- Research article
- Open Access
A species independent universal bio-detection microarray for pathogen forensics and phylogenetic classification of unknown microorganisms
© Shallom et al; licensee BioMed Central Ltd. 2011
- Received: 10 January 2011
- Accepted: 14 June 2011
- Published: 14 June 2011
The ability to differentiate a bioterrorist attack or an accidental release of a research pathogen from a naturally occurring pandemic or disease event is crucial to the safety and security of this nation by enabling an appropriate and rapid response. It is critical in samples from an infected patient, the environment, or a laboratory to quickly and accurately identify the precise pathogen including natural or engineered variants and to classify new pathogens in relation to those that are known. Current approaches for pathogen detection rely on prior genomic sequence information. Given the enormous spectrum of genetic possibilities, a field deployable, robust technology, such as a universal (any species) microarray has near-term potential to address these needs.
A new and comprehensive sequence-independent array (Universal Bio-Signature Detection Array) was designed with approximately 373,000 probes. The main feature of this array is that the probes are computationally derived and sequence independent. There is one probe for each possible 9-mer sequence, thus 49 (262,144) probes. Each genome hybridized on this array has a unique pattern of signal intensities corresponding to each of these probes. These signal intensities were used to generate an un-biased cluster analysis of signal intensity hybridization patterns that can easily distinguish species into accepted and known phylogenomic relationships. Within limits, the array is highly sensitive and is able to detect synthetically mixed pathogens. Examples of unique hybridization signal intensity patterns are presented for different Brucella species as well as relevant host species and other pathogens. These results demonstrate the utility of the UBDA array as a diagnostic tool in pathogen forensics.
This pathogen detection system is fast, accurate and can be applied to any species. Hybridization patterns are unique to a specific genome and these can be used to decipher the identity of a mixed pathogen sample and can separate hosts and pathogens into their respective phylogenomic relationships. This technology can also differentiate between different species and classify genomes into their known clades. The development of this technology will result in the creation of an integrated biomarker-specific bio-signature, multiple select agent specific detection system.
- Lactobacillus Plantarum
- Rift Valley Fever Virus
- Francisella Tularensis
- Brucella Abortus
- Brucella Species
Rapid, accurate and sensitive detection of bio-threat agents requires a broad-spectrum assay capable of discriminating between closely related microbial or viral pathogens. In cases where a biological agent release has been identified, forensic analysis demands detailed genetic signature data for accurate strain identification and attribution. Identification of genetic signatures for detection coupled with identification of pathogenic phenotypes would provide a robust means of discriminating pathogens from closely related but benign species .
Current forensics methods based on bacteriological, serological, biochemical and genomic strategies have been used to detect pathogens using serological methods , PCR , real time PCR [4, 5] and Multi-loci VNTR (variable-number tandem repeats) or MLVA [6–9]. Although bacteriological culture of Brucella spp. from blood, milk, fetal fluids and tissues, or other host tissues remain the 'gold standard' for diagnosis, bacteriologic culture has reduced sensitivity, is labour intensive, time consuming, typically requiring two weeks, and is a risk for laboratory personnel . Serological assays, such as Rose Bengal, a rapid plate agglutination diagnostic test, is currently used for diagnosing infection with Brucella species in the field , however serological tests frequently have reduced specificity due to cross reactivity with other bacteria. Specific antibodies are required to be present at sufficiently high level and may require several weeks to develop before they are detectable. PCR based methods are used for epidemiological trace back and strain specific identification . Although rapid in nature, specific primers are required for specific genes from these genomes or 16S rRNA genes or VNTR (variable-number tandem repeats) in a given genome. Real time PCR based methods have been used to identify Brucella species using IS711, bcsp31 and per target genes [4, 5]. In addition, assays based on single-nucleotide polymorphisms have been developed for identification of Brucella isolates at the species level. These SNPs have been used to classify isolates into known Brucella species . Recently MLVA or multi-loci VNTR (Variable-number tandem repeats) a genotype-based typing method and has been used as an epidemiological classification and SNP identification method for Brucella isolates in a field population [6–9]. MLVA method is used to understand the genetic diversity in polymorphic loci and to establish taxonomic relationships between different biovars of Brucella. It is used for microbial typing and epidemiologic studies by amplifying loci which are specific to a given genome and sequencing these regions. This is a powerful approach and is being used to create phylogenetic relationships and discovery of single nucleotide polymorphisms in independent loci from different Brucella isolates .
