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RARGE II: an integrated phenotype database of Arabidopsis mutant traits using a controlled vocabulary.

ABSTRACT: Arabidopsis thaliana is one of the most popular experimental plants. However, only 40% of its genes have at least one experimental Gene Ontology (GO) annotation assigned. Systematic observation of mutant phenotypes is an important technique for elucidating gene functions. Indeed, several large-scale phenotypic analyses have been performed and have generated phenotypic data sets from many Arabidopsis mutant lines and overexpressing lines, which are freely available online. Since each Arabidopsis mutant line database uses individual phenotype expression, the differences in the structured term sets used by each database make it difficult to compare data sets and make it impossible to search across databases. Therefore, we obtained publicly available information for a total of 66,209 Arabidopsis mutant lines, including loss-of-function (RATM and TARAPPER) and gain-of-function (AtFOX and OsFOX) lines, and integrated the phenotype data by mapping the descriptions onto Plant Ontology (PO) and Phenotypic Quality Ontology (PATO) terms. This approach made it possible to manage the four different phenotype databases as one large data set. Here, we report a publicly accessible web-based database, the RIKEN Arabidopsis Genome Encyclopedia II (RARGE II; http://rarge-v2.psc.riken.jp/), in which all of the data described in this study are included. Using the database, we demonstrated consistency (in terms of protein function) with a previous study and identified the presumed function of an unknown gene. We provide examples of AT1G21600, which is a subunit in the plastid-encoded RNA polymerase complex, and AT5G56980, which is related to the jasmonic acid signaling pathway.

SUBMITTER: Akiyama K

PROVIDER: S-EPMC3894705 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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RARGE II: an integrated phenotype database of Arabidopsis mutant traits using a controlled vocabulary.

Akiyama Kenji K Kurotani Atsushi A Iida Kei K Kuromori Takashi T Shinozaki Kazuo K Sakurai Tetsuya T

Plant & cell physiology 20131121 1

Arabidopsis thaliana is one of the most popular experimental plants. However, only 40% of its genes have at least one experimental Gene Ontology (GO) annotation assigned. Systematic observation of mutant phenotypes is an important technique for elucidating gene functions. Indeed, several large-scale phenotypic analyses have been performed and have generated phenotypic data sets from many Arabidopsis mutant lines and overexpressing lines, which are freely available online. Since each Arabidopsis ...[more]

PMID: 24272250

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Project description:BACKGROUND: Plant microRNAs (miRNAs) have been revealed to play important roles in developmental control, hormone secretion, cell differentiation and proliferation, and response to environmental stresses. However, our knowledge about the regulatory mechanisms and functions of miRNAs remains very limited. The main difficulties lie in two aspects. On one hand, the number of experimentally validated miRNA targets is very limited and the predicted targets often include many false positives, which constrains us to reveal the functions of miRNAs. On the other hand, the regulation of miRNAs is known to be spatio-temporally specific, which increases the difficulty for us to understand the regulatory mechanisms of miRNAs. DESCRIPTION: In this paper we present miRFANs, an online database for Arabidopsis thalianamiRNA function annotations. We integrated various type of datasets, including miRNA-target interactions, transcription factor (TF) and their targets, expression profiles, genomic annotations and pathways, into a comprehensive database, and developed various statistical and mining tools, together with a user-friendly web interface. For each miRNA target predicted by psRNATarget, TargetAlign and UEA target-finder, or recorded in TarBase and miRTarBase, the effect of its up-regulated or down-regulated miRNA on the expression level of the target gene is evaluated by carrying out differential expression analysis of both miRNA and targets expression profiles acquired under the same (or similar) experimental condition and in the same tissue. Moreover, each miRNA target is associated with gene ontology and pathway terms, together with the target site information and regulating miRNAs predicted by different computational methods. These associated terms may provide valuable insight for the functions of each miRNA. CONCLUSION: First, a comprehensive collection of miRNA targets for Arabidopsis thaliana provides valuable information about the functions of plant miRNAs. Second, a highly informative miRNA-mediated genetic regulatory network is extracted from our integrative database. Third, a set of statistical and mining tools is equipped for analyzing and mining the database. And fourth, a user-friendly web interface is developed to facilitate the browsing and analysis of the collected data.

Project description:Objective:To study the effect on patient cohorts of mapping condition (diagnosis) codes from source billing vocabularies to a clinical vocabulary. Materials and Methods:Nine International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9-CM) concept sets were extracted from eMERGE network phenotypes, translated to Systematized Nomenclature of Medicine - Clinical Terms concept sets, and applied to patient data that were mapped from source ICD9-CM and ICD10-CM codes to Systematized Nomenclature of Medicine - Clinical Terms codes using Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) vocabulary mappings. The original ICD9-CM concept set and a concept set extended to ICD10-CM were used to create patient cohorts that served as gold standards. Results:Four phenotype concept sets were able to be translated to Systematized Nomenclature of Medicine - Clinical Terms without ambiguities and were able to perform perfectly with respect to the gold standards. The other 5 lost performance when 2 or more ICD9-CM or ICD10-CM codes mapped to the same Systematized Nomenclature of Medicine - Clinical Terms code. The patient cohorts had a total error (false positive and false negative) of up to 0.15% compared to querying ICD9-CM source data and up to 0.26% compared to querying ICD9-CM and ICD10-CM data. Knowledge engineering was required to produce that performance; simple automated methods to generate concept sets had errors up to 10% (one outlier at 250%). Discussion:The translation of data from source vocabularies to Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) resulted in very small error rates that were an order of magnitude smaller than other error sources. Conclusion:It appears possible to map diagnoses from disparate vocabularies to a single clinical vocabulary and carry out research using a single set of definitions, thus improving efficiency and transportability of research.

Dataset Information

RARGE II: an integrated phenotype database of Arabidopsis mutant traits using a controlled vocabulary.

Publications

RARGE II: an integrated phenotype database of Arabidopsis mutant traits using a controlled vocabulary.

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