PubDNA Finder: a web database linking full-text articles to sequences of nucleic acids

García-Remesal, Miguel; Cuevas, Alejandro; Pérez-Rey, David; Martín, Luiz A. B. San; Anguita, Alberto; Iglesia, Diana de la; Calle, Guillermo de la; Crespo, José; Maojo, Víctor

doi:10.1093/bioinformatics/btq520

Cited by 10 publications

(10 citation statements)

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“…Here, we show that DNA sequences in full-text articles provide a rich source of ‘unique identifiers’ that can be automatically extracted and mapped to genomic data in order to link articles to species, genes and genomic regions. We confirm recent findings that a substantial number of OA articles in PMC contain extractable DNA sequences (Garcia-Remesal et al , 2010b), and provide the first quantitative estimate of the proportion of PMC-OA articles with DNA sequences (∼22%), the majority of which we show are short sequences that are not found in GenBank.…”

Section: Discussionsupporting

confidence: 90%

“…The possibility that DNA and protein sequences can be extracted from biomedical text was first demonstrated by Wren et al (2005) and subsequently by several other groups (Aerts et al , 2008; Garcia-Remesal et al , 2010a, b; Shtatland et al , 2007). Aerts et al (2008) extended this technique to show that DNA sequences extracted from biomedical text could be mapped to genome sequences to identify the location, organism and target gene mentioned in an article.…”

Section: Introductionmentioning

confidence: 99%

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Annotating genes and genomes with DNA sequences extracted from biomedical articles

2011

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Motivation: Increasing rates of publication and DNA sequencing make the problem of finding relevant articles for a particular gene or genomic region more challenging than ever. Existing text-mining approaches focus on finding gene names or identifiers in English text. These are often not unique and do not identify the exact genomic location of a study.Results: Here, we report the results of a novel text-mining approach that extracts DNA sequences from biomedical articles and automatically maps them to genomic databases. We find that ∼20% of open access articles in PubMed central (PMC) have extractable DNA sequences that can be accurately mapped to the correct gene (91%) and genome (96%). We illustrate the utility of data extracted by text2genome from more than 150 000 PMC articles for the interpretation of ChIP-seq data and the design of quantitative reverse transcriptase (RT)-PCR experiments.Conclusion: Our approach links articles to genes and organisms without relying on gene names or identifiers. It also produces genome annotation tracks of the biomedical literature, thereby allowing researchers to use the power of modern genome browsers to access and analyze publications in the context of genomic data.Availability and implementation: Source code is available under a BSD license from http://sourceforge.net/projects/text2genome/ and results can be browsed and downloaded at http://text2genome.org.Contact: maximilianh@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Annotating genes and genomes with DNA sequences extracted from biomedical articles

2011

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“…In fact, the latter task is a significant research topic in biomedical informatics research, since many different systems for automatically indexing and searching the biomedical literature have been developed over the last few years. Examples include Pharmspresso [60], an information retrieval and extraction system for pharmacogenomic-related literature that follows a dictionary-based approach to identify instances of genes, drugs, polymorphisms and diseases, or PubDNA Finder [17], an online repository that we developed to link PubMed Central manuscripts to the sequences of nucleic acids appearing in them, following a hybrid approach that combines a rule-based system and lookup lists. We have already begun working in this direction with the development of a prototype of the “nanotoxicity searcher”.…”

Section: Resultsmentioning

confidence: 99%

Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature

García-Remesal

García-Ruiz

Pérez-Rey

et al. 2013

BioMed Research International

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Nanoinformatics is an emerging research field that uses informatics techniques to collect, process, store, and retrieve data, information, and knowledge on nanoparticles, nanomaterials, and nanodevices and their potential applications in health care. In this paper, we have focused on the solutions that nanoinformatics can provide to facilitate nanotoxicology research. For this, we have taken a computational approach to automatically recognize and extract nanotoxicology-related entities from the scientific literature. The desired entities belong to four different categories: nanoparticles, routes of exposure, toxic effects, and targets. The entity recognizer was trained using a corpus that we specifically created for this purpose and was validated by two nanomedicine/nanotoxicology experts. We evaluated the performance of our entity recognizer using 10-fold cross-validation. The precisions range from 87.6% (targets) to 93.0% (routes of exposure), while recall values range from 82.6% (routes of exposure) to 87.4% (toxic effects). These results prove the feasibility of using computational approaches to reliably perform different named entity recognition (NER)-dependent tasks, such as for instance augmented reading or semantic searches. This research is a “proof of concept” that can be expanded to stimulate further developments that could assist researchers in managing data, information, and knowledge at the nanolevel, thus accelerating research in nanomedicine.

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“…For this purpose, the automatic assignment of categories to the nanomaterials could be carried out with the use of text mining and NLP techniques to extract relevant information from papers describing them. Clustering engines could also help in this categorization by the application of standard mechanisms and algorithms, such as normalization, feature selection, consensus clustering and multidimensional scaling, among others (García-Remesal et al 2010). Nanomaterials would be clustered into sets so as to discover useful patterns (García-Remesal et al 2013).…”

Section: Cyberinfrastructures For Nanomedical Data Management: Curmentioning

confidence: 99%

Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

et al. 2013

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Nanotechnology represents an area of particular promise and significant opportunity across multiple scientific disciplines. Ongoing nanotechnology research ranges from the characterization of nanoparticles and nanomaterials to the analysis and processing of experimental data seeking correlations between nanoparticles and their functionalities and side effects. Due to their special properties, nanoparticles are suitable for cellular-level diagnostics and therapy, offering numerous applications in medicine, e.g. development of biomedical devices, tissue repair, drug delivery systems and biosensors. In nanomedicine, recent studies are producing large amounts of structural and property data, highlighting the role for computational approaches in information management. While in vitro and in vivo assays are expensive, the cost of computing is falling. Furthermore, improvements in the accuracy of computational methods (e.g. data mining, knowledge discovery, modeling and simulation) have enabled effective tools to automate the extraction, management and storage of these vast data volumes. Since this information is widely distributed, one major issue is how to locate and access data where it resides (which also poses data-sharing limitations). The novel discipline of nanoinformatics addresses the information challenges related to nanotechnology research. In this paper, we summarize the needs and challenges in the field and present an overview of extant initiatives and efforts.

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PubDNA Finder: a web database linking full-text articles to sequences of nucleic acids

Abstract: PubDNA Finder can be freely accessed at http://servet.dia.fi.upm.es:8080/pubdnafinder

Cited by 10 publications

References 1 publication

Annotating genes and genomes with DNA sequences extracted from biomedical articles

Annotating genes and genomes with DNA sequences extracted from biomedical articles

Using Nanoinformatics Methods for Automatically Identifying Relevant Nanotoxicology Entities from the Literature

Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

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