Comparing Bacterial DNA Microarray Fingerprints

Abstract

Epidemiologic and forensic investigations often require assays to detect subtle genetic differences between closely related microorganisms. Typically, gel electrophoresis is used to compare randomly amplified DNA fragments between microbial samples, where the patterns of DNA fragment sizes are viewed as genotype 'fingerprints'. The limited genomic sample captured on a gel, however, is not always sufficient to discriminate closely related strains. This paper examines the application of microarray technology to DNA fingerprinting as a high-resolution alternative to gel-based methods. The so-called universal microarray, which uses short oligonucleotide probes that do not target specific genes or species, is intended to be applicable to all microorganisms because it does not require prior knowledge of genomic sequence. In principle, closely related strains can be distinguished if enough independent oligonucleotide probes are used on the microarray, i.e., if the genome is sufficiently sampled. In practice, we confront noisy data, imperfectly matched hybridizations, and a high-dimensional inference problem. We describe the statistical problems of microarray fingerprinting, outline similarities with and differences from more conventional microarray applications, and illustrate a statistical measurement error model to fingerprint 10 closely related strains from three Bacillus species, and 3 strains from non-Bacillus species.

Suspension Array Analysis of 16S rRNA from Fe- and SO 4 2 -Reducing Bacteria in Uranium-Contaminated Sediments Undergoing Bioremediation

“…On the other hand, we are not aware of a formal statistic to test the hypothesis of profile equivalence, where a robust test of profile equivalence requires true replication. Furthermore, profile equivalence may be defined by a digital pattern of on/off probe responses or may take into account individual probe signal intensities in the data vector, as we have described in detail previously (38). As discussed below, the fundamental statistical challenge that we faced in this study was the fact that microbial spatial heterogeneity within the sediment samples precluded replication in the conventional biological or statistical sense, and definition of the appropriate scale for sampling and averaging in the subsurface continues to be a subject of intense dialogue.…”

Multiplex Approach for Screening Genetic Markers of Microbial Indicators

“…Moreover, the number of sample‐ or host‐specific signal was function of the signal to noise (S/N) ratio, with more number of unique signals for lower S/N ratios (Figure 2). Such behavior for 16S rRNA arrays has been previously reported and can be handled using statistical data analysis tools ( Willse et al, 2005 ). This analysis highlights the existence of a large number of host‐specific signals that may be suitable for further analysis.…”

Diagnostic Oligonucleotide Microarray Fingerprinting of Bacillus Isolates

“…By this measure, Yersinia enterocolitica is separated from the two B. anthracis strains by 110 and 108 differences, respectively, while the two B. anthracis strains are differentiated by 10 discriminating probes (with sequences [5Ј to 3Ј] of CAGCTAATG, TGCAGATGC, CGTCAACTT, CAACACTCG, CCAGCGATA, TGCAGAAGC, TGCCAT GAG, TCACGGTAG, TTTACTGAC, and GTTGAGTTG). A more rigorous statistical test that controls the false-discovery rate for the large number of comparisons made also found differences between all pairs of isolates based on their normalized probe intensity values (24). What is evident from Fig.…”

“…REP-PCR fragments from three different organisms were hybridized to three arrays on the same slide according to a balanced incomplete block design, where each slide is treated as a block (24) so that paired strains were directly compared on the same slide exactly twice. The fourth array on each slide was hybridized with the Mix-10 standard targets, all diluted in an equimolar ratio to a final concentration of 1.53 nM (each) in the hybridization solution.…”

“…To ensure accurate spot finding against a potentially distorted printing grid, an analyst manually identified several spots and reran the automated algorithm until all spots in the alignment were accurately identified. For each spot, the average pixel intensity value and the average (local) background intensity value were exported to a spreadsheet for statistical analysis as described in detail elsewhere (24). In brief, we calculated the log(mean spot pixel intensity) minus the log(mean background pixel intensity) for each probe over all replicate arrays (n ϭ 12).…”