Quantitative identification of fecal water pollution sources by TaqMan real-time PCR assays using Bacteroidales 16S rRNA genetic markers

PCR-based analysis of Bacteroidales 16S rRNA genes has emerged as a promising tool to identify sources of fecal water pollution. In this study, three TaqMan real-time PCR assays (BacGeneral, BacHuman, and BacBovine) were developed and evaluated for their ability to quantitatively detect general (total), human-specific, and bovine-specific Bacteroidales 16S rRNA genetic markers. The detection sensitivity was determined to be 6.5 copies of 16S rRNA gene for the BacGeneral and BacHuman assays and 10 copies for the BacBovine assay. The assays were capable of detecting approximately one to two cells per PCR. When tested with 70 fecal samples from various sources (human, cattle, pig, deer, dog, cat, goose, gull, horse, and raccoon), the three assays positively identified the target markers in all samples without any false-negative results. The BacHuman and BacBovine assays exhibited false-positive reactions with non-target samples in a few cases. However, the level of the false-positive reactions was about 50 times smaller than that of the true-positive ones, and therefore, these cross-reactions were unlikely to cause misidentifications of the fecal pollution sources. Microbial source-tracking capability was tested at two freshwater streams of which water quality was influenced by human and cattle feces, respectively. The assays accurately detected the presence of the corresponding host-specific markers upon fecal pollution and the persistence of the markers in downstream areas. The assays are expected to reliably determine human and bovine fecal pollution sources in environmental water samples.     

Primer design using genetic algorithm

MOTIVATION: Before performing a polymerase chain reaction experiment, a pair of primers to clip the target DNA subsequence is required. However, this is a tedious task as too many constraints need to be satisfied. Various kinds of approaches for designing a primer have been proposed in the last few decades, but most of them do not have restriction sites on the designed primers and do not satisfy the specificity constraint. RESULTS: The proposed algorithm imitates nature's process of evolution and genetic operations on chromosomes in order to achieve optimal solutions, and is a best fit for DNA behavior. Experimental results indicate that the proposed algorithm can find a pair of primers that not only obeys the design properties but also has a specific restriction site and specificity. Gel electrophoresis verifies that the proposed method really can clip out the target sequence. AVAILABILITY: A public version of the software is available on request from the authors.     

Motif identification method based on Gibbs sampling and genetic algorithm

The regulation of gene expression is the key of organism genetic mechanism. Motif identification is an important step in constructing expression regulatory network. Based on Gibbs sampling method, this work constructed position weight matrix, thereby proposing motif recognition method based on genetic algorithm. Scoring function is defined to update the population and obtain the convergence matrix of position weight, achieving the identification of motifs with different length. Simulation and experimental data sets were utilized to verify the accuracy and execution time of the algorithm.     

Measuring contaminant transport rates between water and sediments using limnocorrals

Radiotracers injected as soluble salts into 1.2 m³ limnocorrals in a shallow dystrophic lake were transported rapidly (2 to 12% d^–1) from the water to the bottom sediments. Removal rates of most contaminants declined after 14 days. Tracers were removed from the water much more rapidly than stable element analogs present naturally. After 3 weeks Am, Co, Ra, Hg, Sn and Fe activities on the enclosure walls were greater than 15% of the activity in the water. However, activity on the walls was a small fraction (< 6%) of the total amount of tracer injected. Particle fluxes inside the corrals were lower and much less variable than those measured outside. This difference appears to result from greater resuspension of bottom sediments in the lake than in the enclosures. Both particle-bound and soluble tracers were measured in cores of bottom sediments. Tracer sorption onto particles, diffusion into the bottom sediments, and uptake on the plastic enclosure walls were rapid and reversible. Tracer kinetics were very reproducible in replicate enclosures, providing a simple, experimental system in which limnological conditions can be manipulated. Loss rates and distributions of stable isotopes and radioisotopes can be used to develop and test a general model of element transport applicable to both short and long term analyses.     

Optimal subset selection of primary sequence features using the genetic algorithm for thermophilic proteins identification

A genetic algorithm (GA) coupled with multiple linear regression (MLR) was used to extract useful features from amino acids and g-gap dipeptides for distinguishing between thermophilic and non-thermophilic proteins. The method was trained by a benchmark dataset of 915 thermophilic and 793 non-thermophilic proteins. The method reached an overall accuracy of 95.4 % in a Jackknife test using nine amino acids, 38 0-gap dipeptides and 29 1-gap dipeptides. The accuracy as a function of protein size ranged between 85.8 and 96.9 %. The overall accuracies of three independent tests were 93, 93.4 and 91.8 %. The observed results of detecting thermophilic proteins suggest that the GA-MLR approach described herein should be a powerful method for selecting features that describe thermostabile machines and be an aid in the design of more stable proteins.     

