ArticleBMC genomics2008
Optimal control and analysis of two-color genomotyping experiments using bacterial multistrain arrays.
Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Comparison of alternative mixture model methods to analyze bacterial CGH experiments with multi-genome arrays.BMC research notes · 2014Article
- Efficient oligonucleotide probe selection for pan-genomic tiling arrays.BMC bioinformatics · 2009Article
- Improved analysis of bacterial CGH data beyond the log-ratio paradigm.BMC bioinformatics · 2009Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroarray comparative genomic hybridization (aCGH) evaluates the distribution of genes of sequenced bacterial strains among unsequenced strains of the same or related species. As genomic sequences from multiple strains of the same species become available, multistrain microarrays are designed, containing spots for every unique gene in all sequenced strains. To perform two-color aCGH experiments with multistrain microarrays, the choice of control sample can be the genomic DNA of one strain or a mixture of all the strains used in the array design. This important problem has no universally accepted solution.
resultsWe performed a comparative study of the two control sample options with a Streptococcus pneumoniae microarray designed with three fully sequenced strains. We separately hybridized two of these strains (R6 and G54) as test samples using the third strain alone (TIGR4) or a mixture of the three strains as control. We show that for both types of control it is advantageous to analyze spots in separate sets according to their expected control channel signal (5-15% AUC increase). Following this analysis, the use of a mix control leads to higher accuracies (5% increase). This enhanced performance is due to gains in sensitivity (21% increase, p = 0.001) that compensate minor losses in specificity (5% decrease, p = 0.014).
conclusionThe use of a single strain control increases the error rate in genes that are part of the accessory genome, where more variation across unsequenced strains is expected, further justifying the use of the mix control.
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