Greene SCPrimer: a rapid comprehensive tool for designing degenerate primers from multiple sequence alignments

Polymerase chain reaction (PCR) is widely applied in clinical and environmental microbiology. Primer design is key to the development of successful assays and is often performed manually by using multiple nucleic acid alignments. Few public software tools exist that allow comprehensive design of degenerate primers for large groups of related targets based on complex multiple sequence alignments. Here we present a method for designing such primers based on tree building followed by application of a set covering algorithm, and demonstrate its utility in compiling Multiplex PCR primer panels for detection and differentiation of viral pathogens

A method for automatically extracting infectious disease-related primers and probes from the literature

BACKGROUND: Primer and probe sequences are the main components of nucleic acid-based detection systems. Biologists use primers and probes for different tasks, some related to the diagnosis and prescription of infectious diseases. The biological literature is the main information source for empirically validated primer and probe sequences. Therefore, it is becoming increasingly important for researchers to navigate this important information. In this paper, we present a four-phase method for extracting and annotating primer/probe sequences from the literature. These phases are: (1) convert each document into a tree of paper sections, (2) detect the candidate sequences using a set of finite state machine-based recognizers, (3) refine problem sequences using a rule-based expert system, and (4) annotate the extracted sequences with their related organism/gene information. RESULTS: We tested our approach using a test set composed of 297 manuscripts. The extracted sequences and their organism/gene annotations were manually evaluated by a panel of molecular biologists. The results of the evaluation show that our approach is suitable for automatically extracting DNA sequences, achieving precision/recall rates of 97.98% and 95.77%, respectively. In addition, 76.66% of the detected sequences were correctly annotated with their organism name. The system also provided correct gene-related information for 46.18% of the sequences assigned a correct organism name. CONCLUSIONS: We believe that the proposed method can facilitate routine tasks for biomedical researchers using molecular methods to diagnose and prescribe different infectious diseases. In addition, the proposed method can be expanded to detect and extract other biological sequences from the literature. The extracted information can also be used to readily update available primer/probe databases or to create new databases from scratch.The present work has been funded, in part, by the European Commission through the ACGT integrated project (FP6-2005-IST-026996) and the ACTION-Grid support action (FP7-ICT-2007-2-224176), the Spanish Ministry of Science and Innovation through the OntoMineBase project (ref. TSI2006-13021-C02-01), the ImGraSec project (ref. TIN2007-61768), FIS/AES PS09/00069 and COMBIOMED-RETICS, and the Comunidad de Madrid, Spain.S

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Sweetpotato Virus C and Its Contribution to the Potyvirus Complex in Sweetpotato (Ipomoea batatas)

In Louisiana, sweetpotato (Ipomoea batatas) is infected in Louisiana by the four ubiquitous potyviruses: Sweetpotato feathery mottle virus (SPFMV), Sweetpotato virus G (SPVG), Sweetpotato virus 2 (SPV2) and the strain of SPFMV previously known as the common strain, recently renamed as Sweetpotato virus C (SPVC). These four viruses belong to the Potyviridae family, with single stranded RNA of ~11kb. In this group of plant viruses, a single polyprotein is coded entirely but later cleaved into ten mature proteins: P1, HC-pro, P3, 6K1, CI, 6K2, NIa-VPg, NIa-Pro Nib and Coat Protein (CP). In sweetpotato potyviruses, two additional open reading frames produced by polymerase slippage called PIPO and PISPO act as RNA silencing suppressors. Despite the minimal differences at the nucleotide level in these four viruses, their titers, vector transmissibility and presence in the field are different. The objectives of this research were: (i) redesign the qPCR assay of SPFMV and SPVC and determine the best organ and sampling time after sweetpotato transplanting to detect each of these four viruses; (ii) determine if SPVC is the missing element in reproducing the observed yield reduction of natural infections that occur in the field and; (iii) determine the complete sequences of nine isolates from sweetpotato production fields in Louisiana and analyze the genetic structure and variability compared to other isolates present in the world. Results suggested that leaf tissue at the 3rd week after transplanting is the best organ to sample to determine if the plant is infected with the four potyviruses. The inclusion of SPVC did not reproduce the storage root reduction observed under naturally infected plants and, the molecular variation was not high from other isolates previously sequenced but six isolates report recombination events in the CP and P1 region of their genome

MPprimer: a program for reliable multiplex PCR primer design

<p>Abstract</p> <p>Background</p> <p>Multiplex PCR, defined as the simultaneous amplification of multiple regions of a DNA template or multiple DNA templates using more than one primer set (comprising a forward primer and a reverse primer) in one tube, has been widely used in diagnostic applications of clinical and environmental microbiology studies. However, primer design for multiplex PCR is still a challenging problem and several factors need to be considered. These problems include mis-priming due to nonspecific binding to non-target DNA templates, primer dimerization, and the inability to separate and purify DNA amplicons with similar electrophoretic mobility.</p> <p>Results</p> <p>A program named MPprimer was developed to help users for reliable multiplex PCR primer design. It employs the widely used primer design program Primer3 and the primer specificity evaluation program MFEprimer to design and evaluate the candidate primers based on genomic or transcript DNA database, followed by careful examination to avoid primer dimerization. The graph-expanding algorithm derived from the greedy algorithm was used to determine the optimal primer set combinations (PSCs) for multiplex PCR assay. In addition, MPprimer provides a virtual electrophotogram to help users choose the best PSC. The experimental validation from 2× to 5× plex PCR demonstrates the reliability of MPprimer. As another example, MPprimer is able to design the multiplex PCR primers for DMD (dystrophin gene which caused Duchenne Muscular Dystrophy), which has 79 exons, for 20×, 20×, 20×, 14×, and 5× plex PCR reactions in five tubes to detect underlying exon deletions.</p> <p>Conclusions</p> <p>MPprimer is a valuable tool for designing specific, non-dimerizing primer set combinations with constrained amplicons size for multiplex PCR assays.</p