Dynamic modeling of genetic networks using genetic algorithm and S-system

MOTIVATION: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. RESULTS: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.     

Motif discovery using an immune genetic algorithm

In this paper, a new immune genetic algorithm for motif discovery is proposed. The algorithm adopts concentration regulation mechanism to maintain the population diversity and vaccine mechanism to inhibit degeneracy during evolution. Experimental results have demonstrated the method's capacity to find known motifs in relatively long promoter sequences and multiple motifs within a single run.     

Protein structure alignment using a genetic algorithm

We have developed a novel, fully automatic method for aligning the three-dimensional structures of two proteins. The basic approach is to first align the proteins' secondary structure elements and then extend the alignment to include any equivalent residues found in loops or turns. The initial secondary structure element alignment is determined by a genetic algorithm. After refinement of the secondary structure element alignment, the protein backbones are superposed and a search is performed to identify any additional equivalent residues in a convergent process. Alignments are evaluated using intramolecular distance matrices. Alignments can be performed with or without sequential connectivity constraints. We have applied the method to proteins from several well-studied families: globins, immunoglobulins, serine proteases, dihydrofolate reductases, and DNA methyltransferases. Agreement with manually curated alignments is excellent. A web-based server and additional supporting information are available at http://engpub1.bu.edu/-josephs.     

A note on positive identification of paternity by using genetic markers

To see the efficiency of the statistical methods for positive identification of fathers using genetic markers, the statistical properties of the paternity index are studied algebraically and numerically. It is found that the currently used statistical methods are not powerful enough to discriminate between true fathers and non-excluded non-fathers, and, more often than not, may lead to false attributions of paternity. It is, therefore, suggested that exclusion of paternity is the only conclusive evidence that can be accepted by courts of law until better methods are devised.     

Anomalies in species identification of enterococci from veterinary sources using a commercial biochemical identification system

AIMS: A commercial biochemical panel ID kit was used to identify presumptive enterococci isolates of veterinary or agricultural origin obtained during different steps of culture. METHODS AND RESULTS: Fifty isolates identified as enterococci using a genus PCR assay were tested for genus and species identification using the BBL Crystal Identification Gram-Positive ID kit (Becton Dickinson, Sparks, MD, USA). Following sub-culture of the isolates three times, 59% agreement with the original panel ID was obtained. After four and six sub-cultures, percentage agreement increased to 61 and 64%, respectively. Nineteen of the 50 cultures were identified as both Enterococcus faecalis and E. faecium. CONCLUSIONS: As a result of the variability between speciation of isolates following re-culture, additional methods for speciation are warranted. SIGNIFICANCE AND IMPACT OF THE STUDY: This study suggests that the identification of the genus and species of non-human enterococcal isolates can vary greatly during successive passages when using this kit.     

Automated phylogenetic detection of recombination using a genetic algorithm

The evolution of homologous sequences affected by recombination or gene conversion cannot be adequately explained by a single phylogenetic tree. Many tree-based methods for sequence analysis, for example, those used for detecting sites evolving nonneutrally, have been shown to fail if such phylogenetic incongruity is ignored. However, it may be possible to propose several phylogenies that can correctly model the evolution of nonrecombinant fragments. We propose a model-based framework that uses a genetic algorithm to search a multiple-sequence alignment for putative recombination break points, quantifies the level of support for their locations, and identifies sequences or clades involved in putative recombination events. The software implementation can be run quickly and efficiently in a distributed computing environment, and various components of the methods can be chosen for computational expediency or statistical rigor. We evaluate the performance of the new method on simulated alignments and on an array of published benchmark data sets. Finally, we demonstrate that prescreening alignments with our method allows one to analyze recombinant sequences for positive selection.     

Molecular identification of blood meal sources of ticks (Acari,Ixodidae) using cytochrome b gene as a genetic marker

Blood meal analysis (BMA) from ticks allows for the identification of natural hosts of ticks (Acari: Ixodidae). The aim of this study is to identify the blood meal sources of field collected on-host ticks using PCR analysis. DNA of four genera of ticks was isolated and their cytochrome b (Cyt b) gene was amplified to identify host blood meals. A phylogenetic tree was constructed based on data of Cyt b sequences using Neighbor Joining (NJ) and Maximum Parsimony (MP) analysis using MEGA 5.05 for the clustering of hosts of tick species. Twenty out of 27 samples showed maximum similarity (99%) with GenBank sequences through a Basic Local Alignment Search Tool (BLAST) while 7 samples only showed a similarity range of between 91–98%. The phylogenetic trees showed that the blood meal samples were derived from small rodents (Leopoldamyssabanus, Rattustiomanicus and Sundamysmuelleri), shrews (Tupaiaglis) and mammals (Tapirusindicus and Prionailurusbengalensis), supported by 82–88% bootstrap values. In this study, Cyt b gene as a molecular target produced reliable results and was very significant for the effective identification of ticks’ blood meal. The assay can be used as a tool for identifying unknown blood meals of field collected on-host ticks.     

