Addressing the cost of infractions in the online literature and databases.
Ontology highlight
ABSTRACT:
SUBMITTER: Dilley RJ
PROVIDER: S-EPMC5708810 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
Addressing the cost of infractions in the online literature and databases.
Dilley Rodney J RJ Ash Oliver G OG
PloS one 20171130 11
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 infracti ...[more]
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Project description:Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five "incorrect" targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives.
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Project description:BACKGROUND: When searching for renal literature, nephrologists must choose between several different bibliographic databases. We compared the availability of renal clinical studies in six major bibliographic databases. METHODS: We gathered 151 renal systematic reviews, which collectively contained 2195 unique citations referencing primary studies in the form of journal articles, meeting articles or meeting abstracts published between 1963 and 2008. We searched for each citation in three subscription-free bibliographic databases (PubMed, Google Scholar and Scirus) and three subscription-based databases (EMBASE, Ovid-MEDLINE and ISI Web of Knowledge). For the subscription-free databases, we determined which full-text journal articles were available free of charge via links to the article source. RESULTS: The proportion of journal articles contained within each of the six databases ranged from 96 to 97%; results were similar for meeting articles. Availability of meeting abstracts was poor, ranging from 0 to 37% (P < 0.01) with ISI Web of Knowledge containing the largest proportion [37%, 95% confidence interval (95% CI) 32-43%]. Among the subscription-free databases, free access to full-text articles was highest in Google Scholar (38% free, 95% CI 36-41%), and was only marginally higher (39%) when all subscription-free databases were searched. After 2000, free access to full-text articles increased to 49%. CONCLUSIONS: Over 99% of renal clinical journal articles are available in at least one major bibliographic database. Subscription-free databases provide free full-text access to almost half of the articles published after the year 2000, which may be of particular interest to clinicians in settings with limited access to subscription-based resources.
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Project description:BackgroundDatabases of literature-curated protein-protein interactions (PPIs) are often used to interpret high-throughput interactome mapping studies and estimate error rates. These databases combine interactions across thousands of published studies and experimental techniques. Because the tendency for two proteins to interact depends on the local conditions, this heterogeneity of conditions means that only a subset of database PPIs are interacting during any given experiment. A typical use of these databases as gold standards in interactome mapping projects, however, assumes that PPIs included in the database are indeed interacting under the experimental conditions of the study.ResultsUsing raw data from 20 co-fractionation experiments and six published interactomes, we demonstrate that this assumption is often false, with up to 55% of purported gold standard interactions showing no evidence of interaction, on average. We identify a subset of CORUM database complexes that do show consistent evidence of interaction in co-fractionation studies, and we use this subset as gold standards to dramatically improve interactome mapping as judged by the number of predicted interactions at a given error rate.ConclusionsWe recommend using this CORUM subset as the gold standard set in future co-fractionation studies. More generally, we recommend using the subset of literature-curated PPIs that are specific to the experimental context whenever possible.
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Project description:Modern healthcare is becoming increasingly technologized, knowledge-intensive, and specialized, which has consequences for communication between patients and practitioners in clinical encounters. Health information is increasingly accessible to patients through online resources. The objective of this paper is to provide an overview of existing studies that address how patients communicate with practitioners about online health information and to identify the key topics raised in these studies. With the concept of eHealth literacy as its point of departure, this paper specifically highlights the eHealth literacy topic of how patients comprehend, appraise and communicate online health information before and during the encounter with the healthcare professionals. In the literature search, we focus on qualitative studies that consider patients' and/or practitioners' reflections on online health information. We searched PUBMED, SCOPUS and Web of Science to capture research from various fields. Sixteen studies were included that met the following criteria: Qualitative study, focus on patient-practitioner communication, eHealth literacy and online information. The results show that the vast majority of studies were based on qualitative interviews, addressing patients' and practitioners' perspectives. Key topics in studies addressing patient perspectives were: reasons for seeking online information; calibrating understanding of online information with the practitioner; and barriers to discussing online information with practitioners. Key topics raised in studies focusing on practitioners were: trust in the patient and the online health information he/she presented as well as strategies to respond to patients who referred to online health information. The review highlights the need for further qualitative studies, preferably observational studies from authentic clinical communication situations, in order to capture how patients and practitioners communicate about online health information.
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Project description:Social reading sites offer an opportunity to capture a segment of readers' responses to literature, while data-driven analysis of these responses can provide new critical insight into how people 'read'. Posts discussing an individual book on the social reading site, Goodreads, are referred to as 'reviews', and consist of summaries, opinions, quotes or some mixture of these. Computationally modelling these reviews allows one to discover the non-professional discussion space about a work, including an aggregated summary of the work's plot, an implicit sequencing of various subplots and readers' impressions of main characters. We develop a pipeline of interlocking computational tools to extract a representation of this reader-generated shared narrative model. Using a corpus of reviews of five popular novels, we discover readers' distillation of the novels' main storylines and their sequencing, as well as the readers' varying impressions of characters in the novel. In so doing, we make three important contributions to the study of infinite-vocabulary networks: (i) an automatically derived narrative network that includes meta-actants; (ii) a sequencing algorithm, REV2SEQ, that generates a consensus sequence of events based on partial trajectories aggregated from reviews, and (iii) an 'impressions' algorithm, SENT2IMP, that provides multi-modal insight into readers' opinions of characters.
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Project description:Bioinformatics sequence databases such as Genbank or UniProt contain hundreds of millions of records of genomic data. These records are derived from direct submissions from individual laboratories, as well as from bulk submissions from large-scale sequencing centres; their diversity and scale means that they suffer from a range of data quality issues including errors, discrepancies, redundancies, ambiguities, incompleteness and inconsistencies with the published literature. In this work, we seek to investigate and analyze the data quality of sequence databases from the perspective of a curator, who must detect anomalous and suspicious records. Specifically, we emphasize the detection of inconsistent records with respect to the literature. Focusing on GenBank, we propose a set of 24 quality indicators, which are based on treating a record as a query into the published literature, and then use query quality predictors. We then carry out an analysis that shows that the proposed quality indicators and the quality of the records have a mutual relationship, in which one depends on the other. We propose to represent record-literature consistency as a vector of these quality indicators. By reducing the dimensionality of this representation for visualization purposes using principal component analysis, we show that records which have been reported as inconsistent with the literature fall roughly in the same area, and therefore share similar characteristics. By manually analyzing records not previously known to be erroneous that fall in the same area than records know to be inconsistent, we show that one record out of four is inconsistent with respect to the literature. This high density of inconsistent record opens the way towards the development of automatic methods for the detection of faulty records. We conclude that literature inconsistency is a meaningful strategy for identifying suspicious records. Database URL:https://github.com/rbouadjenek/DQBioinformatics.
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