Array based approaches for forensic detection utilizes genome specific ribosomal RNA genes, genome specific PCR markers or oligonucleotide probes. Arrays from rRNA are derived from a combination of rRNA genes from a given set of organisms of high priority. Universal PCR is used to amplify one or more universal genes, including 16S, 18S and 23S as well as screen for pathogen-specific polymorphisms . One of the challenges of this approach is the frequent and unexpected amplification of contaminating template DNA, as observed in control reactions. Another potential problem with targeting 16S rRNA pathogen specific sequences is unexpected polymorphisms. Hence, naturally occurring variants may not be represented on the microarray, and failure to detect the variants would represent false negatives . Another common PCR based approach detects pathogen type by amplification of a specific set of genetic markers that are measured on an array that has several probes for genes from a set of organisms. Such tests have been used for food-borne bacteria such as E. coli O157:H7 , viruses  and mixtures of pathogens . The drawback of using this approach with multiplex PCR primers sets is the generation of spurious products . Array based technologies using 70-mer oligonucleotide probes derived from pathogen specific genes have similar factors that require consideration. For instance, viral detection using a microarray composed of 1,600 unique viral oligonucleotides (70-mers) derived from 140 distinct viral genomes has been previously demonstrated  as a powerful viral detection mechanism, but the drawback of this strategy is that only the group of known pathogen-specific genes will be queried.
Given the enormous spectrum of genetic possibilities, only a highly parallel field deployable technology that is universal in nature has near-term potential to address these needs. The initial vision for a universal DNA microarray was a matrix of oligonucleotide containing features with unique n-mer probes . This matrix could in theory be used to query a biological sample for the presence of any nucleic acid sequence. This technique requires constructing an array that contains 4n features. Larger values of n infuse greater specificity into the arrayed probes, but as n increases the number of required features grows rapidly. This universality is obtained by synthesizing a combinatorial n-mer array containing all 4n possible sequences of length n . The key issue is to find a value of n that is large enough to afford sufficient hybridization specificity, yet small enough to be practically fabricated and analyzed.
We have previously demonstrated the utility of a genome sequence-independent microarray for identifying genetic differences [18, 19]. The initial prototype of universal arrays used oligonucleotide probe lengths of 12 and 13 bases. From 412 possible probes, a subset of 14,283 probes was synthesized using in situ synthesis technology and digital optical chemistry (DOC) [20–22]. Fluorescently labelled genomic DNA was hybridized to produce unique informative patterns (i.e. bio-signatures) on a test set of pathogens and host (Bacillus subtilis, Yersinia pestis, Streptococcus peumoniae, Bacillus anthracis, and Homo sapiens). In addition, we have shown that a custom microsatellite microarray can be used to demonstrate global differences between species by measuring hybridization intensities for every possible repetitive nucleotide motif from 1-mers to 6-mers . Further we have used genome sequence independent microsatellites to identify global differences in the genomes of 93 cancer, cancer-free and high risk patient cell line samples . This paper describes a larger high density oligonucleotide microarray with 370,000 elements, called Universal Bio-signature Detection Array (UBDA), designed by our laboratory and commercially produced by Roche-Nimblegen (Madison, WI) using light-directed photolithography [16, 24]. The platform design which consists mainly of probes, that are tailored to be genome independent, is mathematically derived and therefore unbiased (Additional file 1, Table S1).
This strategy exploits the unique signature of a sample in the form of a pattern generated from hybridization of any unknown genome (DNA or cDNA) to a very high-density species-independent oligonucleotide microarray. Brucella species and several other pathogens were used as examples to demonstrate this forensics technology platform. Hybridization patterns are unique to a genome, and potentially to different isolates or a mixture of organisms. These techniques may be especially useful in evaluating and differentiating species whose genome has not yet been sequenced.
