MiRetrieve-an R package and web application for miRNA text mining.
Ontology highlight
ABSTRACT:
SUBMITTER: Friedrich J
PROVIDER: S-EPMC8696973 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesClinico-genomic data (CGD) acquired through routine clinical practice has the potential to improve our understanding of clinical oncology. However, these data often reside in heterogeneous and semistructured data, resulting in prolonged time-to-analyses.Materials and methodsWe created GENETEX: an R package and Shiny application for text mining genomic reports from electronic health record (EHR) and direct import into Research Electronic Data Capture (REDCap).ResultsGENETEX facilitates the abstraction of CGD from EHR and streamlines the capture of structured data into REDCap. Its functions include natural language processing of key genomic information, transformation of semistructured data into structured data, and importation into REDCap. When evaluated with manual abstraction, GENETEX had >99% agreement and captured CGD in approximately one-fifth the time.ConclusionsGENETEX is freely available under the Massachusetts Institute of Technology license and can be obtained from GitHub (https://github.com/TheMillerLab/genetex). GENETEX is executed in R and deployed as a Shiny application for non-R users. It produces high-fidelity abstraction of CGD in a fraction of the time.
| S-EPMC8476929 | biostudies-literature
Project description:Abstract Extracting and processing information from documents is of great importance as lots of experimental results and findings are stored in local files. Therefore, extracting and analyzing biomedical terms from such files in an automated way is absolutely necessary. In this article, we present OnTheFly2.0, a web application for extracting biomedical entities from individual files such as plain texts, office documents, PDF files or images. OnTheFly2.0 can generate informative summaries in popup windows containing knowledge related to the identified terms along with links to various databases. It uses the EXTRACT tagging service to perform named entity recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and gene ontology terms. Multiple files can be analyzed, whereas identified terms such as proteins or genes can be explored through functional enrichment analysis or be associated with diseases and PubMed entries. Finally, protein–protein and protein–chemical networks can be generated with the use of STRING and STITCH services. To demonstrate its capacity for knowledge discovery, we interrogated published meta-analyses of clinical biomarkers of severe COVID-19 and uncovered inflammatory and senescence pathways that impact disease pathogenesis. OnTheFly2.0 currently supports 197 species and is available at http://bib.fleming.gr:3838/OnTheFly/ and http://onthefly.pavlopouloslab.info.
| S-EPMC8494211 | biostudies-literature
Project description:MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0.90 on the three different types of relations. We conducted full-scale text mining using miRTex to process all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset. The results for all the Medline abstracts are stored in a database for interactive query and file download via the website at http://proteininformationresource.org/mirtex. Using miRTex, we identified genes potentially regulated by miRNAs in Triple Negative Breast Cancer, as well as miRNA-gene relations that, in conjunction with kinase-substrate relations, regulate the response to abiotic stress in Arabidopsis thaliana. These two use cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biological processes.
| S-EPMC4583433 | biostudies-literature
Project description:Purpose: Build genesets using text mining and validate the geneset with data from public repository and a dataset generated in house using in vitro models and RNA-seq Methods: Mucus hypersecretion and mucociliary dysfucntion adverse outcome pathways (AOPs) genesets were build using the text mining method described by Rani et al., 2015. Validation was performed using RNA expression data from cigarette and e-cigarette aerosol treated cells, IL-13 treated airway cells, and COPD-lung biopsies. The cigarette and e-cigarette aerosol RNA-samples from airway cells were generated and sequenced in house. The other dataset were publically available. Results: Using unsupervised clustering, the mucus hypersecretion and mucociliary dysfunction genesets were able to discriminate the cigartte treated cells from the e-cigarettes and the air control. The e-cigarette and the air control clustered together. Clustering was also observed with IL-13 treated cells. IL-13 is an induced of mucus hypersecretion. Clustering was not observed when COPD RNA-seq samples were used. PCA analysis revealed some degree of grouping based on disease status, but this was also heavily confounded by other parameters. Conclusions: Our study described the first application of text mining to build genesets relevant to AOPs. In vitro validation confirmed the genesets could discriminates between treatment that induce mucus hypersecretion phenotypes, however this could not be confirmed with COPD biopsy samples. This could be due to a series of technical confouding factors and the heterogeneity of the COPD disease.
2020-04-30 | GSE142100 | GEO
Project description:BACKGROUND: Mutation impact extraction is an important task designed to harvest relevant annotations from scientific documents for reuse in multiple contexts. Our previous work on text mining for mutation impacts resulted in (i) the development of a GATE-based pipeline that mines texts for information about impacts of mutations on proteins, (ii) the population of this information into our OWL DL mutation impact ontology, and (iii) establishing an experimental semantic database for storing the results of text mining. RESULTS: This article explores the possibility of using the SADI framework as a medium for publishing our mutation impact software and data. SADI is a set of conventions for creating web services with semantic descriptions that facilitate automatic discovery and orchestration. We describe a case study exploring and demonstrating the utility of the SADI approach in our context. We describe several SADI services we created based on our text mining API and data, and demonstrate how they can be used in a number of biologically meaningful scenarios through a SPARQL interface (SHARE) to SADI services. In all cases we pay special attention to the integration of mutation impact services with external SADI services providing information about related biological entities, such as proteins, pathways, and drugs. CONCLUSION: We have identified that SADI provides an effective way of exposing our mutation impact data such that it can be leveraged by a variety of stakeholders in multiple use cases. The solutions we provide for our use cases can serve as examples to potential SADI adopters trying to solve similar integration problems.