Developing a sensitive, high-throughput tool for rapid detection of agronomically important seed-borne pathogens of tomato

The limited specificity, sensitivity and multiplex capacity of detection techniques currently available for important seed-borne pathogens of tomato is a significant risk for the global tomato trade and production industry. These pathogens can be associated with seed at low concentrations but, due to their highly virulent nature, these low levels can be sufficient to infect germinating seedlings and spread to neighbouring plants and fields, potentially causing epidemics and economic losses. In this study, detection techniques currently available for phytodiagnostics were evaluated for the capacity to accurately detect and identify five agronomically important seed-borne pathogens of tomato: Pepino mosaic virus (PepMV), Tomato mosaic virus (ToMV), Clavibacter michiganensis subsp. michiganensis (Cmm), Xanthomonas campestris pv. vesicatoria and Pseudomonas syringae pv. tomato. A prototype diagnostic microarray was also designed in an attempt to develop a tool that could simultaneously detect these five seed-borne pathogens from a single sample. Viral detection based on serological techniques was rapid, accurate and reliable but only detected a single pathogen per assay and required supplementary bioassays to indicate the viability of detected viral pathogens. Selective media plating for bacterial detection demonstrated unreliable recovery of targeted bacteria from infected seed and leaf samples and required supplementary tests to validate the identity of presumptive positives. Assays were lengthy, laborious and sometimes too ambiguous for accurate diagnosis of bacterial pathogens. Nucleic acid-based technologies demonstrated improved sensitivity and specificity for detection of targets from pure culture, leaf and seed extracts, compared to conventional and serological methods, yet also required supplementary bioassays or media assays to validate the viability of detected pathogens. Amplification efficiency however, was affected by the presence of PCR inhibitors and despite positive detection, variable banding intensity in electrophoretic analysis of amplified products necessitated the use of reference cultures to validate diagnosis. The developed microarray incorporated 152 pathogen-specific and control probes to facilitate diagnosis and taxonomic classification of detected pathogens. The array was challenged with pure culture extracts of the five target pathogens, selected related and non-target, unrelated pathogens of tomato. Positive detection of each of the pathogens was demonstrated but the production of hybridisation signals was highly variable and extremely sensitive to minor technical differences. Each of the five pathogens were successfully detected in combination proving that different classes of seed-borne pathogens could be detected from a single sample using the developed microarray. This prototype microarray has good potential for phytodiagnostic screening of the five targeted pathogens, and further validation, optimisation and extension for testing tomato seed samples may facilitate incorporation of this array into standard diagnostic protocols

Metabolic and miRNA Profiling of TMV Infected Plants Reveals Biphasic Temporal Changes

Plant viral infections induce changes including gene expression and metabolic components. Identification of metabolites and microRNAs (miRNAs) differing in abundance along infection may provide a broad view of the pathways involved in signaling and defense that orchestrate and execute the response in plant-pathogen interactions. We used a systemic approach by applying both liquid and gas chromatography coupled to mass spectrometry to determine the relative level of metabolites across the viral infection, together with a miRs profiling using a micro-array based procedure. Systemic changes in metabolites were characterized by a biphasic response after infection. The first phase, detected at one dpi, evidenced the action of a systemic signal since no virus was detected systemically. Several of the metabolites increased at this stage were hormone-related. miRs profiling after infection also revealed a biphasic alteration, showing miRs alteration at 5 dpi where no virus was detected systemically and a late phase correlating with virus accumulation. Correlation analyses revealed a massive increase in the density of correlation networks after infection indicating a complex reprogramming of the regulatory pathways, either in response to the plant defense mechanism or to the virus infection itself. Our data propose the involvement of a systemic signaling on early miRs alteration

Plant Viruses: From Ecology to Control

Plant viruses cause many of the most important diseases threatening crops worldwide. Over the last quarter of a century, an increasing number of plant viruses have emerged in various parts of the world, especially in the tropics and subtropics. As is generally observed for plant viruses, most of the emerging viruses are transmitted horizontally by biological vectors, mainly insects. Reverse genetics using infectious clones—available for many plant viruses—has been used for identification of viral determinants involved in virus–host and virus–vector interactions. Although many studies have identified a number of factors involved in disease development and transmission, the precise mechanisms are unknown for most of the virus–plant–vector combinations. In most cases, the diverse outcomes resulting from virus–virus interactions are poorly understood. Although significant advances have been made towards understand the mechanisms involved in plant resistance to viruses, we are far from being able to apply this knowledge to protect cultivated plants from the all viral threats.The aim of this Special Issue was to provide a platform for researchers interested in plant virology to share their recent results. To achieve this, we invited the plant virology community to submit research articles, short communications and reviews related to the various aspects of plant virology: ecology, virus–plant host interactions, virus–vector interactions, virus–virus interactions, and control strategies. This issue contains some of the best current research in plant virology