UBDA array sensitivity and specificity of probe hybridization
The specificity of the computationally derived 9-mer probes on the UBDA array was studied using the selectivity of the middle nucleotide in each probe. We hypothesized that DNA strands generally will not hybridize efficiently to any probe for which there are multiple mismatches in proximity to the center-most base. The array design was based upon the prediction that the use of relatively short probes (15-21 mers) would result in the middle approximately 9 bases dominating hybridization kinetics. Probes on the UBDA that contained the StuI site (AGG^CCT) were located and classified by the nucleotide position of the cut point, relative to the center of the probe on the microarray by a custom computer code. DNA was digested to completion with StuI, and compared to matched DNA that was not digested. Each of the 9-mer probes with StuI restriction enzyme sites were binned depending on the nucleotide position of the StuI restriction site relative to the center of the probe. Thus probes with the StuI restriction enzyme site were binned in terms of base location according to the position of the StuI restriction enzyme cut site with respect to the center of the probe. As expected, probes with restriction enzyme site in the center of the probe displayed the highest degree of specificity demonstrated by a reduction in signal. A log2 fold change of -0.23 was obtained when comparing digested DNA to undigested DNA, averaged over microarray probes with the restriction enzyme site at the center of the probe. Microarray probes with the StuI site located at the center demonstrated reduced intensity, confirming specificity of genomic DNA to hybridize to the center of the probe. The trend of the log2 fold change increased as the StuI restriction enzyme site moved away from the center of the probe with the average results increasing towards zero (Additional file 4, Figure S2). Thus, confirming that the center nucleotide is the most selective in the hybridization complexes.
Identification of synthetically mixed pathogen sample
Identification of genetic signatures from closely related Brucellaspecies
The spectrum of organisms chosen for hybridization on this array, were primarily bio-threat zoonotic agents infecting farm animals. Our initial studies were based on the ability of the 9-mer probe signal intensities to distinguish between different Brucella species. Currently, there are nine recognized species of Brucella based on host preferences and phenotypic preferences. Six of those species are Brucella abortus (cattle), Brucella canis (dogs), Brucella melitensis (sheep and goat), Brucella neotomae (desert wood rats), Brucella ovis (sheep) and Brucella suis (pigs) . All of these species are zoonotic except B. neotomae and B. ovis. Raw signal values from the pair data files for the Cy3 channel were background corrected and quantile normalized . Signal intensities related to the 9-mer data set were parsed from the data file using a PERL script. These files were imported into the GeneSpring GX (Agilent, Santa Clara, CA) program. Data from these files was clustered using the hierarchical clustering algorithm to generate a heat map and identify a pattern within the underlying data.
Taxonomic phylogenetic relationships between organisms hybridized on the UBDA array
Samples subjected to DNA amplification are comparable to unamplified samples
This is a new forensics array based technology to identify any species. This unique strategy of using patterns generated from hybridization of any unknown genome (DNA or cDNA) to a very high-density species independent oligonucleotide microarray and comparing those patterns to a library of patterns of known samples can be used to identify unknown organisms. Figure 5 shows the grouping of the different genomes into bacterial, viral and eukaryotic genomes. Further the Brucella species grouping pattern obtained from the phylogenomic analysis using the Pearson's correlation matrix shown in Figure 5 are in agreement with Brucella species showing hierarchical clustering represented as a similarity matrix shown in Figure 3. The UBDA hybridization patterns are unique to a genome, and potentially to different isolates and to a mixture of organisms. In the future, this forensics method will work by comparing signal intensity readout to a library of readouts established by interrogating a wide spectrum of species which will be available at our website http://discovery.vbi.vt.edu/ubda/. The phylogenetic tree illustrates the ability of 9-mer probes to differentiate among Brucella species. Pair-wise comparisons between different genomes can be used as a measure to classify bacterial, viral or mammalian genomes into their respective clades. We have begun to amass a library of 'signatures' to facilitate accurate identification and classification of "unknown" samples. We are currently expanding the repository of available bio-signatures to several hundred genomes including field isolates from bacteria, viruses, host genomes and vectors infected with pathogens. Some of the genomes in this repository are classified in the select agent category. UBDA forensics application has the potential to be compatible with micro-machine based front end sample processing and preparation platforms, thus enabling the production of a highly automated, fast and accurate field-deployable detection system.
Other diagnostic techniques such as PCR or RT-PCR require several primers to be designed which are specific for each genome- bacterial, viral or host. There may be spurious products for primers binding at low specificity. The processing costs should also be taken into consideration for these methodologies. The current cost for the UBDA array is approximately $350 per sample which includes reagents and processing costs. The current turnaround time for this forensics technology is less than 24 hours. This is a single experimental procedure compared to other technologies which involve a series of methods such as serological, biochemical and genomic based. Genome specific arrays are in the similar price range as the UBDA array; however researchers can only assay a single genome or a small subset of them. Currently the UBDA platform requires a turnaround time approximately one day from hybridization on the array to data analysis. A diagnostic laboratory in the field requires proximately two weeks before the identity of a given infectious agent can be determined. These methods usually require several standard serological and biochemical tests that are usually selected and based on the clinical symptoms observed in the field. Serology test results are usually available after 48 hours. Although each of these tests is cost effective in nature, they must be fine tuned to be pathogen specific.
