Dataset Information

ForestTreeDB: a database dedicated to the mining of tree transcriptomes.

ABSTRACT: ForestTreeDB is intended as a resource that centralizes large-scale expressed sequence tag (EST) sequencing results from several tree species (http://foresttree.org/ftdb). It currently encompasses 344,878 quality sequences from 68 libraries, from diverse organs of conifer and hybrid poplar trees. It utilizes the Nimbus data model to provide a hosting system for multiple projects, and uses object-relational mapping APIs in Java and Perl for data accesses within an Oracle database designed to be scalable, maintainable and extendable. Transcriptome builds or unigene sets occupy the focal point of the system. Several of the five current species-specific unigenes were used to design microarrays and SNP resources. The ForestTreeDB web application provides the means for multiple combination database queries. It presents the user with a list of discrete queries to retrieve and download large EST datasets or sequences from precompiled unigene assemblies. Functional annotation assignment is not trivial in conifers which are distantly related to angiosperm model plants. Optimal annotations are achieved through database queries that integrate results from several procedures based open-source tools. ForestTreeDB aims to facilitate sequence mining of coherent annotations in multiple species to support comparative genomic approaches. We plan to continuously enrich ForestTreeDB with other resources through collaborations with other genomic projects.

SUBMITTER: Pavy N

PROVIDER: S-EPMC1716727 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND: Large-scale sequencing efforts produced millions of Expressed Sequence Tags (ESTs) collectively representing differentiated biochemical and functional states. Analysis of these EST libraries reveals differential gene expressions, and therefore EST data sets constitute valuable resources for comparative transcriptomics. To translate differentially expressed genes into a better understanding of the underlying biological phenomena, existing microarray analysis approaches usually involve the integration of gene expression with Gene Ontology (GO) databases to derive comparable functional profiles. However, methods are not available yet to process EST-derived transcription maps to enable GO-based global functional profiling for comparative transcriptomics in a high throughput manner. RESULTS: Here we present GO-Diff, a GO-based functional profiling approach towards high throughput EST-based gene expression analysis and comparative transcriptomics. Utilizing holistic gene expression information, the software converts EST frequencies into EST Coverage Ratios of GO Terms. The ratios are then tested for statistical significances to uncover differentially represented GO terms between the compared transcriptomes, and functional differences are thus inferred. We demonstrated the validity and the utility of this software by identifying differentially represented GO terms in three application cases: intra-species comparison; meta-analysis to test a specific hypothesis; inter-species comparison. GO-Diff findings were consistent with previous knowledge and provided new clues for further discoveries. A comprehensive test on the GO-Diff results using series of comparisons between EST libraries of human and mouse tissues showed acceptable levels of consistency: 61% for human-human; 69% for mouse-mouse; 47% for human-mouse. CONCLUSION: GO-Diff is the first software integrating EST profiles with GO knowledge databases to mine functional differentiation between biological systems, e.g. tissues of the same species or the same tissue cross species. With rapid accumulation of EST resources in the public domain and expanding sequencing effort in individual laboratories, GO-Diff is useful as a screening tool before undertaking serious expression studies.

Dataset Information

ForestTreeDB: a database dedicated to the mining of tree transcriptomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets