A novel aCGH method for non-model organisms for assessing genomes and de-novo transcriptome sequencing reads (Staphylococcus aureus)
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ABSTRACT:
ORGANISM(S): Staphylococcus aureus
PROVIDER: GSE51148 | GEO | 2014/05/19
SECONDARY ACCESSION(S): PRJNA227764
REPOSITORIES: GEO
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Project description:Genomes and transcriptomes of non-model organisms can be analyzed using next-generation sequencing technologies, but de-novo sequencing and annotating a full eukaryotic genome is still challenging. So, -omics experimentation with non-model organisms requires a suite of technologies to obtain reliable results in a cost-effective manner. Here, a novel method for microarray-based genome analysis is presented which is especially suitable for non-model organisms. We show that it is useful for complementing regular aCGH analyses and for evaluating transcriptome next-generation sequencing reads. The principle of the method is straightforward: feature intensities obtained after hybridizing the test genome are compared with the feature intensities of a control hybridization. The control hybridization is performed with negative control probes (no targets in the control sample), and with positive control probes (with targets in the control sample). The method has in principle a resolution of a single probe and it does not depend on the structural information of a reference genome: the genomic ordering of probe targets is irrelevant. In a test, analyzing the genome content of a sequenced bacterial strain: Staphylococcus aureus MRSA252, this approach proved to be successful demonstrated by receiver operating characteristic area under the curve values larger than 0.9995.
2014-05-19 | GSE52121 | GEO
Project description:Genomes and transcriptomes of non-model organisms can be analyzed using next-generation sequencing technologies, but de-novo sequencing and annotating a full eukaryotic genome is still challenging. So, -omics experimentation with non-model organisms requires a suite of technologies to obtain reliable results in a cost-effective manner. Here, a novel method for microarray-based genome analysis is presented which is especially suitable for non-model organisms. We show that it is useful for complementing regular aCGH analyses and for evaluating transcriptome next-generation sequencing reads. The principle of the method is straightforward: feature intensities obtained after hybridizing the test genome are compared with the feature intensities of a control hybridization. The control hybridization is performed with negative control probes (no targets in the control sample), and with positive control probes (with targets in the control sample). The method has in principle a resolution of a single probe and it does not depend on the structural information of a reference genome: the genomic ordering of probe targets is irrelevant. In a test, analyzing the genome content of a sequenced bacterial strain: Staphylococcus aureus MRSA252, this approach proved to be successful demonstrated by receiver operating characteristic area under the curve values larger than 0.9995. DNA from eleven Staphylococcus aureus strains was extracted in three replicates, fragmented, and hybridized onto the S. aureus multistrain microarray. DNA from MRSA252 was used as common reference, but this channel was omitted in further analyses.
2014-05-19 | E-GEOD-51148 | biostudies-arrayexpress
Project description:Genomes and transcriptomes of non-model organisms can be analyzed using next-generation sequencing technologies, but de-novo sequencing and annotating a full eukaryotic genome is still challenging. So, -omics experimentation with non-model organisms requires a suite of technologies to obtain reliable results in a cost-effective manner. Here, a novel method for microarray-based genome analysis is presented which is especially suitable for non-model organisms. We show that it is useful for complementing regular aCGH analyses and for evaluating transcriptome next-generation sequencing reads. The principle of the method is straightforward: feature intensities obtained after hybridizing the test genome are compared with the feature intensities of a control hybridization. The control hybridization is performed with negative control probes (no targets in the control sample), and with positive control probes (with targets in the control sample). The method has in principle a resolution of a single probe and it does not depend on the structural information of a reference genome: the genomic ordering of probe targets is irrelevant. In a test, analyzing the genome content of a sequenced bacterial strain: Staphylococcus aureus MRSA252, this approach proved to be successful demonstrated by receiver operating characteristic area under the curve values larger than 0.9995. DNA from twelve Danio rerio individuals was extracted, fragmented, and hybridized onto the D. rerio microarray. DNA from the pool was used as common reference, but this channel was omitted in further analyses.
2014-05-19 | E-GEOD-52121 | biostudies-arrayexpress
Project description:The Affymetrix oligonucleotide microarrays measure gene expression by quantifying intensity of fluorescently labeled gene fragments that bind to sets of 25-mer oligonucleotide probes on the chip with specific sequences tailored to be complementary to the target genes. Each gene is associated with a "probe set" containing several pairs (usually 11) of "perfect match" (perfectly complementary to target sequence) and "mismatch" (different base at position 13 of 25) probes. The raw measurements of each probe set consist of a set of intensities from the probes, which require in silico preprocessing by (1) correcting for background variability, (2) normalizing intensities across samples, and (3) summarizing intensities across the probe set into a single expression value. The output of the summarization step corresponds to the background-adjusted value for the mRNA of interest. We preprocess using GCRMA, which corrects for background variability by accounting for optical noise, probe affinity, and mismatch probe adjustment; normalizes intensities by quantile normalization; and summarizes intensities using a median polish method. To minimize preprocessing batch effects, it is desirable to preprocess all samples in the dataset together. However, preprocessing across multple platforms requires a consolidation of probes with identical sequences, precluding global preprocessing on datasets with multiple platforms using the standard preprocessing pipelines. To address this problem, we have developed and applied a custom preprocessing pipeline to combine the raw .CEL files from multiple platforms that share the same probe sets.