The UBDA approach can be applied to any genome, even in the presence of background contamination (usually host DNA) for which, the unique pattern will be known. The patterns generated from an unknown sample (secretion, tissue culture, environmental sample, etc) with minimal specimen processing can be identified or at least the most similar related species will be predicted by comparison to a library or a repository of patterns. These techniques may be especially useful in evaluating and differentiating species whose genome has not yet been sequenced. Along with a repository of unique hybridization signatures from the genomes of pathogens and their hosts, this array has the ability to rapidly and adequately identify biological threat agents and newly emerging infectious pathogens that are high risk priorities in bio-defense. Application of this technology has the potential to extend to other areas such as food and environmental microbial monitoring and basic research including, (a) speciation and evolution, (b) human/animal disease biomarker discovery, (c) measurement of the genomic response to a chemical, radiation or other exposure, but most important, (d) pathogen forensics and characterization of natural or engineered variants that may confound other species-specific approaches.
Genetic signature discovery and identification of pathogenic phenotypes will provide a robust means of discriminating pathogens that are closely related. This array has high sensitivity as demonstrated by the detection of low amounts of spike-in oligonucleotides. Hybridization patterns are unique to a specific genome and these can be used to de-convolute and thus identity the constituents of a mixed pathogen sample. In addition it can distinguish hosts and pathogens by their divergent phylogenomic relationships as captured in their respective 9-mer hybridization signatures. This platform has potential for commercial and government agency applications as a cost effective reliable platform for accurately screening large numbers of samples for bio-threat agents in forensic analysis, screening for pathogens that routinely infect animals and humans, and as a molecular diagnostic of micro-organisms in a clinical environment. This platform is highly attractive, because it has multiplex capacity where knowledge can be drawn from the array hybridization patterns without prior explicit information of the genomes in the samples. These hybridization patterns are being translated into a knowledge base repository of bio-signatures so that future users of this technology can compare and draw inferences related to the sample under study. The data from these experiments and the array design are located on our web site at http://discovery.vbi.vt.edu/ubda/.
Array design details
A custom microarray was designed by this laboratory and manufactured by Roche-Nimblegen (Madison, WI) as a custom 385 K (385,000 probe platform) chip to include the following sets of probes; 9-mer, pathogen specific probes; rRNA gene specific, microsatellite and control 70-mer oligonucleotide probes. There were 262,144 9-mer probes, and 20,000 of them were replicated 3 times in total (Additional file 1, Table S1). The 9-mer probes were comprised of a core 9-mer nucleotide and flanked on both sides by three nucleotides, selected to maximize sequence coverage of these basic 15-mers. Probes with low GC content were padded with additional bases at their termini to equalize melting temperatures, with most probes ranging from 15-21 nucleotides in total length. For the 9-mer design, the length of the probes was adjusted to match a melting temperature of 54°C. The array design was based upon the prediction that the use of relatively short probes (15-21 mers) would result in the middle 9 bases dominating hybridization kinetics.
rRNA probes were included in the design to serve as positive controls and confirmation of the 9-mer probes power for differentiating genomes. The rRNA probes were selected from the green gene data (http://greengenes.lbl.gov/cgi-bin/nph-show_probes_2_otu_alignments.cgi), utilizing the complete list of 8,935 OTUs (Operational Taxonomic Unit). One probe was selected for each OTU and probe length was adjusted to a Tm equal to 54°C, as was done for 9-mer design. A mis-match probe (1 mis-match, MM) for each OTU probe was included in the design. Perfect match (PM) 8,935 probes and 8,935 one mis-match MM probes were included in the microarray design. All probes are replicated 3 times on the array.
Genome specific probes for Brucella spp., Avian Influenza Virus (AIV), Foot and Mouth Disease Virus (FMDV), and Rift-Valley Fever Virus (RVFV) were designed and included on the microarray as an independent test when the array is used to analyze these species. Sequence alignments were performed to determine the similar and unique regions of the pathogens, with probes selected from the unique regions of each pathogen species or sub-type, and excluding sequences similar to host genomes. In total, 1,062 unique probes were selected and are replicated 3 times.
