Detection of divergent genes in microbial aCGH experiments

BACKGROUND: Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach. METHODS: We introduce a more efficient method for analyzing microbial aCGH data using a finite mixture model and a data rotation scheme. Using the average posterior probabilities from the model fitted to log-ratios before and after rotation, we get a score for each gene, and demonstrate its advantages for ranking and detecting divergent genes with enlarged specificity and sensitivity. RESULTS: The procedure is tested and compared to other approaches on simulated data sets, as well as on four experimental validation data sets for aCGH analysis on fully sequenced strains of Staphylococcus aureus and Streptococcus pneumoniae. CONCLUSION: When tested on simulated data as well as on four different experimental validation data sets from experiments with only fully sequenced strains, our procedure out-competes the standard procedures of using a simple log-ratio cutoff for classification into present and divergent genes

Supervised Lowess normalization of comparative genome hybridization data – application to lactococcal strain comparisons

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. We demonstrate that it outperforms existing normalization methods and maximizes detection of genomic variation (e.g. deletions) from microbial aCGH data.

Efficient oligonucleotide probe selection for pan-genomic tiling arrays

Background: Array comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species' pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome. Results: This paper presents a new probe selection algorithm (PanArray) that can tile multiple whole genomes using a minimal number of probes. Unlike arrays built on clustered gene families, PanArray uses an unbiased, probe-centric approach that does not rely on annotations, gene clustering, or multi-alignments. Instead, probes are evenly tiled across all sequences of the pangenome at a consistent level of coverage. To minimize the required number of probes, probes conserved across multiple strains in the pan-genome are selected first, and additional probes are used only where necessary to span polymorphic regions of the genome. The viability of the algorithm is demonstrated by array designs for seven different bacterial pan-genomes and, in particular, the design of a 385,000 probe array that fully tiles the genomes of 20 different Listeria monocytogenes strains with overlapping probes at greater than twofold coverage. Conclusion: PanArray is an oligonucleotide probe selection algorithm for tiling multiple genome sequences using a minimal number of probes. It is capable of fully tiling all genomes of a species on a single microarray chip. These unique pan-genome tiling arrays provide maximum flexibility for the analysis of both known and uncharacterized strains.https://doi.org/10.1186/1471-2105-10-29

Using comparative genomic hybridization to survey genomic sequence divergence across species: a proof-of-concept from Drosophila

<p>Abstract</p> <p>Background</p> <p>Genome-wide analysis of sequence divergence among species offers profound insights into the evolutionary processes that shape lineages. When full-genome sequencing is not feasible for a broad comparative study, we propose the use of array-based comparative genomic hybridization (aCGH) in order to identify orthologous genes with high sequence divergence. Here we discuss experimental design, statistical power, success rate, sources of variation and potential confounding factors. We used a spotted PCR product microarray platform from <it>Drosophila melanogaster </it>to assess sequence divergence on a gene-by-gene basis in three fully sequenced heterologous species (<it>D. sechellia</it>, <it>D. simulans</it>, and <it>D. yakuba</it>). Because complete genome assemblies are available for these species this study presents a powerful test for the use of aCGH as a tool to measure sequence divergence.</p> <p>Results</p> <p>We found a consistent and linear relationship between hybridization ratio and sequence divergence of the sample to the platform species. At higher levels of sequence divergence (< 92% sequence identity to <it>D. melanogaster</it>) ~84% of features had significantly less hybridization to the array in the heterologous species than the platform species, and thus could be identified as "diverged". At lower levels of divergence (≥ 97% identity), only 13% of genes were identified as diverged. While ~40% of the variation in hybridization ratio can be accounted for by variation in sequence identity of the heterologous sample relative to <it>D. melanogaster</it>, other individual characteristics of the DNA sequences, such as GC content, also contribute to variation in hybridization ratio, as does technical variation.</p> <p>Conclusions</p> <p>Here we demonstrate that aCGH can accurately be used as a proxy to estimate genome-wide divergence, thus providing an efficient way to evaluate how evolutionary processes and genomic architecture can shape species diversity in non-model systems. Given the increased number of species for which microarray platforms are available, comparative studies can be conducted for many interesting lineages in order to identify highly diverged genes that may be the target of natural selection.</p

Comparative and functional genomics reveals genetic diversity and determinants of host specificity among reference strains and a large collection of Chinese isolates of the phytopathogen Xanthomonas campestris pv. campestris

Construction of a microarray based on the genome of Xanthomonas campestris pv.campestris (Xcc), and its use to analyse 18 other virulent Xcc strains, revealed insights into the genetic diversity and determinants of host specificity of Xcc strains

