Literature-based Database Research Articles

REGene: a literature-based knowledgebase of animal regeneration that bridge tissue regeneration and cancer.

Regeneration is a common phenomenon across multiple animal phyla. Regeneration-related genes (REGs) are critical for fundamental cellular processes such as proliferation and differentiation. Identification of REGs and elucidating their functions may help to further develop effective treatment strategies in regenerative medicine. So far, REGs have been largely identified by small-scale experimental studies and a comprehensive characterization of the diverse biological processes regulated by REGs is lacking. Therefore, there is an ever-growing need to integrate REGs at the genomics, epigenetics, and transcriptome level to provide a reference list of REGs for regeneration and regenerative medicine research. Towards achieving this, we developed the first literature-based database called REGene (REgeneration Gene database). In the current release, REGene contains 948 human (929 protein-coding and 19 non-coding genes) and 8445 homologous genes curated from gene ontology and extensive literature examination. Additionally, the REGene database provides detailed annotations for each REG, including: gene expression, methylation sites, upstream transcription factors, and protein-protein interactions. An analysis of the collected REGs reveals strong links to a variety of cancers in terms of genetic mutation, protein domains, and cellular pathways. We have prepared a web interface to share these regeneration genes, supported by refined browsing and searching functions at http://REGene.bioinfo-minzhao.org/.

Large-Scale Modelling of the Environmentally-Driven Population Dynamics of Temperate Aedes albopictus (Skuse).

The Asian tiger mosquito, Aedes albopictus, is a highly invasive vector species. It is a proven vector of dengue and chikungunya viruses, with the potential to host a further 24 arboviruses. It has recently expanded its geographical range, threatening many countries in the Middle East, Mediterranean, Europe and North America. Here, we investigate the theoretical limitations of its range expansion by developing an environmentally-driven mathematical model of its population dynamics. We focus on the temperate strain of Ae. albopictus and compile a comprehensive literature-based database of physiological parameters. As a novel approach, we link its population dynamics to globally-available environmental datasets by performing inference on all parameters. We adopt a Bayesian approach using experimental data as prior knowledge and the surveillance dataset of Emilia-Romagna, Italy, as evidence. The model accounts for temperature, precipitation, human population density and photoperiod as the main environmental drivers, and, in addition, incorporates the mechanism of diapause and a simple breeding site model. The model demonstrates high predictive skill over the reference region and beyond, confirming most of the current reports of vector presence in Europe. One of the main hypotheses derived from the model is the survival of Ae. albopictus populations through harsh winter conditions. The model, constrained by the environmental datasets, requires that either diapausing eggs or adult vectors have increased cold resistance. The model also suggests that temperature and photoperiod control diapause initiation and termination differentially. We demonstrate that it is possible to account for unobserved properties and constraints, such as differences between laboratory and field conditions, to derive reliable inferences on the environmental dependence of Ae. albopictus populations.

ECGene: A Literature-Based Knowledgebase of Endometrial Cancer Genes.

ABSTRACTEndometrial cancer (EC) ranks as the sixth common cancer for women worldwide. To better distinguish cancer subtypes and identify effective early diagnostic biomarkers, we need improved understanding of the biological mechanisms associated with EC dysregulated genes. Although there is a wealth of clinical and molecular information relevant to EC in the literature, there has been no systematic summary of EC‐implicated genes. In this study, we developed a literature‐based database ECGene (Endometrial Cancer Gene database) with comprehensive annotations. ECGene features manual curation of 414 genes from thousands of publications, results from eight EC gene expression datasets, precomputation of coexpressed long noncoding RNAs, and an EC‐implicated gene interactome. In the current release, we generated and comprehensively annotated a list of 458 EC‐implicated genes. We found the top‐ranked EC‐implicated genes are frequently mutated in The Cancer Genome Atlas (TCGA) tumor samples. Furthermore, systematic analysis of coexpressed lncRNAs provided insight into the important roles of lncRNA in EC development. ECGene has a user‐friendly Web interface and is freely available at http://ecgene.bioinfo‐minzhao.org/. As the first literature‐based online resource for EC, ECGene serves as a useful gateway for researchers to explore EC genetics.

DbPEC: a comprehensive literature-based database for preeclampsia related genes and phenotypes.