Probes dedicated to surveying microsatellite content were designed for every 1- to 6-mer repetitive sequence. For each 1- to 5-mer repetitive sequence, single mis-match (1 MM) probes were also designed. A total of 3,557 unique microsatellite probes were generated and replicated at total of 3 times. Microsatellite probes were included on this array to anchor the results to previous experiments and to aid in the de-convolution of the contribution of host genomic DNA. For higher life forms typically have many microsatellite loci, whereas bacteria and viruses have none or very few in their genome.
Gene-specific probes were designed to target important metabolic pathways, such as alcohol dehydrogenase, glucose-6-phosphate isomerase and SHV-like β-lactamase, by using the highly conserved sequences. In total, 432 probes were designed and replicated a total of 3 times.
For labelling controls, a set of six synthetic 70-mer oligonucleotides were designed to be spiked into each labelling reaction and hybridized to a constellation of 361 dedicated probes on the array comprising of perfect match probes (34 probes), 1 mis-match (100 probes), 2 mis-match (137 probes) and 3 mis-match probes (90 probes), ranging from 15-19 nucleotides. The set of 361 probes are replicated 5 times total (Additional file 2, Table S2). Figure 1 shows a comparison of signal intensity values of perfect match control probes on the array generated from human genomic DNA without spike of oligonucleotides to samples with a spiked-in. Regression analysis of signal intensity values from the mis-matched probes on the data set is in Figures S1A-S1D (Additional file 3). The array design files for each feature category on the UBDA array are in Additional file 6 (9-mer probes) and Additional file 7 (all other probes) and also available at http://discovery.vbi.vt.edu/ubda/.
Human genomic DNA was extracted from blood samples collected from a volunteer by the McDermott Center for Human Growth and Development Genetics Clinical Laboratory in accordance with Institutional Review Board at UT Southwestern Medical Center (Dallas, TX). Genomic DNA from Bos taurus, Gallus gallus, Meleagris gallopavo, Ovis aries, Capra hircus, and Equus caballus was obtained from Zyagen (San Diego, CA). Brucella species, Cryptosporidium parvum, Lactobacillus plantarum, Streptococcus mitis, Escherichia coli and Influenza virus genomic DNA was obtained from BEI resources and ATCC (Manasses, VA). The spectrum of organisms chosen for hybridization on the UBDA array was primarily bio-threat zoonotic agents, agents infecting farm animals.
DNA concentration (260 nm) and purity (260/280 and 260/230 nm) was assessed by the spectrophotometer and quality by agarose gel electrophoresis. Samples with 260/230 nm ratios greater than 1.8 were used following established protocols for array comparative genomic hybridization (CGH). Hybridization conditions were optimized to ensure specificity and sensitivity. All DNA test samples (1 μg) were labelled with Cy3 and co-hybridized with the same Cy5-labeled human reference (Promega, Inc, Madison, WI), according to Roche Nimblegen standard microarray labelling procedures. For each microarray, human genomic DNA (Promega, Madison, WI) was labelled with Cy-5 and used as a reference channel in each experiment. DNA labelling, hybridization and data acquisition were performed by Mogene (St. Louis, MO). We tested hybridization temperatures ranging from 30°C to 50°C. For microarray hybridization, a custom buffer (0.5% Triton X-100, 1 M NaCl, and 100 mM Tris-HCl pH 7.5, filtered with a 0.2 micron nitrocellulose filter, prepared fresh) was used at 38°C, and microarrays were washed following Roche Nimblegen's CGH standard techniques (available at http://www.nimblegen.com). Hybridization conditions were standardized for the UBDA array to minimize any errors that could lead to bias resulting after processing the slides and image scanning on an array scanner. Signals from probes complementary to labelling controls indicate that the post-DNA preparation process, from labelling to hybridization, washing and scanning, were successful. Hybridization, scanning, and data extraction were performed following Roche NimbleGen standard protocol for CGH arrays, and the resulting raw data were provided via secure web link.
