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DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining.

ABSTRACT: Noncoding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long ncRNAs (lncRNAs), are important players in diseases and emerge as novel drug targets. Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are critical for better understanding ncRNA roles that may eventually help develop their use in medicine. To support ncRNA research and facilitate retrieval of relevant information regarding miRNAs and lncRNAs from the plethora of published ncRNA-related research, we developed DES-ncRNA ( www.cbrc.kaust.edu.sa/des_ncrna ). DES-ncRNA is a knowledgebase containing text- and data-mined information from public scientific literature and other public resources. Exploration of mined information is enabled through terms and pairs of terms from 19 topic-specific dictionaries including, for example, antibiotics, toxins, drugs, enzymes, mutations, pathways, human genes and proteins, drug indications and side effects, mutations, diseases, etc. DES-ncRNA contains approximately 878,000 associations of terms from these dictionaries of which 36,222 (5,373) are with regards to miRNAs (lncRNAs). We provide several ways to explore information regarding ncRNAs to users including controlled generation of association networks as well as hypotheses generation. We show an example how DES-ncRNA can aid research on Alzheimer disease and suggest potential therapeutic role for Fasudil. DES-ncRNA is a powerful tool that can be used on its own or as a complement to the existing resources, to support research in human ncRNA. To our knowledge, this is the only knowledgebase dedicated to human miRNAs and lncRNAs derived primarily through literature-mining enabling exploration of a broad spectrum of associated biomedical entities, not paralleled by any other resource.

SUBMITTER: Salhi A

PROVIDER: S-EPMC5546543 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:Long non-coding RNAs (lncRNAs) are generally defined as RNA transcripts longer than 200 nucleotides that are not translated into proteins. Recently, many small open reading frames (smORFs) embedded in lncRNA scripts have been verified to be able to encode functional polypeptides (namely lncRNA-SEPs here). Although collaborative analysis by advanced genomics, bioinformatics and proteomics largely drives SEPs discovery, the poor predictability, diminutive size and low abundance still challenge systematic identification of SEPs from different biological samples. Here, we took advantage of the NONCODE database that deposited with the most complete collection and annotation of lncRNA transcripts from different species to build a database that to maximally collect all putative small ORFs from human and mouse lncRNA transcripts. Two effective and complementary polypeptides enrichment strategies (30 kDa MWCO filter and C8 SPE column) were also integrated to further improve the discovery of novel lncRNA-SEPs. These efforts led to the discovery of 362 lncRNA-SEPs from 8 human cell lines and 238 lncRNA-SEPs from 3 mouse cell lines and 8 mouse tissues. 18 out of these lncRNA-SEPs were verified experimentally by multiple technologies including in vitro expression, immunoblotting and parallel reaction monitoring-based mass spectrometry (PRM-MS) in 293T cells. Further bioinformatic analysis reveals that the physical and chemical properties of these novel lncRNA-SEPs, such as amino acid composition and codon usage, are varied from canonical proteins. Intriguingly, nearly 70% of the identified lncRNA-SEPs were found to be initiated with non-AUG start codons. Collectively, the efficient workflows presented in this study enables us identify 600 novel lncRNA-SPEs from multiple cell lines and tissues, which should represent the largest number of MS-detected lncRNA-encoding SEPs ever reported to date. These novel lncRNA-SEPs not only could provide new clues for the annotation of the noncoding elements in the genome, but also could serve as a valuable resource for the functional characterization of individual lncRNA-SEPs.

Project description:BackgroundFor selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives.MethodsA biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases.ResultsThe current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms) in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods) is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http://www.scaiview.com/scaiview-academia.html.ConclusionsThe approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Project description:BackgroundWith the biomedical literature continually expanding, searching PubMed for information about specific genes becomes increasingly difficult. Not only can thousands of results be returned, but gene name ambiguity leads to many irrelevant hits. As a result, it is difficult for life scientists and gene curators to rapidly get an overall picture about a specific gene from documents that mention its names and synonyms.ResultsIn this paper, we present eGIFT (http://biotm.cis.udel.edu/eGIFT), a web-based tool that associates informative terms, called iTerms, and sentences containing them, with genes. To associate iTerms with a gene, eGIFT ranks iTerms about the gene, based on a score which compares the frequency of occurrence of a term in the gene's literature to its frequency of occurrence in documents about genes in general. To retrieve a gene's documents (Medline abstracts), eGIFT considers all gene names, aliases, and synonyms. Since many of the gene names can be ambiguous, eGIFT applies a disambiguation step to remove matches that do not correspond to this gene. Another additional filtering process is applied to retain those abstracts that focus on the gene rather than mention it in passing. eGIFT's information for a gene is pre-computed and users of eGIFT can search for genes by using a name or an EntrezGene identifier. iTerms are grouped into different categories to facilitate a quick inspection. eGIFT also links an iTerm to sentences mentioning the term to allow users to see the relation between the iTerm and the gene. We evaluated the precision and recall of eGIFT's iTerms for 40 genes; between 88% and 94% of the iTerms were marked as salient by our evaluators, and 94% of the UniProtKB keywords for these genes were also identified by eGIFT as iTerms.ConclusionsOur evaluations suggest that iTerms capture highly-relevant aspects of genes. Furthermore, by showing sentences containing these terms, eGIFT can provide a quick description of a specific gene. eGIFT helps not only life scientists survey results of high-throughput experiments, but also annotators to find articles describing gene aspects and functions.

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DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining.

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