Preeclampsia is one of the most common causes of fetal and maternal morbidity and mortality in the world. We built a Database for Preeclampsia (dbPEC) consisting of the clinical features, concurrent conditions, published literature and genes associated with Preeclampsia. We included gene sets associated with severity, concurrent conditions, tissue sources and networks. The published scientific literature is the primary repository for all information documenting human disease. We used semantic data mining to retrieve and extract the articles pertaining to preeclampsia-associated genes and performed manual curation. We deposited the articles, genes, preeclampsia phenotypes and other supporting information into the dbPEC. It is publicly available and freely accessible. Previously, we developed a database for preterm birth (dbPTB) using a similar approach. Using the gene sets in dbPTB, we were able to successfully analyze a genome-wide study of preterm birth including 4000 women and children. We identified important genes and pathways associated with preterm birth that were not otherwise demonstrable using genome-wide approaches. dbPEC serves not only as a resources for genes and articles associated with preeclampsia, it is a robust source of gene sets to analyze a wide range of high-throughput data for gene set enrichment analysis.Database URL: http://ptbdb.cs.brown.edu/dbpec/

CSGene: a literature-based database for cell senescence genes and its application to identify critical cell aging pathways and associated diseases.

Cell senescence is a cellular process in which normal diploid cells cease to replicate and is a major driving force for human cancers and aging-associated diseases. Recent studies on cell senescence have identified many new genetic components and pathways that control cell aging. However, there is no comprehensive resource for cell senescence that integrates various genetic studies and relationships with cell senescence, and the risk associated with complex diseases such as cancer is still unexplored. We have developed the first literature-based gene resource for exploring cell senescence genes, CSGene. We complied 504 experimentally verified genes from public data resources and published literature. Pathway analyses highlighted the prominent roles of cell senescence genes in the control of rRNA gene transcription and unusual rDNA repeat that constitute a center for the stability of the whole genome. We also found a strong association of cell senescence with HIV-1 infection and viral carcinogenesis that are mainly related to promoter/enhancer binding and chromatin modification processes. Moreover, pan-cancer mutation and network analysis also identified common cell aging mechanisms in cancers and uncovered a highly modular network structure. These results highlight the utility of CSGene for elucidating the complex cellular events of cell senescence.

CardioGenBase: A Literature Based Multi-Omics Database for Major Cardiovascular Diseases.

Cardiovascular diseases (CVDs) account for high morbidity and mortality worldwide. Both, genetic and epigenetic factors are involved in the enumeration of various cardiovascular diseases. In recent years, a vast amount of multi-omics data are accumulated in the field of cardiovascular research, yet the understanding of key mechanistic aspects of CVDs remain uncovered. Hence, a comprehensive online resource tool is required to comprehend previous research findings and to draw novel methodology for understanding disease pathophysiology. Here, we have developed a literature-based database, CardioGenBase, collecting gene-disease association from Pubmed and MEDLINE. The database covers major cardiovascular diseases such as cerebrovascular disease, coronary artery disease (CAD), hypertensive heart disease, inflammatory heart disease, ischemic heart disease and rheumatic heart disease. It contains ~1,500 cardiovascular disease genes from ~2,4000 research articles. For each gene, literature evidence, ontology, pathways, single nucleotide polymorphism, protein-protein interaction network, normal gene expression, protein expressions in various body fluids and tissues are provided. In addition, tools like gene-disease association finder and gene expression finder are made available for the users with figures, tables, maps and venn diagram to fit their needs. To our knowledge, CardioGenBase is the only database to provide gene-disease association for above mentioned major cardiovascular diseases in a single portal. CardioGenBase is a vital online resource to support genome-wide analysis, genetic, epigenetic and pharmacological studies.

Dynamics of the discovery process of protein-protein interactions from low content studies.

