Search

The integration of oilfield multidisciplinary ontology is increasingly important for the growth of the Semantic Web. However, current methods encounter performance bottlenecks either in storing data and searching for information when processing large amounts of data. To overcome these challenges, we propose a domain-ontology process based on the Neo4j graph database. In this paper, we focus on data storage and information retrieval of oilfield ontology. We have designed mapping rules from ontology files to regulate the Neo4j database, which can greatly reduce the required storage space. A two-tier index architecture, including object and triad indexing, is used to keep loading times low and match with different patterns for accurate retrieval. Therefore, we propose a retrieval method based on this architecture. Based on our evaluation, the retrieval method can save 13.04% of the storage space and improve retrieval efficiency by more than 30 times compared with the methods of relational databases. [ABSTRACT FROM AUTHOR]

Myocardial infarction sometimes appears misspelt as myocardial infraction in the cardiovascular research literature. With accurate citation of literature contributions important to advancing the field and its contributors, in this study we aimed to document the frequency and explore the causes and impact of this error specific to the cardiology literature. Literature databases (PubMed, Scopus, Web of Science, WIPO, Google Scholar, Google) were searched to identify the rate of myocardial infraction errors and found an error rate between 0.2% and 1.6%, with substantial differences between search tools used. A Scopus search was used to show changes in errors over time, differences between journals and by specific authors. Myocardial infraction occurred at increasing annual rates over time and at higher rates than other errors. Increasing error rates were associated with increased volume of searchable material rather than quality of the literature. Transcription from article to database is a common source of error and some databases have higher rates of these errors. Simple measures to avoid and to correct these errors in the literature and the databases are also discussed. [ABSTRACT FROM AUTHOR]

The Encyclopedia of DNA elements (ENCODE) project is an ongoing collaborative effort to create a comprehensive catalog of functional elements initiated shortly after the completion of the Human Genome Project. The current database exceeds 6500 experiments across more than 450 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the H. sapiens and M. musculus genomes. All ENCODE experimental data, metadata, and associated computational analyses are submitted to the ENCODE Data Coordination Center (DCC) for validation, tracking, storage, unified processing, and distribution to community resources and the scientific community. As the volume of data increases, the identification and organization of experimental details becomes increasingly intricate and demands careful curation. The ENCODE DCC has created a general purpose software system, known as SnoVault, that supports metadata and file submission, a database used for metadata storage, web pages for displaying the metadata and a robust API for querying the metadata. The software is fully open-source, code and installation instructions can be found at: (for the generic database) and to store genomic data in the manner of ENCODE. The core database engine, SnoVault (which is completely independent of ENCODE, genomic data, or bioinformatic data) has been released as a separate Python package. [ABSTRACT FROM AUTHOR]

Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases. [ABSTRACT FROM AUTHOR]

Objective: To describe the development of a novel on-line database aimed to serve as a source of information concerning healthcare interventions appraised for their clinical value and appropriateness by several initiatives worldwide, and to present a retrospective analysis of the appraisals already included in the database. Methods and Findings: Database development and a retrospective analysis. The database DianaHealth.com is already on-line and it is regularly updated, independent, open access and available in English and Spanish. Initiatives are identified in medical news, in article references, and by contacting experts in the field. We include appraisals in the form of clinical recommendations, expert analyses, conclusions from systematic reviews, and original research that label any health care intervention as low-value or inappropriate. We obtain the information necessary to classify the appraisals according to type of intervention, specialties involved, publication year, authoring initiative, and key words. The database is accessible through a search engine which retrieves a list of appraisals and a link to the website where they were published. DianaHealth.com also provides a brief description of the initiatives and a section where users can report new appraisals or suggest new initiatives. From January 2014 to July 2015, the on-line database included 2940 appraisals from 22 initiatives: eleven campaigns gathering clinical recommendations from scientific societies, five sets of conclusions from literature review, three sets of recommendations from guidelines, two collections of articles on low clinical value in medical journals, and an initiative of our own. Conclusions: We have developed an open access on-line database of appraisals about healthcare interventions considered of low clinical value or inappropriate. DianaHealth.com could help physicians and other stakeholders make better decisions concerning patient care and healthcare systems sustainability. Future efforts should be focused on assessing the impact of these appraisals in the clinical practice. [ABSTRACT FROM AUTHOR]

The statistical validation of database search results is a complex issue in bottom-up proteomics. The correct and incorrect peptide spectrum match (PSM) scores overlap significantly, making an accurate assessment of true peptide matches challenging. Since the complete separation between the true and false hits is practically never achieved, there is need for better methods and rescoring algorithms to improve upon the primary database search results. Here we describe the calibration and False Discovery Rate (FDR) estimation of database search scores through a dynamic FDR calculation method, FlexiFDR, which increases both the sensitivity and specificity of search results. Modelling a simple linear regression on the decoy hits for different charge states, the method maximized the number of true positives and reduced the number of false negatives in several standard datasets of varying complexity (18-mix, 49-mix, 200-mix) and few complex datasets (E. coli and Yeast) obtained from a wide variety of MS platforms. The net positive gain for correct spectral and peptide identifications was up to 14.81% and 6.2% respectively. The approach is applicable to different search methodologies- separate as well as concatenated database search, high mass accuracy, and semi-tryptic and modification searches. FlexiFDR was also applied to Mascot results and showed better performance than before. We have shown that appropriate threshold learnt from decoys, can be very effective in improving the database search results. FlexiFDR adapts itself to different instruments, data types and MS platforms. It learns from the decoy hits and sets a flexible threshold that automatically aligns itself to the underlying variables of data quality and size [ABSTRACT FROM AUTHOR]

Statistically meaningful comparison/combination of peptide identification results from various search methods is impeded by the lack of a universal statistical standard. Providing an E-value calibration protocol, we demonstrated earlier the feasibility of translating either the score or heuristic E-value reported by any method into the textbook-defined E-value, which may serve as the universal statistical standard. This protocol, although robust, may lose spectrum-specific statistics and might require a new calibration when changes in experimental setup occur. To mitigate these issues, we developed a new MS/MS search tool, RAId_aPS, that is able to provide spectrum-specific E-values for additive scoring functions. Given a selection of scoring functions out of RAId score, K-score, Hyperscore and XCorr, RAId_aPS generates the corresponding score histograms of all possible peptides using dynamic programming. Using these score histograms to assign E-values enables a calibration-free protocol for accurate significance assignment for each scoring function. RAId_aPS features four different modes: (i) compute the total number of possible peptides for a given molecular mass range, (ii) generate the score histogram given a MS/MS spectrum and a scoring function, (iii) reassign E-values for a list of candidate peptides given a MS/MS spectrum and the scoring functions chosen, and (iv) perform database searches using selected scoring functions. In modes (iii) and (iv), RAId_aPS is also capable of combining results from different scoring functions using spectrum-specific statistics. The web link is http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/raid_aps/index.html. Relevant binaries for Linux, Windows, and Mac OS X are available from the same page. [ABSTRACT FROM AUTHOR]