Peter A. Noble

Analysis of genomic sequences by Chaos Game Representation

MOTIVATION Chaos Game Representation (CGR) is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to find the coordinates for their position in a continuous space. This distribution of positions has two properties: it is unique, and the source sequence can be recovered from the coordinates such that distance between positions measures similarity between the corresponding sequences. The possibility of using the latter property to identify succession schemes have been entirely overlooked in previous studies which raises the possibility that CGR may be upgraded from a mere representation technique to a sequence modeling tool. RESULTS The distribution of positions in the CGR plane were shown to be a generalization of Markov chain probability tables that accommodates non-integer orders. Therefore, Markov models are particular cases of CGR models rather than the reverse, as currently accepted. In addition, the CGR generalization has both practical (computational efficiency) and fundamental (scale independence) advantages. These results are illustrated by using Escherichia coli K-12 as a test data-set, in particular, the genes thrA, thrB and thrC of the threonine operon.

Optimization of Single-Base-Pair Mismatch Discrimination in Oligonucleotide Microarrays

ABSTRACT The discrimination between perfect-match and single-base-pair-mismatched nucleic acid duplexes was investigated by using oligonucleotide DNA microarrays and nonequilibrium dissociation rates (melting profiles). DNA and RNA versions of two synthetic targets corresponding to the 16S rRNA sequences of Staphylococcus epidermidis (38 nucleotides) and Nitrosomonas eutropha (39 nucleotides) were hybridized to perfect-match probes (18-mer and 19-mer) and to a set of probes having all possible single-base-pair mismatches. The melting profiles of all probe-target duplexes were determined in parallel by using an imposed temperature step gradient. We derived an optimum wash temperature for each probe and target by using a simple formula to calculate a discrimination index for each temperature of the step gradient. This optimum corresponded to the output of an independent analysis using a customized neural network program. These results together provide an experimental and analytical framework for optimizing mismatch discrimination among all probes on a DNA microarray.

Direct profiling of environmental microbial populations by thermal dissociation analysis of native rRNAs hybridized to oligonucleotide microarrays

ABSTRACT Oligonucleotide microarrays were used to profile directly extracted rRNA from environmental microbial populations without PCR amplification. In our initial inspection of two distinct estuarine study sites, the hybridization patterns were reproducible and varied between estuarine sediments of differing salinities. The determination of a thermal dissociation curve (i.e., melting profile) for each probe-target duplex provided information on hybridization specificity, which is essential for confirming adequate discrimination between target and nontarget sequences.

Single-Base-Pair Discrimination of Terminal Mismatches by Using Oligonucleotide Microarrays and Neural Network Analyses

ABSTRACT The effects of single-base-pair near-terminal and terminal mismatches on the dissociation temperature (Td) and signal intensity of short DNA duplexes were determined by using oligonucleotide microarrays and neural network (NN) analyses. Two perfect-match probes and 29 probes having a single-base-pair mismatch at positions 1 to 5 from the 5′ terminus of the probe were designed to target one of two short sequences representing 16S rRNA. Nonequilibrium dissociation rates (i.e., melting profiles) of all probe-target duplexes were determined simultaneously. Analysis of variance revealed that position of the mismatch, type of mismatch, and formamide concentration significantly affected the Td and signal intensity. Increasing the concentration of formamide in the washing buffer decreased the Td and signal intensity, and it decreased the variability of the signal. Although Tds of probe-target duplexes with mismatches in the first or second position were not significantly different from one another, duplexes with mismatches in the third to fifth positions had significantly lower Tds than those with mismatches in the first or second position. The trained NNs predicted the Td with high accuracies (R2 = 0.93). However, the NNs predicted the signal intensity only moderately accurately (R2 = 0.67), presumably due to increased noise in the signal intensity at low formamide concentrations. Sensitivity analysis revealed that the concentration of formamide explained most (75%) of the variability in Tds, followed by position of the mismatch (19%) and type of mismatch (6%). The results suggest that position of the mismatch at or near the 5′ terminus plays a greater role in determining the Td and signal intensity of duplexes than the type of mismatch.

Tests of rRNA hybridization to microarrays suggest that hybridization characteristics of oligonucleotide probes for species discrimination cannot be predicted

Hybridization of rRNAs to microarrays is a promising approach for prokaryotic and eukaryotic species identification. Typically, the amount of bound target is measured by fluorescent intensity and it is assumed that the signal intensity is directly related to the target concentration. Using thirteen different eukaryotic LSU rRNA target sequences and 7693 short perfect match oligonucleotide probes, we have assessed current approaches for predicting signal intensities by comparing Gibbs free energy (ΔG°) calculations to experimental results. Our evaluation revealed a poor statistical relationship between predicted and actual intensities. Although signal intensities for a given target varied up to 70-fold, none of the predictors were able to fully explain this variation. Also, no combination of different free energy terms, as assessed by principal component and neural network analyses, provided a reliable predictor of hybridization efficiency. We also examined the effects of single-base pair mismatch (MM) (all possible types and positions) on signal intensities of duplexes. We found that the MM effects differ from those that were predicted from solution-based hybridizations. These results recommend against the application of probe design software tools that use thermodynamic parameters to assess probe quality for species identification. Our results imply that the thermodynamic properties of oligonucleotide hybridization are by far not yet understood.

