but, after 24-hour-hospitalization, he developed ACS with new chest X-ray findings. He was treated with broad spectrum antibiotics, blood transfusions and bronchodilators and after a six-day treatment, he was significantly improved. The patient was discharged 13 days later with no other therapy at home. The possibility of ACS development should be still considered, even when a known patient with SCA presents a painful vaso-occlusive crisis with an initial normal chest X-ray. Therefore, repeated clinical examination is required and possible changes in the clinical status could indicate the necessity of a new radiographic examination. In this way, early ACS could be recognized and the catastrophic consequences due to this syndrome could be avoided.

C-PAmP: large scale analysis and database construction containing high scoring computationally predicted antimicrobial peptides for all the available plant species

Background

Antimicrobial peptides are a promising alternative to conventional antibiotics. Plants are an important source of such peptides; their pharmacological properties are known since antiquity. Access to relevant information, however, is not straightforward, as there are practically no major repositories of experimentally validated and/or predicted plant antimicrobial peptides. PhytAMP is the only database dedicated to plant peptides with confirmed antimicrobial action, holding 273 entries. Data on such peptides can be otherwise retrieved from generic repositories.

Description

We present C-PAmP, a database of computationally predicted plant antimicrobial peptides. C-PAmP contains 15,174,905 peptides, 5–100 amino acids long, derived from 33,877 proteins of 2,112 plant species in UniProtKB/Swiss-Prot. Its web interface allows queries based on peptide/protein sequence, protein accession number and species. Users can view the corresponding predicted peptides along with their probability score, their classification according to the Collection of Anti-Microbial Peptides (CAMP), and their PhytAMP id where applicable. Moreover, users can visualise protein regions with a high concentration of predicted antimicrobial peptides. In order to identify potential antimicrobial peptides we used a classification algorithm, based on a modified version of the pseudo amino acid concept. The classifier tested all subsequences ranging from 5 to 100 amino acids of the plant proteins in UniProtKB/Swiss-Prot and stored those classified as antimicrobial with a high probability score (>90%). Its performance measures across a 10-fold cross-validation are more than satisfactory (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90) and it succeeded in classifying 99.5% of the PhytAMP peptides correctly.

Conclusions

We have compiled a major repository of predicted plant antimicrobial peptides using a highly performing classification algorithm. Our repository is accessible from the web and supports multiple querying options to optimise data retrieval. We hope it will greatly benefit drug design research by significantly limiting the range of plant peptides to be experimentally tested for antimicrobial activity.

BiDaS: a web-based Monte Carlo BioData Simulator based on sequence/feature characteristics

BiDaS is a web-application that can generate massive Monte Carlo simulated sequence or numerical feature data sets (e.g. dinucleotide content, composition, transition, distribution properties) based on small user-provided data sets. BiDaS server enables users to analyze their data and generate large amounts of: (i) Simulated DNA/RNA and aminoacid (AA) sequences following practically identical sequence and/or extracted feature distributions with the original data. (ii) Simulated numerical features, presenting identical distributions, while preserving the exact 2D or 3D between-feature correlations observed in the original data sets. The server can project the provided sequences to multidimensional feature spaces based on: (i) 38 DNA/RNA features describing conformational and physicochemical nucleotide sequence features from the B-DNA-VIDEO database, (ii) 122 DNA/RNA features based on conformational and thermodynamic dinucleotide properties from the DiProDB database and (iii) Pseudo-aminoacid composition of the initial sequences. To the best of our knowledge, this is the first available web-server that allows users to generate vast numbers of biological data sets with realistic characteristics, while keeping between-feature associations. These data sets can be used for a wide variety of current biological problems, such as the in-depth study of gene, transcript, peptide and protein groups/families; the creation of large data sets from just a few available members and the strengthening of machine learning classifiers. All simulations use advanced Monte Carlo sampling techniques. The BiDaS web-application is available at http://bioserver-3.bioacademy.gr/Bioserver/BiDaS/.

How accurate are diagnostic tools for Epstein-Barr virus (EBV) to establish causal association of an uncommon clinical condition with EBV?