BackgroundThousands of biological and biomedical investigators study of the functional role of single genes and their protein products in normal physiology and in disease. The findings from these studies are reported in research articles that stimulate new research. It is now established that a complex regulatory networks's is controlling human cellular fate, and this community of researchers are continually unraveling this network topology. Attempts to integrate results from such accumulated knowledge resulted in literature-based protein-protein interaction networks (PPINs) and pathway databases. These databases are widely used by the community to analyze new data collected from emerging genome-wide studies with the assumption that the data within these literature-based databases is the ground truth and contain no biases. While suspicion for research focus biases is growing, a concrete proof for it is still missing. It is difficult to prove because the real PPINs are mostly unknown.ResultsHere we analyzed the longitudinal discovery process of literature-based mammalian and yeast PPINs to observe that these networks are discovered non-uniformly. The pattern of discovery is related to a theoretical concept proposed by Kauffman called “expanding the adjacent possible”. We introduce a network discovery model which explicitly includes the space of possibilities in the form of a true underlying PPIN.ConclusionsOur model strongly suggests that research focus biases exist in the observed discovery dynamics of these networks. In summary, more care should be placed when using PPIN databases for analysis of newly acquired data, and when considering prior knowledge when designing new experiments.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0173-z) contains supplementary material, which is available to authorized users.

Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types.

For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literature-based database of electrophysiological properties (www.neuroelectro.org), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across laboratories than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into six to nine superclasses. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at theta-band frequencies.

TSGene: a web resource for tumor suppressor genes

Tumor suppressor genes (TSGs) are guardian genes that play important roles in controlling cell proliferation processes such as cell-cycle checkpoints and inducing apoptosis. Identification of these genes and understanding their functions are critical for further investigation of tumorigenesis. So far, many studies have identified numerous TSGs and illustrated their functions in various types of tumors or normal samples. Furthermore, accumulating evidence has shown that non-coding RNAs can act as TSGs to prevent the tumorigenesis processes. Therefore, there is a growing demand to integrate TSGs with large-scale experimental evidence (e.g. gene expression and epigenetic signatures) to provide a comprehensive resource for further investigation of TSGs and their molecular mechanisms in cancer. To achieve this goal, we first developed a comprehensive literature-based database called TSGene (tumor suppressor gene database), freely available at http://bioinfo.mc.vanderbilt.edu/TSGene/. In the current release, TSGene contains 716 human (637 protein-coding and 79 non-coding genes), 628 mouse and 567 rat TSGs curated from UniProtKB, the Tumor Associated Gene database and 5795 PubMed abstracts. Additionally, the TSGene provides detailed annotations for each TSG, such as cancer mutations, gene expressions, methylation sites, TF regulations and protein–protein interactions.

Automatic categorization of diverse experimental information in the bioscience literature.

BackgroundCuration of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance.ResultsWe successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for automatic association of newly published papers with ten data types including RNAi, antibody, phenotype, gene regulation, mutant allele sequence, gene expression, gene product interaction, overexpression phenotype, gene interaction, and gene structure correction.ConclusionsOur methods are applicable to a variety of data types with training set containing several hundreds to a few thousand documents. It is completely automatic and, thus can be readily incorporated to different workflow at different literature-based databases. We believe that the work presented here can contribute greatly to the tremendous task of automating the important yet labor-intensive biocuration effort.

Statistical modeling of occupational chlorinated solvent exposures for case–control studies using a literature-based database

Occupational exposure assessment for population-based case-control studies is challenging due to the wide variety of industries and occupations encountered by study participants. We developed and evaluated statistical models to estimate the intensity of exposure to three chlorinated solvents-methylene chloride, 1,1,1-trichloroethane, and trichloroethylene-using a database of air measurement data and associated exposure determinants. A measurement database was developed after an extensive review of the published industrial hygiene literature. The database of nearly 3000 measurements or summary measurements included sample size, measurement characteristics (year, duration, and type), and several potential exposure determinants associated with the measurements: mechanism of release (e.g. evaporation), process condition, temperature, usage rate, type of ventilation, location, presence of a confined space, and proximity to the source. The natural log-transformed measurement levels in the exposure database were modeled as a function of the measurement characteristics and exposure determinants using maximum likelihood methods. Assuming a single lognormal distribution of the measurements, an arithmetic mean exposure intensity level was estimated for each unique combination of exposure determinants and decade. The proportions of variability in the measurement data explained by the modeled measurement characteristics and exposure determinants were 36, 38, and 54% for methylene chloride, 1,1,1-trichloroethane, and trichloroethylene, respectively. Model parameter estimates for the exposure determinants were in the anticipated direction. Exposure intensity estimates were plausible and exhibited internal consistency, but the ability to evaluate validity was limited. These prediction models can be used to estimate chlorinated solvent exposure intensity for jobs reported by population-based case-control study participants that have sufficiently detailed information regarding the exposure determinants.

