Dragon TF Association Miner: a system for exploring transcription factor associations through text-mining

Pan, Hong; Zuo, Li; Choudhary, Vidhu; Zhang, Zhuo; Leow, Shoi Houi; Chong, Fui Teen; Huang, Yingliang; Ong, Victor Wui Siong; Mohanty, Bijayalaxmi; Tan, Sin Lam; Krishnan, S. P. T.; Bajic, Vladimir B.

doi:10.1093/nar/gkh484

Cited by 31 publications

(23 citation statements)

References 22 publications

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“…In both cases, proteins were represented as vectors of words occurring in abstracts that mentioned the protein's name. More recent work in the area of text-assisted functional annotation [37,58] examined the classification of biomedical abstracts (rather than of proteins) into functional categories, tagging the abstracts themselves with relevant GO codes.…”

Section: Introductionmentioning

confidence: 99%

Text as data: Using text-based features for proteins representation and for computational prediction of their characteristics

Shatkay

Brady

Wong

2015

Methods

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The current era of large-scale biology is characterized by a fast-paced growth in the number of sequenced genomes and, consequently, by a multitude of identified proteins whose function has yet to be determined. Simultaneously, any known or postulated information concerning genes and proteins is part of the ever-growing published scientific literature, which is expanding at a rate of over a million new publications per year. Computational tools that attempt to automatically predict and annotate protein characteristics, such as function and localization patterns, are being developed along with systems that aim to support the process via text mining. Most work on protein characterization focuses on features derived directly from protein sequence data. Protein-related work that does aim to utilize the literature typically concentrates on extracting specific facts (e.g., protein interactions) from text. In the past few years we have taken a different route, treating the literature as a source of text-based features, which can be employed just as sequence-based protein-features were used in earlier work, for predicting protein subcellular location and possibly also function. We discuss here in detail the overall approach, along with results from work we have done in this area demonstrating the value of this method and its potential use.

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Section: Introductionmentioning

confidence: 99%

Text as data: Using text-based features for proteins representation and for computational prediction of their characteristics

Shatkay

Brady

Wong

2015

Methods

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“…a-star.edu.sg/DRAGON/TFAM/) (Pan et al, 2004). Progesterone response elements (PREs) were collected from the literature (DeMayo and Wheeler, unpublished data).…”

Section: Computational Techniquesmentioning

confidence: 99%

Targeting iCre expression to murine progesterone receptor cell-lineages using bacterial artificial chromosome transgenesis

Mukherjee

Soyal

Wheeler

et al. 2006

genesis

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Gene-targeting in embryonic stem cells has been the dominant genetic approach when engineering mouse models to query the physiologic importance of the progesterone receptor (PR). Although these models have been instrumental in disclosing the in vivo significance of the progesterone signaling pathway, generation of such mice exacts considerable expenditure of time, effort, and expense. Considering the growing list of new PR mouse models that are urgently required to address the next questions in progestin biology, bacterial artificial chromosome (BAC) recombineering in conjunction with transgenesis was evaluated as an alternative method to accelerate the creation of these models in the future. Using this approach, we describe the generation of three PR-BAC(iCre) transgenic lines in which improved Cre recombinase (iCre) was targeted in-frame, downstream, and under the control of the PR promoter contained within a BAC transgene. Crossing with the ROSA26R revealed that the PR-BAC(iCre) transgenic expresses active iCre only in cell-lineages that express the PR. The specificity of the PR-BAC(iCre) transgene not only underscores the importance of BAC-mediated transgenesis as a quick, easy, and affordable method by which to engineer the next generation of PR mouse models, but also provides a unique opportunity to investigate transcriptional control of PR expression as well as PR structure-function relationships in vivo.

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“…Keywords used to search the abstracts in the databases were the same as given in the legend. Pan et al (2004) or whole-plant function. DPBE complements the existing biological resources by presenting associations that may have been described previously or that are possibly novel.…”

Section: The Dpbe Systemmentioning

confidence: 99%

“…The technical details about this process have been described previously (Pan et al, 2004). When the system has analyzed all documents, it produces two categories of reports: tabular and graphical (Fig.…”

Section: Tabular and Graphical Outputs Of Dpbementioning

confidence: 99%

“…Second, depending on the topic of the literature search, even with very specific selection of documents the resulting networks could be very complex. To allow users to view these networks in the browser, an automatic partitioning procedure has been implemented to split a large network into several smaller networks (Pan et al, 2004). Users can minimize this by selecting fewer vocabulary lists and making their selection of documents even more specific.…”

Section: Tabular and Graphical Outputs Of Dpbementioning

confidence: 99%

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Dragon Plant Biology Explorer. A Text-Mining Tool for Integrating Associations between Genetic and Biochemical Entities with Genome Annotation and Biochemical Terms Lists

et al. 2005

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We introduce a tool for text mining, Dragon Plant Biology Explorer (DPBE) that integrates information on Arabidopsis (Arabidopsis thaliana) genes with their functions, based on gene ontologies and biochemical entity vocabularies, and presents the associations as interactive networks. The associations are based on (1) user-provided PubMed abstracts; (2) a list of Arabidopsis genes compiled by The Arabidopsis Information Resource; (3) user-defined combinations of four vocabulary lists based on the ones developed by the general, plant, and Arabidopsis GO consortia; and (4) three lists developed here based on metabolic pathways, enzymes, and metabolites derived from AraCyc, BRENDA, and other metabolism databases. We demonstrate how various combinations can be applied to fields of (1) gene function and gene interaction analyses, (2) plant development, (3) biochemistry and metabolism, and (4) pharmacology of bioactive compounds. Furthermore, we show the suitability of DPBE for systems approaches by integration with ''omics'' platform outputs. Using a list of abiotic stress-related genes identified by microarray experiments, we show how this tool can be used to rapidly build an information base on the previously reported relationships. This tool complements the existing biological resources for systems biology by identifying potentially novel associations using text analysis between cellular entities based on genome annotation terms. Thus, it allows researchers to efficiently summarize existing information for a group of genes or pathways, so as to make better informed choices for designing validation experiments. Last, DPBE can be helpful for beginning researchers and graduate students to summarize vast information in an unfamiliar area. DPBE is freely available for academic and nonprofit users at http://research.i2r.a-star.edu.sg/DRAGON/ME2/.

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Dragon TF Association Miner: a system for exploring transcription factor associations through text-mining

Cited by 31 publications

References 22 publications

Text as data: Using text-based features for proteins representation and for computational prediction of their characteristics

Text as data: Using text-based features for proteins representation and for computational prediction of their characteristics

Targeting iCre expression to murine progesterone receptor cell-lineages using bacterial artificial chromosome transgenesis

Dragon Plant Biology Explorer. A Text-Mining Tool for Integrating Associations between Genetic and Biochemical Entities with Genome Annotation and Biochemical Terms Lists

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