ArticleBMC bioinformatics2005
'PACLIMS': a component LIM system for high-throughput functional genomic analysis.
Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Computer aided data acquisition tool for high-throughput phenotyping of plant populations.Plant methods · 2009Article
- Development of a database system for mapping insertional mutations onto the mouse genome with large-scale experimental data.BMC genomics · 2009Article
- eCOMPAGT -- efficient combination and management of phenotypes and genotypes for genetic epidemiology.BMC bioinformatics · 2009Article
- Functional genomics in the rice blast fungus to unravel the fungal pathogenicity.Journal of Zhejiang University. Science. B · 2008Review
- SNPLims: a data management system for genome wide association studies.BMC bioinformatics · 2008Article
- Design and implementation of a generalized laboratory data model.BMC bioinformatics · 2007Article
- Pathogen profiling for disease management and surveillance.Nature reviews. Microbiology · 2007Review
- Laboratory Information Management Software for genotyping workflows: applications in high throughput crop genotyping.BMC bioinformatics · 2006Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent advances in sequencing techniques leading to cost reduction have resulted in the generation of a growing number of sequenced eukaryotic genomes. Computational tools greatly assist in defining open reading frames and assigning tentative annotations. However, gene functions cannot be asserted without biological support through, among other things, mutational analysis. In taking a genome-wide approach to functionally annotate an entire organism, in this application the approximately 11,000 predicted genes in the rice blast fungus (Magnaporthe grisea), an effective platform for tracking and storing both the biological materials created and the data produced across several participating institutions was required.
resultsThe platform designed, named PACLIMS, was built to support our high throughput pipeline for generating 50,000 random insertion mutants of Magnaporthe grisea. To be a useful tool for materials and data tracking and storage, PACLIMS was designed to be simple to use, modifiable to accommodate refinement of research protocols, and cost-efficient. Data entry into PACLIMS was simplified through the use of barcodes and scanners, thus reducing the potential human error, time constraints, and labor. This platform was designed in concert with our experimental protocol so that it leads the researchers through each step of the process from mutant generation through phenotypic assays, thus ensuring that every mutant produced is handled in an identical manner and all necessary data is captured.
conclusionMany sequenced eukaryotes have reached the point where computational analyses are no longer sufficient and require biological support for their predicted genes. Consequently, there is an increasing need for platforms that support high throughput genome-wide mutational analyses. While PACLIMS was designed specifically for this project, the source and ideas present in its implementation can be used as a model for other high throughput mutational endeavors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.