Normalization and centering of array-based heterologous genome hybridization based on divergent control probes

<p>Abstract</p> <p>Background</p> <p>Hybridization of heterologous (non-specific) nucleic acids onto arrays designed for model-organisms has been proposed as a viable genomic resource for estimating sequence variation and gene expression in non-model organisms. However, conventional methods of normalization that assume equivalent distributions (such as quantile normalization) are inappropriate when applied to non-specific (heterologous) hybridization. We propose an algorithm for normalizing and centering intensity data from heterologous hybridization that makes no prior assumptions of distribution, reduces the false appearance of homology, and provides a way for researchers to confirm whether heterologous hybridization is suitable.</p> <p>Results</p> <p>Data are normalized by adjusting for Gibbs free energy binding, and centered by adjusting for the median of a common set of control probes assumed to be equivalently dissimilar for all species. This procedure was compared to existing approaches and found to be as successful as Loess normalization at detecting sequence variations (deletions) and even more successful than quantile normalization at reducing the accumulation of false positive probe matches between two related nematode species, <it>Caenorhabditis elegans </it>and <it>C. briggsae</it>. Despite the improvements, we still found that probe fluorescence intensity was too poorly correlated with sequence similarity to result in reliable detection of matching probe sequence.</p> <p>Conclusions</p> <p>Cross-species hybridizations can be a way to adapt genome-enabled tools for closely related non-model organisms, but data must be appropriately normalized and centered in a way that accommodates hybridization of nucleic acids with diverged sequence. For short, 25-mer probes, hybridization intensity alone may be insufficiently correlated with sequence similarity to allow reliable inference of homology at the probe level.</p

The Construction and Use of a <i>Francisella tularensis</i> DNA Microarray

A DNA microarray was designed and constructed using the genome sequence of the highly virulent obligate intracellular pathogen Francisella tularensis strain Schu S4. The microarray was optimised and then tested by performing a comparative genomics study on Francisella strains. The microarray was used to distinguish between Francisella strains at the subspecies level, detecting differences between the genomes of the subspecies at a similar rate to differences previously published from Francisella comparative genomics studies. Further analysis of the genomic differences identified between subspecies using the microarray has provided some suggestions as to the genetic basis for the relative attenuation of one subspecies, and similarly, differences identified between the F. tularensis live vaccine strain and its progenitor strain provided some clues as the genetic basis for the attenuation of the vaccine strain. The microarray was also used to carry out functional genomics studies on Francisella novicida cultured under in vitro stress conditions: iron starvation, oxidative stress, elevated temperature, and acidic pH. A number of genes were regulated in response to each of these conditions, and a detailed analysis of the data has provided insights into the stress response of Francisella, and some of the mechanisms that it may employ upon encountering similar stresses in vivo

Intrastrain genomic and phenotypic variability of the commercial Saccharomyces cerevisiae strain Zymaflore VL1 reveals microevolutionary adaptation to vineyard environments

The maintenance of microbial species in different environmental conditions is associated with adaptive microevolutionary changes that are shown here to occur within the descendants of the same strain in comparison with the commercial reference strain. However, scarce information is available regarding changes that occur among strain descendants during their persistence in nature. Herein we evaluate genome variations among four isolates of the commercial winemaking strain Saccharomyces cerevisiae Zymaflore VL1 that were re-isolated from vineyards surrounding wineries where this strain was applied during several years, in comparison with the commercial reference strain. Comparative genome hybridization showed amplification of 14 genes among the recovered isolates being related with mitosis, meiosis, lysine biosynthesis, galactose and asparagine catabolism, besides 9 Ty elements. The occurrence of microevolutionary changes was supported by DNA sequencing that revealed 339-427 SNPs and 12-62 indels. Phenotypic screening and metabolic profiles also distinguished the recovered isolates from the reference strain. We herein show that the transition from nutrient-rich musts to nutritionally scarce natural environments induces adaptive responses and microevolutionary changes promoted by Ty elements and by nucleotide polymorphisms that were not detected in the reference strain.Ricardo Franco-Duarte and Ines Mendes are recipients of a fellowship from the Portuguese Science Foundation, FCT (SFRH/BD/48591/2008, SFRH/BD/74798/2010, respectively). Financial support was obtained from FEDER funds through the program COMPETE, by national funds through FCT by the projects FCOMP-01-0124-008775 (PTDC/AGR-ALI/103392/2008) and PTDC/AGR-ALI/121062/2010, and through the strategic funding UID/BIA/04050/2013.info:eu-repo/semantics/publishedVersio