TreeSNPs: a laboratory information management system (LIMS) dedicated to SNP discovery in trees

Clément, Sébastien; Fillon, Joël; Bousquet, Jean; Beaulieu, Jean

doi:10.1007/s11295-009-0261-6

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…Most SSRs and SNPs (3546 [74.8%] and 41 152 [93.1%], respectively) occur in UniGenes with a putative ORF (2966 and 10 756, respectively), and 1% occurs in start/end codons. These numbers are difficult to compare to other conifers because of the different algorithms used for detection [2,18,39]. Due to the enormous size of the pine genome, ORF-based SSRs and SNPs are advantageous since they will reduce the mapping efforts required for the development of high-density maps and association studies.…”

Section: Utility and Discussionmentioning

confidence: 99%

“…This effort to combine and inter-relate a great variety of different information should be acknowledged, even though EST pre-processing is not optimal. TreeSNPs [18] and PineSAP [19] are databases exclusively devoted to single nucleotide polymorphisms (SNPs) in Picea and Pinus species, respectively. Recently, Parchman and co-workers [2] described the first high-throughput analysis of a pine species, but no database was created for this.…”

Section: Introductionmentioning

confidence: 99%

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EuroPineDB: a high-coverage web database for maritime pine transcriptome

et al. 2011

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BackgroundPinus pinaster is an economically and ecologically important species that is becoming a woody gymnosperm model. Its enormous genome size makes whole-genome sequencing approaches are hard to apply. Therefore, the expressed portion of the genome has to be characterised and the results and annotations have to be stored in dedicated databases.DescriptionEuroPineDB is the largest sequence collection available for a single pine species, Pinus pinaster (maritime pine), since it comprises 951 641 raw sequence reads obtained from non-normalised cDNA libraries and high-throughput sequencing from adult (xylem, phloem, roots, stem, needles, cones, strobili) and embryonic (germinated embryos, buds, callus) maritime pine tissues. Using open-source tools, sequences were optimally pre-processed, assembled, and extensively annotated (GO, EC and KEGG terms, descriptions, SNPs, SSRs, ORFs and InterPro codes). As a result, a 10.5× P. pinaster genome was covered and assembled in 55 322 UniGenes. A total of 32 919 (59.5%) of P. pinaster UniGenes were annotated with at least one description, revealing at least 18 466 different genes. The complete database, which is designed to be scalable, maintainable, and expandable, is freely available at: http://www.scbi.uma.es/pindb/. It can be retrieved by gene libraries, pine species, annotations, UniGenes and microarrays (i.e., the sequences are distributed in two-colour microarrays; this is the only conifer database that provides this information) and will be periodically updated. Small assemblies can be viewed using a dedicated visualisation tool that connects them with SNPs. Any sequence or annotation set shown on-screen can be downloaded. Retrieval mechanisms for sequences and gene annotations are provided.ConclusionsThe EuroPineDB with its integrated information can be used to reveal new knowledge, offers an easy-to-use collection of information to directly support experimental work (including microarray hybridisation), and provides deeper knowledge on the maritime pine transcriptome.

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Section: Utility and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EuroPineDB: a high-coverage web database for maritime pine transcriptome

et al. 2011

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“…Many researches have proved that multiple identity user system generally leads to better efficiency than stand‐alone system . Multi‐user detection learning scenario can increase the security and professionalism of the system.…”

Section: Target Laboratory Scenario Requirements and System Modules Smentioning

confidence: 99%

“…Multi‐user detection learning scenario can increase the security and professionalism of the system. Great deals of experimental studies comparing multi‐user interaction patterns indicate that management work more efficient and orderly when the user identity is separate .…”

Section: Target Laboratory Scenario Requirements and System Modules Smentioning

confidence: 99%

Construction and evaluation of PHP‐based management and training system for electrical power laboratory

et al. 2016

Comp Applic In Engineering

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ABSTRACT:The online management and training of information for laboratories has rarely been discussed in the traditional education system design. However, it plays a vital role in informatization and automation of electrical power laboratory. Using PHP: Hypertext Preprocessor (PHP), we have designed and developed an online management and training environment targeted for electrical power laboratory. In this article, the Model-ViewController (MVC) architecture cooperative designed for the management and training system modules was provided, and key steps of developing such modules using PHP were described. A questionnaire survey was conducted for students to use the system, and their attitudes toward the system were calculated and analyzed. Results showed that management and training system for electrical power laboratory can lead to preferable learning interest and outcome. ß 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:371-381, 2016; View this article online at wileyonlinelibrary.com/journal/cae;

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“…The researcher community has also proposed different tools focused on the management and the analysis of different biological data. For instance, TreeSNPs [2] manages data generated through single nucleotide polymorphism (SNP) identification. Differently, Screensaver [12] supports the storage and the comparison of small molecule and RNA screening data.…”

Section: Introductionmentioning

confidence: 99%

LAS: A Software Platform to Support Oncological Data Management

et al. 2012

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The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data quality.

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TreeSNPs: a laboratory information management system (LIMS) dedicated to SNP discovery in trees

Cited by 10 publications

References 8 publications

EuroPineDB: a high-coverage web database for maritime pine transcriptome

EuroPineDB: a high-coverage web database for maritime pine transcriptome

Construction and evaluation of PHP‐based management and training system for electrical power laboratory

LAS: A Software Platform to Support Oncological Data Management

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