Epstein-Barr virus (EBV) infection has been implicated as a possible cause of a wide range of clinical conditions in children and young adults. In uncommon clinical conditions, where clinical experience is missing, it is important to evaluate both the biological plausibility and the virological basis that substantiates their causal association with EBV. By reviewing the diagnostic procedures performed in the diagnosis of EBV infection in case reports concerning uncommon clinical conditions causally related to EBV infection in children and young adults, the aim of the present study was to discuss the limitations of the diagnostic procedure used to establish EBV diagnosis, which may cause false-positive results and compromise the reliability of such a diagnosis. We should be aware not only of the nuances of serological tests and virus detection tests for latent viruses such as EBV, but also of the risk of using them alone or in combination with molecular methods as the sole mean for establishing a causal relation between EBV infection and an uncommon clinical condition. Accurate laboratory tests for EBV detection, strict criteria for EBV infection diagnosis, and a cumulative clinical experience coupled with biological plausibility and experimental data are needed to avoid a possible coincidental association between several clinical manifestations, mainly uncommon clinical conditions, and EBV infection.

KEYWORDS

Epstein-Barr virus; diagnostics; uncommon condition.

ChemBioServer: a web-based pipeline for filtering, clustering and visualization of chemical compounds used in drug discovery

SUMMARY

ChemBioServer is a publicly available web application for effectively mining and filtering chemical compounds used in drug discovery. It provides researchers with the ability to (i) browse and visualize compounds along with their properties, (ii) filter chemical compounds for a variety of properties such as steric clashes and toxicity, (iii) apply perfect match substructure search, (iv) cluster compounds according to their physicochemical properties providing representative compounds for each cluster, (v) build custom compound mining pipelines and (vi) quantify through property graphs the top ranking compounds in drug discovery procedures. ChemBioServer allows for pre-processing of compounds prior to an in silico screen, as well as for post-processing of top-ranked molecules resulting from a docking exercise with the aim to increase the efficiency and the quality of compound selection that will pass to the experimental test phase.

AVAILABILITY

The ChemBioServer web application is available at: http://bioserver-3.bioacademy.gr/Bioserver/ChemBioServer/.

CONTACT

gspyrou@bioacademy.gr

Comprehensive field synopsis and systematic meta-analyses of genetic association studies in cutaneous melanoma

BACKGROUND

Although genetic studies have reported a number of loci associated with cutaneous melanoma (CM) risk, a comprehensive synopsis of genetic association studies published in the field and systematic meta-analysis for all eligible polymorphisms have not been reported.

METHODS

We systematically annotated data from all genetic association studies published in the CM field (n = 145), including data from genome-wide association studies (GWAS), and performed random-effects meta-analyses across all eligible polymorphisms on the basis of four or more independent case-control datasets in the main analyses. Supplementary analyses of three available datasets derived from GWAS and GWAS-replication studies were also done. Nominally statistically significant associations between polymorphisms and CM were graded for the strength of epidemiological evidence on the basis of the Human Genome Epidemiology Network Venice criteria. All statistical tests were two-sided.

RESULTS

Forty-two polymorphisms across 18 independent loci evaluated in four or more datasets including candidate gene studies and available GWAS data were subjected to meta-analysis. Eight loci were identified in the main meta-analyses as being associated with a risk of CM (P < .05) of which four loci showed a genome-wide statistically significant association (P < 1 × 10(-7)), including 16q24.3 (MC1R), 20q11.22 (MYH7B/PIGU/ASIP), 11q14.3 (TYR), and 5p13.2 (SLC45A2). Grading of the cumulative evidence by the Venice criteria suggested strong epidemiological credibility for all four loci with genome-wide statistical significance and one additional gene at 9p23 (TYRP1). In the supplementary meta-analyses, a locus at 9p21.3 (CDKN2A/MTAP) reached genome-wide statistical significance with CM and had strong epidemiological credibility.

CONCLUSIONS

To the best of our knowledge, this is the first comprehensive field synopsis and systematic meta-analysis to identify genes associated with an increased susceptibility to CM.

Bioinformatics approaches in the discovery and understanding of reproduction-related biomarkers

The emerging field of bioinformatics in proteomics is introducing new algorithms in order to handle large and heterogeneous datasets and improve the knowledge-discovery process. Management systems, software construction and application, database population and leverage, as well as computed prediction, have crafted bioinformatics into a valuable tool for basic research. Human reproduction is one of many fields proteomics has been extensively studying over the last decade, accumulating complex experimental data at a rate far exceeding the ability to assimilate them. Transformation of the rapidly proliferating quantities of experimental information into a usable form in order to facilitate their analysis is a challenging task. On this track, bioinformatics, an essential part of proteomics research, aspires to amend inquiries into a better manipulated, a better handled and a better understood form so as to enhance existing knowledge expansion.