2014-09-18 | GSE60486 | GEO
Project description:Background: Genome-wide detection of single feature polymorphisms (SFP) in swine using transcriptome profiling of day 25 placental RNA by contrasting probe intensities from either Meishan or an occidental composite breed with Affymetrix porcine microarrays is presented. A linear mixed model analysis was used to identify significant breed-by-probe interactions. Results: Gene specific linear mixed models were fit to each of the log2 transformed probe intensities on these arrays, using fixed effects for breed, probe, breed-by-probe interaction, and a random effect for array. After surveying the day 25 placental transcriptome, 789 probes with a q-value ≤ 0.05 and |fold change| ≥ 2 for the breed-by-probe interaction were identified as candidates containing SFP. To address the quality of the bioinformatics approach, universal pyrosequencing assays were designed from Affymetrix exemplar sequences to independently assess polymorphisms within a subset of probes. Of those probes sampled from high-, medium-, and low-ranking categories, 20 of 27 were confirmed by pyrosequencing to contain SFPs. In most cases, the 25-mer probe sequence printed on the microarray diverged from Meishan, not occidental crosses. This analysis was used to define a set of highly reliable predicted SFPs according to their probability scores. Conclusions: By this method we detected transition and transversion single nucleotide polymorphisms, as well as insertions/deletions. These results demonstrate that this approach can identify polymorphisms between two breeds and/or lines of any species for which a short oligonucleotide array is available, and can be used to rapidly develop markers for genetic mapping and association analysis in species where high density genotyping platforms are otherwise unavailable. SNPs and INDELS discovered by this approach have been publicly deposited in NCBI’s SNP repository dbSNP. This method is an attractive bioinformatics tool for uncovering breed-by-probe interactions, for rapidly identifying expressed SNPs, for investigating potential functional correlations between gene expression and breed polymorphisms, and is robust enough to be used on any Affymetrix gene expression platform. Keywords: Transcriptional profiling of Day 25 porcine placentas
2009-02-06 | GSE10441 | GEO
Project description:Background: Genome-wide detection of single feature polymorphisms (SFP) in swine using transcriptome profiling of day 25 placental RNA by contrasting probe intensities from either Meishan or an occidental composite breed with Affymetrix porcine microarrays is presented. A linear mixed model analysis was used to identify significant breed-by-probe interactions. Results: Gene specific linear mixed models were fit to each of the log2 transformed probe intensities on these arrays, using fixed effects for breed, probe, breed-by-probe interaction, and a random effect for array. After surveying the day 25 placental transcriptome, 789 probes with a q-value ≤ 0.05 and |fold change| ≥ 2 for the breed-by-probe interaction were identified as candidates containing SFP. To address the quality of the bioinformatics approach, universal pyrosequencing assays were designed from Affymetrix exemplar sequences to independently assess polymorphisms within a subset of probes. Of those probes sampled from high-, medium-, and low-ranking categories, 20 of 27 were confirmed by pyrosequencing to contain SFPs. In most cases, the 25-mer probe sequence printed on the microarray diverged from Meishan, not occidental crosses. This analysis was used to define a set of highly reliable predicted SFPs according to their probability scores. Conclusions: By this method we detected transition and transversion single nucleotide polymorphisms, as well as insertions/deletions. These results demonstrate that this approach can identify polymorphisms between two breeds and/or lines of any species for which a short oligonucleotide array is available, and can be used to rapidly develop markers for genetic mapping and association analysis in species where high density genotyping platforms are otherwise unavailable. SNPs and INDELS discovered by this approach have been publicly deposited in NCBI’s SNP repository dbSNP. This method is an attractive bioinformatics tool for uncovering breed-by-probe interactions, for rapidly identifying expressed SNPs, for investigating potential functional correlations between gene expression and breed polymorphisms, and is robust enough to be used on any Affymetrix gene expression platform. Keywords: Transcriptional profiling of Day 25 porcine placentas
2008-02-09 | GSE10447 | GEO
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2010-12-31 | GSE24269 | GEO