Array data processing and organism classification
A Robust Multi-chip Average (RMA) normalization procedure was performed across all arrays. The procedure included background subtraction and quantile normalization using Nimblescan Software (Roche NimbleGen). After normalization, all 262,144 9-mer probes were extracted from the 370 K array using PERL scripts and averaged across the replicate probes. Subsequent statistical analysis was performed using GeneSpringGX 11.0 (Agilent Technologies, Santa Clara, CA). All signal intensity values were log2 transformed for further analysis. Data were also filtered by intensity values (lower cut off percentile of 20% for raw signals), and subsequent pair-wise comparisons were performed on the sample data set. Clustering is one of the data mining processes for discovery and identifying patterns in the underlying data. Clustering algorithms partition data into subsets based on similarity and dissimilarity. Clustering methods follow three steps: pattern recognition, use of a clustering algorithm and similarity measure matrix . For pattern recognition, pair-wise comparisons are used between samples to select the features on which the clustering is to be performed. Our experimental platform is comparative genome hybridization for which hierarchical clustering is used to determine phylogenomic relationships between organisms. Hierarchical clustering  transforms a distance matrix of pair-wise similarity measurements between all items into a hierarchy of nested groupings. The hierarchy is represented with a binary tree-like dendogram. Hierarchical clustering was performed on the resulting data sets, using the Euclidian matrix and centroid linkage to classify various organisms. Data sets were analyzed for Brucella species. A cut-off of 5-fold change in hybridization intensity for a given probe was used to reduce the data set to only those meaningful probes that showed a difference between at least one of the pair-wise comparisons.
Phylogenetic taxonomic tree based on array intensity
Data obtained from the Universal Bio-Detection Array (normalized signal intensity values that were log2 transformed) and computational analysis for all 262,144 9-mer probes were treated identically for the purpose of tree building. All 262,144 data points for each of the 20 samples were first RMA normalized. For each sample, a Pearson's correlation matrix was created which included self similarity and similarity to the remaining 19 samples from all the 262,144 data points of each sample. The resulting distance matrix was used to produce a phylogenetic tree, using the neighbour-joining method within the PHYLIP software suite and TreeView.
Whole genome amplification
Francisella tularensis LVS strain genomic DNA, starting material, 10 nanogram was amplified using whole genome amplification method as defined (GenomiPhi V2, GE Healthcare). We obtained 2-3 μg of whole genome amplified DNA from 10 ng of starting genomic DNA.
This work was funded by Department of Homeland Security through the FAZD Center (National Center of Excellence for Foreign Animal and Zoonotic Disease Defense) at Texas A & M University and Virginia Bioinformatics Institute director's funds. SJS received support from a trans-disciplinary fellowship from Virginia Tech and Virginia Bioinformatics Institute. We would like to extend a special thanks to Angela George and Dale Preston of the Texas Animal Health Commission, Austin, Texas for assistance with sample preparation. We thank Dr. Abey Bandara and Dr. Tom Inzana at Virginia Tech for providing the Francisella tularensis LVS strain genomic DNA. We would like to extend a special thanks to Greg Thorne and Shaukat Rangwala with MoGene their valuable technical assistance. We appreciate the assistance of Linda Gunn, Renee Nester, Traci Roberts and Laurie Spotswood for administrative assistance. We also appreciate Zyagen and BEI resources for providing genomic DNA.
- Pannucci J, Cai H, Pardington PE, Williams E, Okinaka RT, Kuske CR, Cary RB: Virulence signatures: microarray-based approaches to discovery and analysis. Biosens Bioelectron. 2004, 20 (4): 706-718. 10.1016/j.bios.2004.04.005.PubMedView ArticleGoogle Scholar
