Supervised Lowess normalization of comparative genome hybridization data: application to lactococcal strain comparisons.
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PROVIDER: PRJNA102545 | ENA |
REPOSITORIES: ENA
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Project description:Background Array-based comparative genome hybridization (aCGH) is commonly used to determine the genomic content of bacterial strains. In aCGH data, systematic errors are comparable to those occurring in transcriptome data. However, especially for microbes, an additional source of variation exists: differences in hybridization due to gene sequence divergence between the strains hybridized. Current normalization methods do not take this source of variation into consideration. Results We present Supervised Lowess, or S-Lowess, an application of the subset Lowess normalization method that does take difference in genomic content into account. The performance of S-Lowess was assessed on aCGH experiments in which differentially labeled genomic DNA fragments of Lactococcus lactis IL1403 and L. lactis MG1363 strains were hybridized to IL1403 microarrays. Since both genome sequences are known (they have only on average 85 % sequence identity), the success rate in detecting deletions in the MG1363 genome of different aCGH normalization methods can be compared. S-Lowess detects 97% of the deletions, whereas other aCGH normalization methods detect up to only 60% of the deletions. Conclusions S-Lowess removes systematic errors from dual-dye aCGH data in two steps: (i) determination of likely homologous genes (LHG); and (ii) estimation of correction factors for systematic errors from spots of LHG and subset Lowess normalization of the remaining spots using these correction factors. It is implemented in a user-friendly web-tool accessible from http://bioinformatics.biol.rug.nl/websoftware/s-lowess. We demonstrate that it outperforms existing normalization methods and maximizes the number of detectable genomic deletions or duplications from microbial aCGH data. Keywords: comparative genome hybridisation In this study, 4 aCGH comparisons (slides) between L. lactis MG1363 and L. lactis IL1403 were performed (including dye swap with biological replicates; see also supplementary materials). The resulting aCGH slide signals were normalized using the different 'likely homologous gene' (LHG) sets (see above) yielding ratios of signals of labelled DNA of MG1363 over those of IL1403. A maximum of 8 ratios per amplicon (gene) were obtained for the 4 hybridized slides (each with 2 replicate spots per amplicon). Only genes with at least 5 measurements were used in this study. The normalization methods evaluated in this study are: a) no normalization, b) total signal normalization, c) grid-based Lowess (implemented in PreP; f = 0.7) [GarcM-CM--a de la Nava, J et al. 2003. Bioinformatics 19:2328-2329], and d) S-Lowess using different subsets of conserved lactococcal genes (for details see above). Results with the MANOR R package (spatial normalization; standard parameters) [Neuvial P, et al. 2006. BMC Bioinformatics 7:264; Liva S, et al. 2006. Nucleic Acids Res 34:W477-W481] are shown in our supplementary materials.
2008-07-19 | E-GEOD-9039 | biostudies-arrayexpress
Project description:Background Array-based comparative genome hybridization (aCGH) is commonly used to determine the genomic content of bacterial strains. In aCGH data, systematic errors are comparable to those occurring in transcriptome data. However, especially for microbes, an additional source of variation exists: differences in hybridization due to gene sequence divergence between the strains hybridized. Current normalization methods do not take this source of variation into consideration. Results We present Supervised Lowess, or S-Lowess, an application of the subset Lowess normalization method that does take difference in genomic content into account. The performance of S-Lowess was assessed on aCGH experiments in which differentially labeled genomic DNA fragments of Lactococcus lactis IL1403 and L. lactis MG1363 strains were hybridized to IL1403 microarrays. Since both genome sequences are known (they have only on average 85 % sequence identity), the success rate in detecting deletions in the MG1363 genome of different aCGH normalization methods can be compared. S-Lowess detects 97% of the deletions, whereas other aCGH normalization methods detect up to only 60% of the deletions. Conclusions S-Lowess removes systematic errors from dual-dye aCGH data in two steps: (i) determination of likely homologous genes (LHG); and (ii) estimation of correction factors for systematic errors from spots of LHG and subset Lowess normalization of the remaining spots using these correction factors. It is implemented in a user-friendly web-tool accessible from http://bioinformatics.biol.rug.nl/websoftware/s-lowess. We demonstrate that it outperforms existing normalization methods and maximizes the number of detectable genomic deletions or duplications from microbial aCGH data. Keywords: comparative genome hybridisation
2008-07-20 | GSE9039 | GEO
Project description:BACKGROUND: Array-based comparative genome hybridization (aCGH) is commonly used to determine the genomic content of bacterial strains. Since prokaryotes in general have less conserved genome sequences than eukaryotes, sequence divergences between the genes in the genomes used for an aCGH experiment obstruct determination of genome variations (e.g. deletions). Current normalization methods do not take into consideration sequence divergence between target and microarray features and therefore cannot distinguish a difference in signal due to systematic errors in the data or due to sequence divergence. RESULTS: We present supervised Lowess, or S-Lowess, an application of the subset Lowess normalization method. By using a predicted subset of array features with minimal sequence divergence between the analyzed strains for the normalization procedure we remove systematic errors from dual-dye aCGH data in two steps: (1) determination of a subset of conserved genes (i.e. likely conserved genes, LCG); and (2) using the LCG for subset Lowess normalization. Subset Lowess determines the correction factors for systematic errors in the subset of array features and normalizes all array features using these correction factors. The performance of S-Lowess was assessed on aCGH experiments in which differentially labeled genomic DNA fragments of Lactococcus lactis IL1403 and L. lactis MG1363 strains were hybridized to IL1403 DNA microarrays. Since both genomes are sequenced and gene deletions identified, the success rate of different aCGH normalization methods in detecting these deletions in the MG1363 genome were determined. S-Lowess detects 97% of the deletions, whereas other aCGH normalization methods detect up to only 60% of the deletions. CONCLUSION: S-Lowess is implemented in a user-friendly web-tool accessible from http://bioinformatics.biol.rug.nl/websoftware/s-lowess. We demonstrate that it outperforms existing normalization methods and maximizes detection of genomic variation (e.g. deletions) from microbial aCGH data.
| S-EPMC2275246 | biostudies-literature
Project description:A major challenge in utilizing microarray technologies to measure nucleic acid abundances is 'normalization', the goal of which is to separate biologically meaningful signal from other confounding sources of signal, often due to unavoidable technical factors. It is intuitively clear that true biological signal and confounding factors need to be simultaneously considered when performing normalization. However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date.We show here that failing to include all study-specific biological and technical variables when performing normalization leads to biased downstream analyses. We propose a general normalization framework that fits a study-specific model employing every known variable that is relevant to the expression study. The proposed method is generally applicable to the full range of existing probe designs, as well as to both single-channel and dual-channel arrays. We show through real and simulated examples that the method has favorable operating characteristics in comparison to some of the most highly used normalization methods.An R package called snm implementing the methodology will be made available from Bioconductor (http://bioconductor.org).jstorey@princeton.eduSupplementary data are available at Bioinformatics online.
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Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling
2009-02-17 | GSE12085 | GEO