Dependence of Phenotypic Variance in Body Size on Environmental Quality

The recent "overhead threshold" model for optimal age and body size at maturity (Day and Rowe 2002 ) predicts that phenotypic variability in adult body size will be low under inferior environmental quality and will increase with improving conditions. The model is, however, based on a potentially restrictive assumption of a monotone increase of fecundity with increasing body size. On the basis of a numerical model, we show that introducing the concept of maximum adult body size changes the predictions of the model. The dependence of variability in adult body size on environmental quality becomes a concave function with a maximum at intermediate values. Depending on the range of environmental conditions considered, one may therefore expect to observe both increasing and decreasing functions. We test the predictions of our model on a literature-based database of 131 insect species covering all major orders. We demonstrate that, in most species, relative phenotypic variation in body size decreases when environment-specific average of adult body size increases. In the majority of cases at least, such a relationship can be interpreted as a decreased relative variation in better growing conditions. With some potentially meaningful exceptions (e.g., females of capital-breeding insects), the general pattern was largely invariable across different taxa, ecological subdivisions, and sexes.

Habitat preferences and distribution characteristics are indicative of species long-term persistence in the Estonian flora

Large-scale changes in regional floras provide direct information about changes in biodiversity through time and enable the evaluation of conservation targets. We compared the distribution ranges in 2004 of Estonian native terrestrial flora with the distribution ranges before 1970, using the Atlas of Estonian Flora. Relative persistence was related to species endemism, commonness, occurrence at its border of the global distribution range, main habitat type, sensitivity to human impact, life-form, conservation category, and Red List category. A literature-based database of the flora of Estonian habitat types was used to evaluate relative persistence of the flora of different habitats. Changes in the flora are largely dependent on human activities. The decrease in mire and grassland habitats and the increase in forests are reflected in the persistences of related species. Flora of mire habitats decreased the most. The fact that an almost ten-fold decrease of grasslands has not resulted in as large a decrease in the ranges of grassland species could serve as evidence of the extinction debt of these habitats. We also found a greater decrease among habitat specialists than habitat generalists and lower average persistence of the species of species-rich habitats. Our data show that current prioritization of species for conservation is in concordance with needs, as reflected in the changes in the range of species. However, conservation has not been entirely successful: the decrease of protected species continues. Our simple method for summarizing large databases was effective for the evaluation of large scale effects of conservation actions.

The Autoimmune Disease Database: a dynamically compiled literature-derived database

BackgroundAutoimmune diseases are disorders caused by an immune response directed against the body's own organs, tissues and cells. In practice more than 80 clinically distinct diseases, among them systemic lupus erythematosus and rheumatoid arthritis, are classified as autoimmune diseases. Although their etiology is unclear these diseases share certain similarities at the molecular level i.e. susceptibility regions on the chromosomes or the involvement of common genes. To gain an overview of these related diseases it is not feasible to do a literary review but it requires methods of automated analyses of the more than 500,000 Medline documents related to autoimmune disorders.ResultsIn this paper we present the first version of the Autoimmune Disease Database which to our knowledge is the first comprehensive literature-based database covering all known or suspected autoimmune diseases. This dynamically compiled database allows researchers to link autoimmune diseases to the candidate genes or proteins through the use of named entity recognition which identifies genes/proteins in the corresponding Medline abstracts. The Autoimmune Disease Database covers 103 autoimmune disease concepts. This list was expanded to include synonyms and spelling variants yielding a list of over 1,200 disease names. The current version of the database provides links to 541,690 abstracts and over 5,000 unique genes/proteins.ConclusionThe Autoimmune Disease Database provides the researcher with a tool to navigate potential gene-disease relationships in Medline abstracts in the context of autoimmune diseases.

The Beilstein NetFire system

A new and valuable literature-based database and search system, called NetFire, has recently been made available on the Internet by Beilstein Information Systems [1]. It is the first attempt by Beilstein to provide chemical information outside of their main area of extracted and evaluated data. The existing Beilstein system, provided via their Cross-Fire system, is an in-house system which has received wide acceptance since it was introduced about two years ago. NetFire is a very new and different system, and for readers of TrAC, a nice Internet resource. It is yet another example of the innovation of new and modern electronic products which have characterized Beilstein in the few years since the organization is no longer being funded, in part, by the German Government, but rather must stand on its own.