- Ruiz-Mesa JD, Sanchez-Gonzalez J, Reguera JM, Martin L, Lopez-Palmero S, Colmenero JD: Rose Bengal test: diagnostic yield and use for the rapid diagnosis of human brucellosis in emergency departments in endemic areas. Clin Microbiol Infect. 2005, 11 (3): 221-225. 10.1111/j.1469-0691.2004.01063.x.PubMedView ArticleGoogle Scholar
- Bricker BJ: PCR as a diagnostic tool for brucellosis. Vet Microbiol. 2002, 90 (1-4): 435-446. 10.1016/S0378-1135(02)00228-6.PubMedView ArticleGoogle Scholar
- Bounaadja L, Albert D, Chenais B, Henault S, Zygmunt MS, Poliak S, Garin-Bastuji B: Real-time PCR for identification of Brucella spp.: a comparative study of IS711, bcsp31 and per target genes. Vet Microbiol. 2009, 137 (1-2): 156-164. 10.1016/j.vetmic.2008.12.023.PubMedView ArticleGoogle Scholar
- Hinic V, Brodard I, Thomann A, Holub M, Miserez R, Abril C: IS711-based real-time PCR assay as a tool for detection of Brucella spp. in wild boars and comparison with bacterial isolation and serology. BMC Vet Res. 2009, 5: 22-10.1186/1746-6148-5-22.PubMedPubMed CentralView ArticleGoogle Scholar
- Her M, Kang SI, Kim JW, Kim JY, Hwang IY, Jung SC, Park SH, Park MY, Yoo H: A genetic comparison of Brucella abortus isolates from animals and humans by using an MLVA assay. J Microbiol Biotechnol. 2010, 20 (12): 1750-1755.PubMedGoogle Scholar
- Whatmore AM, Perrett LL, MacMillan AP: Characterisation of the genetic diversity of Brucella by multilocus sequencing. BMC Microbiol. 2007, 7: 34-10.1186/1471-2180-7-34.PubMedPubMed CentralView ArticleGoogle Scholar
- Abril C, Thomann A, Brodard I, Wu N, Ryser-Degiorgis MP, Frey J, Overesch G: A novel isolation method of Brucella species and molecular tracking of Brucella suis biovar 2 in domestic and wild animals. Vet Microbiol. 2011Google Scholar
- De Santis R, Ciammaruconi A, Faggioni G, D'Amelio R, Marianelli C, Lista F: Lab on a chip genotyping for Brucella spp. based on 15-loci multi locus VNTR analysis. BMC Microbiol. 2009, 9: 66-10.1186/1471-2180-9-66.PubMedPubMed CentralView ArticleGoogle Scholar
- Scott JC, Koylass MS, Stubberfield MR, Whatmore AM: Multiplex assay based on single-nucleotide polymorphisms for rapid identification of Brucella isolates at the species level. Appl Environ Microbiol. 2007, 73 (22): 7331-7337. 10.1128/AEM.00976-07.PubMedPubMed CentralView ArticleGoogle Scholar
- Call DR: Challenges and opportunities for pathogen detection using DNA microarrays. Crit Rev Microbiol. 2005, 31 (2): 91-99. 10.1080/10408410590921736.PubMedView ArticleGoogle Scholar
- Call DR, Brockman FJ, Chandler DP: Detecting and genotyping Escherichia coli O157:H7 using multiplexed PCR and nucleic acid microarrays. Int J Food Microbiol. 2001, 67 (1-2): 71-80. 10.1016/S0168-1605(01)00437-8.PubMedView ArticleGoogle Scholar
- Chizhikov V, Wagner M, Ivshina A, Hoshino Y, Kapikian AZ, Chumakov K: Detection and genotyping of human group A rotaviruses by oligonucleotide microarray hybridization. J Clin Microbiol. 2002, 40 (7): 2398-2407. 10.1128/JCM.40.7.2398-2407.2002.PubMedPubMed CentralView ArticleGoogle Scholar
- Wilson WJ, Strout CL, DeSantis TZ, Stilwell JL, Carrano AV, Andersen GL: Sequence-specific identification of 18 pathogenic microorganisms using microarray technology. Mol Cell Probes. 2002, 16 (2): 119-127. 10.1006/mcpr.2001.0397.PubMedView ArticleGoogle Scholar
- Wang D, Coscoy L, Zylberberg M, Avila PC, Boushey HA, Ganem D, DeRisi JL: Microarray-based detection and genotyping of viral pathogens. Proc Natl Acad Sci USA. 2002, 99 (24): 15687-15692. 10.1073/pnas.242579699.PubMedPubMed CentralView ArticleGoogle Scholar
- Pease AC, Solas D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SP: Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci USA. 1994, 91 (11): 5022-5026. 10.1073/pnas.91.11.5022.PubMedPubMed CentralView ArticleGoogle Scholar
- Royce TE, Rozowsky JS, Gerstein MB: Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification. Nucleic Acids Res. 2007, 35 (15): e99-10.1093/nar/gkm549.PubMedPubMed CentralView ArticleGoogle Scholar