PepServe: a web server for peptide analysis, clustering and visualization

Peptides, either as protein fragments or as naturally occurring entities are characterized by their sequence and function features. Many times the researchers need to massively manage peptide lists concerning protein identification, biomarker discovery, bioactivity, immune response or other functionalities. We present a web server that manages peptide lists in terms of feature analysis as well as interactive clustering and visualization of the given peptides. PepServe is a useful tool in the understanding of the peptide feature distribution among a group of peptides. The PepServe web application is freely available at http://bioserver-1.bioacademy.gr/Bioserver/PepServe/.

Investigating the minimum required number of genes for the classification of neuromuscular disease microarray data

The discovery of potential microarray markers, which will expedite molecular diagnosis/prognosis and provide reliable results to clinical decision-making and treatment selection for patients, is of paramount importance. Feature selection techniques, which aim at minimizing the dimensionality of the microarray data by keeping the most statistically significant genes, are a powerful approach toward this goal. In this paper, we investigate the minimum required subsets of genes, which best classify neuromuscular disease data. For this purpose, we implemented a methodology pipeline that facilitated the use of multiple feature selection methods and subsequent performance of data classification. Five feature selection methods on datasets from ten different neuromuscular diseases were utilized. Our findings reveal subsets of very small number of genes, which can successfully classify normal/disease samples. Interestingly, we observe that similar classification results may be obtained from different subsets of genes. The proposed methodology can expedite the identification of small gene subsets with high-classification accuracy that could ultimately be used in the genetics clinics for diagnostic, prognostic, and pharmacogenomic purposes.

Can electronic zoom replace magnification in mammography? A comparative Monte Carlo study

Magnification, which is considered to be a relatively high "dose cost" mammographic technique, is a complementary examination performed on women exhibiting breast complaints or abnormalities. Particular attention is given to the imaging procedure as the primary aim is to confirm the existence of suspected abnormalities, despite the additional dose. The introduction of post-processing capabilities and the widespread use of digital mammography promoted some controversy in the last decades on whether electronic zoom performed on the derived initial screening mammogram can effectively replace this technique. This study used Monte Carlo simulation methods to derive simulated screening mammograms produced under several exposure conditions, aiming to electronically magnify and compare them to the corresponding magnification mammograms. Comparison was based on quantitative measurements of image quality, namely contrast to noise ratio (CNR) and spatial resolution. Results demonstrated that CNR was higher for geometric magnification compared to the case of electronic zooming. The percentage difference was higher for lesions of smaller radius and achieved 29% for 0.10 mm details. Although spatial resolution is maintained high in the zoomed images, when investigating microcalcifications of 0.05 mm radius or less, only with geometric magnification can they be visualised.

Studying the properties of the updating coefficients in the OSEM algorithm for iterative image reconstruction in PET

We have studied the properties of the pixel updating coefficients in the 2D ordered subsets expectation maximization (OSEM) algorithm for iterative image reconstruction in positron emission tomography, in order to address the problem of image quality degradation-a known property of the technique after a number of iterations. The behavior of the updating coefficients has been extensively analyzed on synthetic coincidence data, using the necessary software tools. The experiments showed that the statistical properties of these coefficients can be correlated with the quality of the reconstructed images as a function of the activity distribution in the source and the number of subsets used. Considering the fact that these properties can be quantified during the reconstruction process of data from real scans where the activity distribution in the source is unknown the results of this study might be useful for the development of a stopping criterion for the OSEM algorithm.

A two-dimensional proteomic profile of Tetrahymena thermophila whole cell lysate

Tetrahymena thermophila is a unicellular eukaryotic model organism used for a variety of biochemical, molecular and biological studies. According to its macronucleus genome sequence, it is expected to contain more than 27,000 protein-coding genes, although only a small proportion of them have information published specifically about them. Here, we present a reference map for whole cell lysate of T. thermophila obtained using two-dimensional gel electrophoresis (2-DE) combined with mass spectrometry. Although (2-DE) is one of the most efficient techniques for resolving complex protein mixtures and revealing the relative high-abundance proteins, it has not yet been applied generally to ciliates. In order to obtain qualitative protein samples for analysis, an appropriate homogenization method is required. Optimization of the homogenization method led to the analysis of nearly 4500 protein spots, the final identification of 375 different proteins using Mascot software and an additional 258 gene products using a newly developed web service, called Peptide Finder, resulting in a total of 631 different gene products that are considered to constitute the proteomic profile of the whole cell lysate of T. thermophila.