Project description:Microbial community analysis with DNA oligonucleotide microarrays targeting ribosomal RNA (rRNA) provides a highly parallel interrogation of nucleic acids isolated from environmental samples. High fidelity readout is essential for accurate interpretation of hybridisations. We describe the hybridisation of in vitro transcribed 16S rRNA from an uncontaminated and 2,4,6-trinitrotoluene contaminated soil to an oligonucleotide microarray containing group- and species-specific perfect match (PM) probes and their 2 corresponding mismatch (MM) probes. Thermal dissociation analysis was used to determine the specificity of each PM-MM probe set. Functional ANOVA often discriminated PM-MM probe sets when Td values (temperature at 50% probe-target dissociation) could not. Maximum discrimination for many PM and MM probes often occurred at temperatures greater than the Td. Comparison of signal intensities measured prior to dissociation analysis from hybridisations of the two soil samples revealed significant differences in domain-, group- and species-specific probes. Functional ANOVA showed significantly different dissociation curves for 11 PM probes when hybridisations from the two soil samples were compared, even though initial signal intensities for 3 of the 11 did not vary. This approach provides a highly parallel, multi-level analysis that incorporates MM probes and dissociation curves into high fidelity microarray analysis of complex environmental nucleic acid profiles. Keywords: Microbial diversity, thermal dissociation analysis
2005-10-26 | GSE3499 | GEO
Project description:The objective of this work was to determine the effectiveness of cross-hybridization of gDNA from five native soil nematodes to an Affymetrix Caenorhabditis elegans tiling array. Cross-hybridization experiments using C. briggsae, for which genome information is available, allowed hybridisation intensities to be correlated with known sequence differences. Initial analysis of data by conventional array-based Comparative Genomic Hybridization (aCGH) techniques at the chip level lead to misleading results due to an artefact from the combination of scaling, bandwidth smoothing, and differential GC content in exon and intron regions. To circumvent this artefact, individual probes were instead normalized and centered by adjusting for probe-specific thermodynamic binding affinity. However, cross-hybridization of C. briggsae DNA revealed that the resultant probe intensities alone were still uncorrelated to sequence similarity below 90% identity. Below 90% similarity, all probes hybridize uniformly poorly, and above 90% similarity the hybridization differences are not large enough to detect over background, therefore, no 'threshold' ratio of hybridization intensity was successful at identifying probes with similarity to the heterologous genome. In light of the observations described here, we suggest that the criteria for replication and verification of gene expression profiles generated from cross-species microarray hybridizations be more stringent than typically adopted for con-specific hybridizations.
2010-08-18 | GSE23667 | GEO
Project description:Microarrays are useful tools for detecting and quantifying specific functional and phylogenetic genes in natural microbial communities. In order to track uncultivated microbial genotypes and their close relatives in an environmental context, we designed and implemented a âgenome proxyâ microarray that targets microbial genome fragments recovered directly from the environment. Fragments consisted of sequenced clones from large-insert genomic libraries from microbial communities in Monterey Bay, the Hawaii Ocean Time-series station ALOHA, and Antarctic coastal waters. In a prototype array, we designed probe sets to thirteen of the sequenced genome fragments and to genomic regions of the cultivated cyanobacterium Prochlorococcus MED4. Each probe set consisted of multiple 70-mers, each targeting an individual ORF, and distributed along each ~40-160kbp contiguous genomic region. The targeted organisms or clones, and close relatives, were hybridized to the array both as pure DNA mixtures and as additions of cells to a background of coastal seawater. This prototype array correctly identified the presence or absence of the target organisms and their relatives in laboratory mixes, with negligible cross-hybridization to organisms having â¤~75% genomic identity. In addition, the array correctly identified target cells added to a background of environmental DNA, with a limit of detection of ~0.1% of the community, corresponding to ~10^3 cells/ml in these samples. Signal correlated to cell concentration with an R2 of 1.0 across six orders of magnitude. In addition the array could track a related strain (at 86% genomic identity to that targeted) with a linearity of R2=0.9999 and a limit of detection of ~1% of the community. Closely related genotypes were distinguishable by differing hybridization patterns across each probe set. This arrayâs multiple-probe, âgenome-proxyâ approach and consequent ability to track both target genotypes and their close relatives is important for the arrayâs environmental application given the recent discoveries of considerable intra-population diversity within marine microbial communities. Keywords: target addition experiment, proof-of-concept for GPL6012 ***Overall Array design*** The prototype microarray targeted thirteen BAC or fosmid genome fragments (20-160kb) from both bacteria and archaea, recovered from a variety of marine habitats, as well as the cyanobacterium Prochlorococcus MED4. These clones were originally sequenced because of the presence of taxonomic marker or specific functional genes. This array consisted of sets of 70-bp oligonucleotides targeting each genome or genome fragment (Fig. 1), dispersed along the target sequences with no more than one probe per gene, and excluding rRNA genes as targets. The probes were selected solely based on theoretical