Integrating LIDAR elevation data, multi-spectral imagery and neural network modelling for marsh characterization

Vertical elevation relative to mean sea level is a critical variable for the productivity and stability of salt marshes. This research classified a high spatial resolution Airborne Data Acquisition and Registration (ADAR) digital camera image of a salt marsh landscape at North Inlet, South Carolina, USA using an artificial neural network. The remote sensing‐derived thematic map was cross‐referenced with Light Detection and Ranging (LIDAR) elevation data to compute the frequency distribution of marsh elevation relative to tidal elevations. At North Inlet, the median elevation of the salt marsh dominated by Spartina alterniflora was 0.349 m relative to the North American Vertical Datum 1988 (NAVD88), while the mean high water level was 0.618 m (2001 to May, 2003) with a mean tidal range of 1.39 m. The distribution of elevations of Spartina habitat within its vertical range was normal, and 80% of the salt marsh was situated between a narrow range of 0.22 m and 0.481 m. Areas classified as Juncus marsh, dominated by Juncus roemerianus, had a broader, skewed distribution, with 80% of the distribution between 0.296 m and 0.981 m and a median elevation of 0.519 m. The Juncus marsh occurs within the intertidal region of brackish marshes and along the upper fringe of salt marshes. The relative elevation of the Spartina marsh at North Inlet is consistent with recent work that predicts a decrease in equilibrium elevation with an increasing rate of sea‐level rise and suggests that the marshes here have not kept up with an increase in the rate of sea‐level rise during the last two decades.

Adapting the CHEMTAX Method for Assessing Phytoplankton Taxonomic Composition in Southeastern U.S. Estuaries

CHEMTAX is a matrix factorization program used to derive taxonomic structure of phytoplankton from photosynthetic pigment vitios. The program was originally developed from and applied to the analysis of oceanic phytoplankton assemblages. We found that application of the original CHEMTAX reference matrix to southeastern United States estuarine systems produced inaccurate results, as verified by microscopy. Modification of the matrix, based primarily on the pigment ratios of 33 estuarine isolates, improved the predictive capabilities of CHEMTAX for our samples. Limitations of the method included an overstimation of diatom biomass (due to the inability to differentiate diatoms from taxa with chloroplasts derived from diatom endosymbionts, notably some dinoflagellates) and a tendency to exclude some raphidophyte species. In complement with microscopic verification, the method was shown to improve assessment of phytoplankton taxonomic composition.

Application of Neural Computing Methods for Interpreting Phospholipid Fatty Acid Profiles of Natural Microbial Communities

ABSTRACT The microbial community compositions of surface and subsurface marine sediments and sediments lining burrows of marine polychaetes and hemichordates from the North Inlet estuary (near Georgetown, S.C.) were analyzed by comparing ester-linked phospholipid fatty acid (PLFA) profiles with a back-propagating neural network (NN). The NNs were trained to relate PLFA inputs to sediment type outputs (e.g., surface, subsurface, and burrow lining) and worm species (e.g., Notomastus lobatus, Balanoglossus aurantiacus, andBranchyoasychus americana). Sensitivity analysis was used to determine which of the 60 PLFAs significantly contributed to training the NN. The NN architecture was optimized by changing the number of hidden neurons and calculating the cross-validation error between predicted and actual outputs of training and test data. The optimal NN architecture was found to be four hidden neurons with 60-input neurons representing the 60 PLFAs, and four output neurons coding for both sediment types and worm species. Comparison of cross-validation results using NNs and linear discriminant analysis (LDA) revealed that NNs had significantly fewer incorrect classifications (2.7%) than LDA (8.4%). For the NN cross-validation, both sediment type and worm species had 3 incorrect classifications out of 112. For the LDA cross-validation, sediment type and worm species had 7 and 12 incorrect classifications out of 112, respectively. Sensitivity analysis of the trained NNs revealed that 17 fatty acids explained 50% of variability in the data set. These PLFAs were highly different among sediments and burrow types, indicating significant differences in the microbiota.

Physico-chemical foundations underpinning microarray and next-generation sequencing experiments

Hybridization of nucleic acids on solid surfaces is a key process involved in high-throughput technologies such as microarrays and, in some cases, next-generation sequencing (NGS). A physical understanding of the hybridization process helps to determine the accuracy of these technologies. The goal of a widespread research program is to develop reliable transformations between the raw signals reported by the technologies and individual molecular concentrations from an ensemble of nucleic acids. This research has inputs from many areas, from bioinformatics and biostatistics, to theoretical and experimental biochemistry and biophysics, to computer simulations. A group of leading researchers met in Ploen Germany in 2011 to discuss present knowledge and limitations of our physico-chemical understanding of high-throughput nucleic acid technologies. This meeting inspired us to write this summary, which provides an overview of the state-of-the-art approaches based on physico-chemical foundation to modeling of the nucleic acids hybridization process on solid surfaces. In addition, practical application of current knowledge is emphasized.

Distinctive thanatomicrobiome signatures found in the blood and internal organs of humans

According to the Human Microbiome Project, 90% of the cells in a healthy adult body are microorganisms. What happens to these cells after human host death, defined here as the thanatomicrobiome (i.e., thanatos-, Greek defn., death), is not clear. To fill the void, we examined the thanatomicrobiome of the spleen, liver, brain, heart and blood of human cadavers. These organs are thought to be devoid of microorganisms in a healthy adult host. We report that the thanatomicrobiome was highly similar among organ tissues from the same cadaver but very different among the cadavers possibly due to differences in the elapsed time since death and/or environmental factors. Isolation of microbial DNA from cadavers is known to be a challenge. We compared the effectiveness of two methods by amplifying the 16S rRNA genes and sequencing the amplicons from four cadavers. Paired comparisons revealed that the conventional DNA extraction method (bead-beating in phenol/chloroform/bead-beating followed by ethanol precipitation) yielded more 16S rRNA amplicons (28 of 30 amplicons) than a second method (repeated cycles of heating/cooling followed by centrifugation to remove cellular debris) (19 of 30 amplicons). Shannon diversity index of the 16S rRNA genes revealed no significant difference by extraction method. The present report provides a proof of principle that the thanatomicrobiome may be an efficient biomarker to study postmortem transformations of cadavers.