Buffalo species identification and delineation using genetic barcoding markers

Enrichment of barcode databases with mitochondrial cytochrome c oxidase subunit I (COI) barcode sequences in different animal taxa has become important for identification of animal source in food samples to prevent commercial fraud. In this study, COI barcode sequence in seventy one river buffalo samples were determined, analyzed and deposited in Genbank barcode database and barcode of life database (BOLD) to contribute for construction of public reference library for COI barcode sequence in river buffalo. Moreover COI barcode sequence was used to identify the closely related buffalo groups: river buffalo, swamp buffalo, lowland anoa and African buffalo. Results indicated the success of the COI barcode in the identification of each of the tested groups. Whereas a suggested sequence of other mitochondrial segment representing two successive transfer RNA (tRNA) genes; tRNA-Threonine (MT-TT) and tRNA-Proline (MT-TP) was failed to be used as a barcode marker for differentiation between the tested buffalo groups.     

Modelling molecular interaction pathways using a two-stage identification algorithm

In systems biology, molecular interactions are typically modelled using white-box methods, usually based on mass action kinetics. Unfortunately, problems with dimensionality can arise when the number of molecular species in the system is very large, which makes the system modelling and behavior simulation extremely difficult or computationally too expensive. As an alternative, this paper investigates the identification of two molecular interaction pathways using a black-box approach. This type of method creates a simple linear-in-the-parameters model using regression of data, where the output of the model at any time is a function of previous system states of interest. One of the main objectives in building black-box models is to produce an optimal sparse nonlinear one to effectively represent the system behavior. In this paper, it is achieved by applying an efficient iterative approach, where the terms in the regression model are selected and refined using a forward and backward subset selection algorithm. The method is applied to model identification for the MAPK signal transduction pathway and the Brusselator using noisy data of different sizes. Simulation results confirm the efficacy of the black-box modelling method which offers an alternative to the computationally expensive conventional approach.     

High-throughput identification of genetic markers using representational oligonucleotide microarray analysis

We describe a novel approach for high-throughput development of genetic markers using representational oligonucleotide microarray analysis. We test the performance of the method in sugar beet (Beta vulgaris L.) as a model for crop plants with little sequence information available. Genomic representations of both parents of a mapping population were hybridized on microarrays containing in total 146,554 custom made oligonucleotides based on sugar beet bacterial artificial chromosome (BAC) end sequences and expressed sequence tags (ESTs). Oligonucleotides showing a signal with one parental line only, were selected as potential marker candidates and placed onto an array, designed for genotyping of 184 F₂ individuals from the mapping population. Utilizing known co-dominant anchor markers we obtained 511 new dominant markers (392 derived from BAC end sequences, and 119 from ESTs) distributed over all nine sugar beet linkage groups and calculated genetic maps. Further improvements for large-scale application of the approach are discussed and its feasibility for the cost-effective and flexible generation of genetic markers is presented.     

Pose estimation in automated visual inspection using genetic algorithm

In this paper, we propose a genetic algorithm based approach to determine the pose of an object in Automated Visual Inspection having three degrees of freedom. We have investigated the effect of noise at 20 dB SNR and also mismatch resulting from incorrect correspondences between the object space points and the image space points, on the estimation of pose parameters. The maximum error in translation parameters is less than 0.45 cm and rotational error is less than 0.2 degree at 20 dB SNR. The error in parameter estimation is insignificant upto 7 pairs of mismatched points out of 24 points in object space and the results skyrockets when 8 or more pairs of points are mismatched. We have compared our result with that obtained by least square technique and it shows that GA based method outperform the gradient based technique when the number of vertices of the object to be inspected is small. These results have clearly established the robustness of GA in estimating the pose of an object with small number of vertices in automated visual inspection.     

Constructing large-scale genetic maps using an evolutionary strategy algorithm

This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with approximately 50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.     

Rapid identification and differentiation of agricultural faecal contamination sources using multiplex PCR

Aims:  To develop a quick, easy-to-use, robust and sensitive multiplex PCR assay to detect common sources of agricultural faecal contamination using a combination of bacterial and eukaryote-specific PCR targets.
Method and Results:  A novel multiplex PCR method was developed that utilizes primers specific for a conserved region of the eukaryote cytochrome-B gene as well as a universal 16S rRNA and the E. coli -specific uidA gene. This multiplex PCR assay was capable of identifying faecal amendments from pig, sheep, cow and goat sources in 24/30 (80%) of amended water samples.
Conclusions:  The method was capable of accurately identifying common agricultural sources.
Significance and Impact of the study:  The procedure described here is simple, rapid (<5 h) and can be used as a first step in microbial source tracking studies, particularly where agricultural faecal contamination is suspected.