- Belosludtsev YY, Bowerman D, Weil R, Marthandan N, Balog R, Luebke K, Lawson J, Johnston SA, Lyons CR, Obrien K, Garner HR, Powdrill TF: Organism identification using a genome sequence-independent universal microarray probe set. Biotechniques. 2004, 37 (4): 654-658. 660PubMedGoogle Scholar
- Galindo CL, McIver LJ, McCormick JF, Skinner MA, Xie Y, Gelhausen RA, Ng K, Kumar NM, Garner HR: Global microsatellite content distinguishes humans, primates, animals, and plants. Mol Biol Evol. 2009, 26 (12): 2809-2819. 10.1093/molbev/msp192.PubMedPubMed CentralView ArticleGoogle Scholar
- Luebke KJ, Balog RP, Mittelman D, Garner HR: Digital optical chemistry: A novel system for the rapid fabrication of custom oligonucleotide arrays. Microfabricated Sensors. 2002, 815: 87-106.View ArticleGoogle Scholar
- Luebke KJ, Balog RP, Garner HR: Prioritized selection of oligodeoxyribonucleotide probes for efficient hybridization to RNA transcripts. Nucleic Acids Research. 2003, 31 (2): 750-758. 10.1093/nar/gkg133.PubMedPubMed CentralView ArticleGoogle Scholar
- Balog R, Hedhili MN, Bournel F, Penno M, Tronc M, Azria R, Illenberger E: Synthesis of Cl-2 induced by low energy (0-18 eV) electron impact to condensed 1,2-C2F4Cl2 molecules. Physical Chemistry Chemical Physics. 2002, 4 (14): 3350-3355.View ArticleGoogle Scholar
- Galindo CL: Sporadic breast cancer patient's germline DNA exhibit an AT-rich microsatellite signature. Genes, Chromosomes and Cancer. 2011, 50 (4): 275-283.PubMedPubMed CentralGoogle Scholar
- McGall GH, Fidanza JA: Photolithographic synthesis of high-density oligonucleotide arrays. Methods Mol Biol. 2001, 170: 71-101.PubMedGoogle Scholar
- Kane MD, Jatkoe TA, Stumpf CR, Lu J, Thomas JD, Madore SJ: Assessment of the sensitivity and specificity of oligonucleotide (50mer) microarrays. Nucleic Acids Res. 2000, 28 (22): 4552-4557. 10.1093/nar/28.22.4552.PubMedPubMed CentralView ArticleGoogle Scholar
- Denapaite D, Bruckner R, Nuhn M, Reichmann P, Henrich B, Maurer P, Schahle Y, Selbmann P, Zimmermann W, Wambutt R, Hakenbeck R: The genome of Streptococcus mitis B6--what is a commensal?. PLoS One. 2010, 5 (2): e9426-10.1371/journal.pone.0009426.PubMedPubMed CentralView ArticleGoogle Scholar
- Alting-Mees MA, Short JM: pBluescript II: gene mapping vectors. Nucleic Acids Res. 1989, 17 (22): 9494-10.1093/nar/17.22.9494.PubMedPubMed CentralView ArticleGoogle Scholar
- Morgan WJ: Brucella classification and regional distribution. Dev Biol Stand. 1984, 56: 43-53.PubMedGoogle Scholar
- Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4 (2): 249-264. 10.1093/biostatistics/4.2.249.PubMedView ArticleGoogle Scholar
- Paulsen IT, Seshadri R, Nelson KE, Eisen JA, Heidelberg JF, Read TD, Dodson RJ, Umayam L, Brinkac LM, Beanan MJ, Daugherty SC, Deboy RT, Durkin AS, Kolonay JF, Madupu R, Nelson WC, Ayodeji B, Kraul M, Shetty J, Malek J, VanAken SE, Riedmuller S, Tettelin H, Gill SR, White O, Salzberg SL, Hoover DL, Lindler LE, Halling SM, Boyle SM, et al: The Brucella suis genome reveals fundamental similarities between animal and plant pathogens and symbionts. Proc Natl Acad Sci USA. 2002, 99 (20): 13148-13153. 10.1073/pnas.192319099.PubMedPubMed CentralView ArticleGoogle Scholar
- DelVecchio VG, Kapatral V, Redkar RJ, Patra G, Mujer C, Los T, Ivanova N, Anderson I, Bhattacharyya A, Lykidis A, Reznik G, Jablonski L, Larsen N, D'Souza M, Bernal A, Mazur M, Goltsman E, Selkov E, Elzer PH, Hagius S, O'Callaghan D, Letesson JJ, Haselkorn R, Kyrpides N, Overbeek R: The genome sequence of the facultative intracellular pathogen Brucella melitensis. Proc Natl Acad Sci USA. 2002, 99 (1): 443-448. 10.1073/pnas.221575398.PubMedPubMed CentralView ArticleGoogle Scholar
- Page RD: TreeView: an application to display phylogenetic trees on personal computers. Comput Appl Biosci. 1996, 12 (4): 357-358.PubMedGoogle Scholar
- Frades I, Matthiesen R: Overview on techniques in cluster analysis. Methods Mol Biol. 2010, 593: 81-107. 10.1007/978-1-60327-194-3_5.PubMedView ArticleGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95 (25): 14863-14868. 10.1073/pnas.95.25.14863.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.