A similarity network approach for the analysis and comparison of protein sequence/structure sets

A set of proteins is a complex system whose elements are interrelated on the concept of sequence- and structure-based similarity. Here, we applied a similarity network-based methodology for the representation and analysis of protein sequences and structures sets using a non-redundant set of 311 proteins and three different information criteria based on sequence-derived features, sequence local alignment and structural alignment. A wide set of measurements, like network degree, clustering coefficient, characteristic path length and vertex centrality were utilized to characterize the networks' topology. Protein similarity networks were found medium or highly interconnected and the existence of both clusters and random edges classified their fully connected versions as Small World Networks (SWNs). The SWN architecture was able to host the continuous similarity transition among proteins and model the protein information flow during evolution. Recently reported ancestral elements, like the alpha/beta class and certain folds, were remarkably found to act as hubs in the networks. Additionally, the moderate information value of sequence-derived features when used for fold and class assignment was shown on a network basis. The methodology described here can be applied for the analysis of other complex systems which consist of interrelated elements and a certain information flow.

Position dominant sequence elements in experimentally verified human promoters and their putative relation to cancer

Promoter regions of the human genome play a key role in our understanding of the regulatory mechanisms related to the physiological and disease states. The aim of this study was to investigate the sequence positional properties of experimentally verified human promoters. Consequently, we determined short sequence elements ranging from 4 to 9mers presenting position dominance close to, or away from the transcription start site (TSS). For this purpose rigid statistical criteria were used and whether position dominance was in any way related to transcription control was determined. To achieve this goal we designed and implemented a dedicated filtering method to massively detect position-dominant sequence elements embedded in the promoter set. Additionally, via a high throughput procedure, we gathered data on the majority of the publicly available transcription factor-binding sites (TFBSs) and matched them to our findings, aiming to accomplish a large-scale correlation between position-dominant sequence elements and TFBSs. In this analysis, we present unique compositional and conservational perturbations at the TSS and the core promoter region. Using our filtering method, 7,088 short sequences ranging from 4 to 9mers were found to present strong positional dominance close to or away from the TSS, while the aforementioned short sequences were matched to a large number of known TFBSs. Moreover, using probability theory, evidence is presented showing that TFBSs tend to present strong positional preferences. In addition, we demonstrate that the actual TFBS copy number is related to the transcription regulatory process. On the basis of the last argument, it is suggested that all the detected short sequences which did not match any known TFBS, have a high potential for being novel transcription control elements. Furthermore, using a well-described ;high potential cancer biomarker resource', we attempted to identify position dominant sequence elements associated with cancer, as derived by their presence in the respective promoters of cancer related proteins.

UniMaP: finding unique mass and peptide signatures in the human proteome

The uniqueness of a measured molecular mass or peptide sequence plays a very important role in the fields of protein identification and peptide/protein-biomarker investigation. We present a publicly available web application that offers information concerning the uniqueness of one or more molecular masses and one or more peptide sequences in the human proteome. When a sequence is found to be unique in humans, the application is able to search across all species querying whether this sequence is unique, not only in humans but also in other species found in the Swiss-Prot Database. The application is also able to search for unique protein fragments derived computationally from enzymatic digestion driven by certain enzymes. Furthermore, the application can list all the unique masses and peptides of a given protein. Through this application, researchers are able to find unique tags, either on a molecular mass level or on a sequence level. These unique tags are remarkably important in research related to protein identification or biomarker discovery and measurements.

AVAILABILITY

UniMaP web-application is available at http://bioserver-1.bioacademy.gr/Bioserver/UniMaP/

Design of a 'smart' patient record system for mammography patients

One of the most common cancer types among women is breast cancer. Regular mammographic examinations increase the possibility for early diagnosis and treatment and significantly improve the chance of survival for patients with breast cancer. Keeping an informed and complete patient record is of great importance as the doctor needs this information for every patient examination. The proposed implementation is a patient record system that includes 'smart' algorithms in order to automatically use data from the patient's record to calculate well established epidemiological breast cancer models. A computer-aided diagnosis system is also used in order to analyze each mammogram and obtain a certain risk percentage concerning whether the patient has to undergo biopsy or not. The aforementioned system has been implemented to support a large set of patient data (1,178 patients) which included detailed personal patient data, medical history and examination details per date and is used in the daily clinical practice of a breast cancer diagnostic center facilitating patient record retrieval, storage and supporting the doctor's decisions.