| S-EPMC3194198 | biostudies-literature
Project description:BackgroundBiological function is greatly dependent on the interactions of proteins with other proteins and genes. Abstracts from the biomedical literature stored in the NCBI's PubMed database can be used for the derivation of interactions between genes and proteins by identifying the co-occurrences of their terms. Often, the amount of interactions obtained through such an approach is large and may mix processes occurring in different contexts. Current tools do not allow studying these data with a focus on concepts of relevance to a user, for example, interactions related to a disease or to a biological mechanism such as protein aggregation.ResultsTo help the concept-oriented exploration of such data we developed PESCADOR, a web tool that extracts a network of interactions from a set of PubMed abstracts given by a user, and allows filtering the interaction network according to user-defined concepts. We illustrate its use in exploring protein aggregation in neurodegenerative disease and in the expansion of pathways associated to colon cancer.ConclusionsPESCADOR is a platform independent web resource available at: http://cbdm.mdc-berlin.de/tools/pescador/
| S-EPMC3228910 | biostudies-literature
Project description:Gene-phenotype associations play an important role in understanding the disease mechanisms which is a requirement for treatment development. A portion of gene-phenotype associations are observed mainly experimentally and made publicly available through several standard resources such as MGI. However, there is still a vast amount of gene-phenotype associations buried in the biomedical literature. Given the large amount of literature data, we need automated text mining tools to alleviate the burden in manual curation of gene-phenotype associations and to develop comprehensive resources. In this study, we present an ontology-based approach in combination with statistical methods to text mine gene-phenotype associations from the literature. Our method achieved AUC values of 0.90 and 0.75 in recovering known gene-phenotype associations from HPO and MGI respectively. We posit that candidate genes and their relevant diseases should be expressed with similar phenotypes in publications. Thus, we demonstrate the utility of our approach by predicting disease candidate genes based on the semantic similarities of phenotypes associated with genes and diseases. To the best of our knowledge, this is the first study using an ontology based approach to extract gene-phenotype associations from the literature. We evaluated our disease candidate prediction model on the gene-disease associations from MGI. Our model achieved AUC values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of gene-disease associations respectively. Our manual analysis on the text mined data revealed that our method can accurately extract gene-phenotype associations which are not currently covered by the existing public gene-phenotype resources. Overall, results indicate that our method can precisely extract known as well as new gene-phenotype associations from literature. All the data and methods are available at https://github.com/bio-ontology-research-group/genepheno.
| S-EPMC6391585 | biostudies-literature
Project description:BackgroundThe annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB.ResultsThe procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments.ConclusionsThe information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.
| S-EPMC3660268 | biostudies-literature
Project description:BACKGROUND: Decoding transcriptional regulatory networks and the genomic cis-regulatory logic implemented in their control nodes is a fundamental challenge in genome biology. High-throughput computational and experimental analyses of regulatory networks and sequences rely heavily on positive control data from prior small-scale experiments, but the vast majority of previously discovered regulatory data remains locked in the biomedical literature. RESULTS: We develop text-mining strategies to identify relevant publications and extract sequence information to assist the regulatory annotation process. Using a vector space model to identify Medline abstracts from papers likely to have high cis-regulatory content, we demonstrate that document relevance ranking can assist the curation of transcriptional regulatory networks and estimate that, minimally, 30,000 papers harbor unannotated cis-regulatory data. In addition, we show that DNA sequences can be extracted from primary text with high cis-regulatory content and mapped to genome sequences as a means of identifying the location, organism and target gene information that is critical to the cis-regulatory annotation process. CONCLUSION: Our results demonstrate that text-mining technologies can be successfully integrated with genome annotation systems, thereby increasing the availability of annotated cis-regulatory data needed to catalyze advances in the field of gene regulation.
| S-EPMC2374703 | biostudies-other
Project description:Various disciplines are trying to solve one of the most noteworthy queries and broadly used concepts in biology, essentiality. Centrality is a primary index and a promising method for identifying essential nodes, particularly in biological networks. The newly created CentiServer is a comprehensive online resource that provides over 110 definitions of different centrality indices, their computational methods, and algorithms in the form of an encyclopedia. In addition, CentiServer allows users to calculate 55 centralities with the help of an interactive web-based application tool and provides a numerical result as a comma separated value (csv) file format or a mapped graphical format as a graph modeling language (GML) file. The standalone version of this application has been developed in the form of an R package. The web-based application (CentiServer) and R package (centiserve) are freely available at http://www.centiserver.org/.
| S-EPMC4646361 | biostudies-literature