thermodynamic properties and GC content (~40%); that is, probe selection did not focus on specific genes or regions, but simply produced the âoptimalâ probes for each genome proxy based on the probesâ predicted hybridization properties. rRNA genes were excluded, because this probe design approach, which avoids sequence alignments and considerations of RNA secondary structure, would be unlikely to result in useful rRNA probes. Furthermore, rRNA probes of traditional design could not be included on the array because their appropriate hybridization conditions would be very different from those of this arrayâs probes. ***Microarray probe design*** Microarray 70-mer probes were designed using the program ArrayOligoSelector (Zhu et al., 2003) with the following settings: target %GC = 40%, 1 probe/gene, with the ORFs for each genome fragment as both the input and the database file. The output candidate 70mers were then sorted based on their %GC and those closest to 40% were chosen. In the case of more than the target number of probes having 40%GC, the subset with the lowest free energy of hybridization were selected as probes. Generally, 20 probes were selected per organism. Prochlorococcus MED4 was represented by 60 probes total, 20 each for three different 80kb âgenome-proxyâ regions: 0-80kb, 1.29-1.37Mbp, and 1.58 to 1.66Mbp. Using the same method, a set (n=20) of positive control probes were designed to the genome of the halophillic archaeon Halobacterium salinarum NRC-1. Negative control probes (n=28) were designed to a set of 49 random 1000-base sequences (Stothard, 2000). ***Microarray construction and hybridization*** Oligonucleotides were synthesized (Illumina, San Diego, California), suspended in 3XSSC to a concentration of 40pmol/μl, and spotted on homemade poly-L-lysine-coated glass slides using a QArray 2 microarraying robot (Genetix, Hampshire, England). Six replicates of each probe were spotted. ***Microarray data analysis*** Hybridized arrays were scanned using an Axon Instruments 4000B scanner (Foster City, CA) and the data was normalized and filtered using perl scripts written for the purpose, by the following steps. (1) Signal intensities for each spot were calculated by subtracting the local background (mean F532 â median B532, as calculated by GenePix Pro 5.1 software, Axon Instruments). (2) The median value across replicates was calculated for each probe. (3) For each probe set, the number of probes greater than twice the mean negative control signal was calculated, before further processing. (4) Filter I: Arrays with less than half their positive control probes exceeding twice the mean negative control signal were considered poor quality, low dynamic range, arrays and were excluded from further analysis. (5) Each probe signal was corrected for non-specific binding by subtracting the mean negative control spot signal. (6) The data was then normalized for array-to-array variations in brightness by dividing each probe signal by the mean positive control signal. This positive control signal was the mean signal across the Halobacterium salinarum probes in each hybridization, with identical amounts of H. salinarum DNA having been added to each reaction prior to amplification and labeling. (7) Filter II: In order for a genotype to be considered âpresentâ, at least 45% of its probes had to exceed twice the mean negative control signal. (8) Finally, each genotype signal was calculated as either the MEAN or TUKEY BIWEIGHT across its probe set. ***Experimental Design*** The array was hybridized to laboratory mixtures of cloned environmental genomic DNA targeted by the array, in varying ratios. The use of multiple probes to target many genes from each organism helped to normalize probe-to-probe heterogeneity, by averaging across all probes in a set (as described below). The evenness of probe response across each genotypeâs set was also used to evaluate the relatedness of hybridizing DNA. To more precisely define the arrayâs phylogenetic range and specificity, it was tested against DNA from Prochlorococcus MED4 and related strains, spanning the known range of Prochlorococcus phylogenetic diversity. To test the effects of hybridization stringency on the specificity and signal of the MED4 probes, Prochlorococcus strains were hybridized at a range of conditions. To test whether the specificity results for Prochlorococcus were comparable for other targeted clades, two genome fragments recovered from closely related phylotypes within the SAR86 clade of the gamma-proteobacteria were represented on the array, and were tested for specificity. To understand the equivalence of probe sets targeting different regions of the same organismâs genome, we targeted three 80kb âgenome proxyâ regions of the Prochlorococcus MED4 genome. One of the regions fell in a genomic âislandâ where inter-strain variability is concentrated (âISL5â in Coleman et al., 2006). To test the array in a complex environmental context, we collected coastal seawater (lacking detectable Prochlorococcus cells by flow cytometry) and added Prochlorococcus cells from strains MED4, MIT9515, MIT9312 and MIT9313 over a range of concentrations from ~101 â 106 cells/ml (Fig. 3). The seawater was then filtered and the DNA extracted, amplified, labeled, and hybridized to the array.
2010-05-25 | E-GEOD-9384 | biostudies-arrayexpress