Mass spectrometry-based proteomics in reproductive medicine

The emergence of powerful mass spectrometry-based proteomic techniques has added a new dimension to the field of biomedical research. Application of these high throughput methodologies in pregnancy-related pathology has contributed to the comprehension of the underlying pathophysiologies and the successful identification of relevant protein biomarkers that can potentially change early diagnosis and treatment of several medical conditions related to human pregnancy. Most of the existing research on human reproduction and gestation has focused on follicular fluid, cervical/vaginal fluid, and amniotic fluid. Although proteome technologies in reproductive medicine research are not as yet widely applied, characterization of the proteome of reproductive fluids can be expected to significantly improve maternal healthcare. This article aims to summarize the applications of mass spectrometry based technology on the most important and specific biological fluids related to reproduction and gestation.

Peptide Finder: mapping measured molecular masses to peptides and proteins

The identification of unknown amino acid sequences of peptides as well as protein identification is of great significance in proteomics. Here, we present a publicly available web application that facilitates a high resolution mapping of measured molecular masses to peptides and proteins, irrespectively of the enzyme/digestion method used. Furthermore, multi-filtering may be applied in terms of measured mass tolerance, molecular mass and isoelectric point range as well as pattern matching to refine the results. This approach serves complementary to the existing solutions for protein identification and gives insights in novel peptides discovery and protein identification at the cases where the identification scores from the other approaches may be below significance threshold. Peptide Finder has been proven useful in proteomics procedures with experimental data from MALDI-TOF.

AVAILABILITY

Peptide Finder web-application is available at http://bioserver-1.bioacademy.gr/Bioserver/PeptideFinder/.

Energy, angular and spatial distributions of primary electrons inside photoconducting materials for digital mammography: Monte Carlo simulation studies

Materials such as a-Se, a-As(2)Se(3), GaSe, GaAs, Ge, CdTe, CdZnTe, Cd(0.8)Zn(0.2)Te, ZnTe, PbO, TlBr, PbI(2) and HgI(2) are potential candidates as photoconductors in direct detectors for digital mammography. The x-ray induced primary electrons inside a photoconductor's bulk comprise the initial signal that propagates and forms the final signal (image) on the detector's electrodes. An already developed model for a-Se has been properly extended to simulate the primary electron production in the materials mentioned. Primary electron characteristics, such as their energy, angular and spatial distributions that strongly influence the characteristics of the final image, were studied for both monoenergetic and polyenergetic x-ray spectra in the mammographic energy range. The characteristic feature in the electron energy distributions for PbI(2) and HgI(2) is the atomic deexcitation peaks, whereas for the rest of the materials their shape can also be influenced by the electrons produced from primary photons. The electrons have a small tendency to be forward ejected whereas they prefer to be ejected perpendicular (theta = pi/2) to the incident beam's axis and at two lobes around phi = 0 and phi = pi. At practical mammographic energies (15-40 keV) a-Se, a-As(2)Se(3) and Ge have the minimum azimuthal uniformity whereas CdZnTe, Cd(0.8)Zn(0.2)Te and CdTe the maximum one. The spatial distributions for a-Se, a-As(2)Se(3), GaSe, GaAs, Ge, PbO and TlBr are almost independent of the polyenergetic spectrum, while those for CdTe, CdZnTe, Cd(0.8)Zn(0.2)Te, ZnTe, PbI(2) and HgI(2) have a spectrum dependence. In the practical mammographic energy range and at this primitive stage of primary electron production, a-Se has the best inherent spatial resolution as compared to the rest of the photoconductors. PbO has the minimum bulk space in which electrons can be produced whereas CdTe has the maximum one.

A survey of the availability of primary bioinformatics web resources

The explosive growth of the bioinformatics field has led to a large amount of data and software applications publicly available as web resources. However, the lack of persistence of web references is a barrier to a comprehensive shared access. We conducted a study of the current availability and other features of primary bioinformatics web resources (such as software tools and databases). The majority (95%) of the examined bioinformatics web resources were found running on UNIX/Linux operating systems, and the most widely used web server was found to be Apache (or Apache-related products). Of the overall 1,130 Uniform Resource Locators (URLs) examined, 91% were highly available (more than 90% of the time), while only 4% showed low accessibility (less than 50% of the time) during the survey. Furthermore, the most common URL failure modes are presented and analyzed.