Simulate_PCR for amplicon prediction and annotation from multiplex, degenerate primers and probes

BACKGROUND: Pairing up primers to amplify desired targets and avoid undesired cross reactions can be a combinatorial challenge. Effective prediction of specificity and inclusivity from multiplexed primers and TaqMan®/Luminex® probes is a critical step in PCR design. RESULTS: Code is described to identify all primer and probe combinations from a list of unpaired, unordered candidates that should produce a product. It predicts and extracts all amplicon sequences in a large sequence database from a list of primers and probes, allowing degenerate bases and user-specified levels of primer-target mismatch tolerance. Amplicons hit by TaqMan®/Luminex® probes are indicated, and products may be annotated with gene information from NCBI. Fragment length distributions are calculated to predict electrophoretic gel banding patterns. CONCLUSIONS: Simulate_PCR is the only freely available software that can be run from the command line for high throughput applications which can calculate all products from large lists of primers and probes compared to a large sequence database such as nt. It requires no prior knowledge of how primers should be paired. Degenerate bases are allowed and entire amplicon sequences are extracted and annotated with gene information. Examples are provided for sets of TaqMan®/Luminex® PCR signatures predicted to amplify all HIV-1 genomes, all Coronaviridae genomes, and a group of antibiotic resistance genes. The software is a command line perl script freely available as open source

Software for optimization of SNP and PCR-RFLP genotyping to discriminate many genomes with the fewest assays

BACKGROUND: Microbial forensics is important in tracking the source of a pathogen, whether the disease is a naturally occurring outbreak or part of a criminal investigation. RESULTS: A method and SPR Opt (SNP and PCR-RFLP Optimization) software to perform a comprehensive, whole-genome analysis to forensically discriminate multiple sequences is presented. Tools for the optimization of forensic typing using Single Nucleotide Polymorphism (SNP) and PCR-Restriction Fragment Length Polymorphism (PCR-RFLP) analyses across multiple isolate sequences of a species are described. The PCR-RFLP analysis includes prediction and selection of optimal primers and restriction enzymes to enable maximum isolate discrimination based on sequence information. SPR Opt calculates all SNP or PCR-RFLP variations present in the sequences, groups them into haplotypes according to their co-segregation across those sequences, and performs combinatoric analyses to determine which sets of haplotypes provide maximal discrimination among all the input sequences. Those set combinations requiring that membership in the fewest haplotypes be queried (i.e. the fewest assays be performed) are found. These analyses highlight variable regions based on existing sequence data. These markers may be heterogeneous among unsequenced isolates as well, and thus may be useful for characterizing the relationships among unsequenced as well as sequenced isolates. The predictions are multi-locus. Analyses of mumps and SARS viruses are summarized. Phylogenetic trees created based on SNPs, PCR-RFLPs, and full genomes are compared for SARS virus, illustrating that purported phylogenies based only on SNP or PCR-RFLP variations do not match those based on multiple sequence alignment of the full genomes. CONCLUSION: This is the first software to optimize the selection of forensic markers to maximize information gained from the fewest assays, accepting whole or partial genome sequence data as input. As more sequence data becomes available for multiple strains and isolates of a species, automated, computational approaches such as those described here will be essential to make sense of large amounts of information, and to guide and optimize efforts in the laboratory. The software and source code for SPR Opt is publicly available and free for non-profit use at

Predicting the sensitivity and specificity of published real-time PCR assays

<p>Abstract</p> <p>Background</p> <p>In recent years real-time PCR has become a leading technique for nucleic acid detection and quantification. These assays have the potential to greatly enhance efficiency in the clinical laboratory. Choice of primer and probe sequences is critical for accurate diagnosis in the clinic, yet current primer/probe signature design strategies are limited, and signature evaluation methods are lacking.</p> <p>Methods</p> <p>We assessed the quality of a signature by predicting the number of true positive, false positive and false negative hits against all available public sequence data. We found real-time PCR signatures described in recent literature and used a BLAST search based approach to collect all hits to the primer-probe combinations that should be amplified by real-time PCR chemistry. We then compared our hits with the sequences in the NCBI taxonomy tree that the signature was designed to detect.</p> <p>Results</p> <p>We found that many published signatures have high specificity (almost no false positives) but low sensitivity (high false negative rate). Where high sensitivity is needed, we offer a revised methodology for signature design which may designate that multiple signatures are required to detect all sequenced strains. We use this methodology to produce new signatures that are predicted to have higher sensitivity and specificity.</p> <p>Conclusion</p> <p>We show that current methods for real-time PCR assay design have unacceptably low sensitivities for most clinical applications. Additionally, as new sequence data becomes available, old assays must be reassessed and redesigned. A standard protocol for both generating and assessing the quality of these assays is therefore of great value. Real-time PCR has the capacity to greatly improve clinical diagnostics. The improved assay design and evaluation methods presented herein will expedite adoption of this technique in the clinical lab.</p

Identification of SNPs Associated with Variola Virus Virulence

Background: Decades after the eradication of smallpox, its etiological agent, variola virus (VARV), remains a threat as a potential bioweapon. Outbreaks of smallpox around the time of the global eradication effort exhibited variable case fatality rates (CFRs), likely attributable in part to complex viral genetic determinants of smallpox virulence. We aimed to identify genome-wide single nucleotide polymorphisms associated with CFR. We evaluated unadjusted and outbreak geographic location-adjusted models of single SNPs and two- and three-way interactions between SNPs. Findings: Using the data mining approach multifactor dimensionality reduction (MDR), we identified five VARV SNPs in models significantly associated with CFR. The topper forming unadjusted model and adjusted models both revealed the same two-way gene-gene interaction. We discuss the biological plausibility of the influence of the SNPs identified these and other significant models on the strain-specific virulence of VARV. Conclusions: We have identified genetic loci in the VARV genome that are statistically associated with VARV virulence as measured by CFR. While our ability to infer a causal relationship between the specific SNPs identified in our analysis and VARV virulence is limited, our results suggest that smallpox severity is in part associated with VARV strain variation and that VARV virulence may be determined by multiple genetic loci. This study represents the first application of MDR to the identification of pathogen gene-gene interactions for predicting infectious disease outbreak severity

A microbial detection array (MDA) for viral and bacterial detection

BACKGROUND: Identifying the bacteria and viruses present in a complex sample is useful in disease diagnostics, product safety, environmental characterization, and research. Array-based methods have proven utility to detect in a single assay at a reasonable cost any microbe from the thousands that have been sequenced. METHODS: We designed a pan-Microbial Detection Array (MDA) to detect all known viruses (including phages), bacteria and plasmids and developed a novel statistical analysis method to identify mixtures of organisms from complex samples hybridized to the array. The array has broader coverage of bacterial and viral targets and is based on more recent sequence data and more probes per target than other microbial detection/discovery arrays in the literature. Family-specific probes were selected for all sequenced viral and bacterial complete genomes, segments, and plasmids. Probes were designed to tolerate some sequence variation to enable detection of divergent species with homology to sequenced organisms, and to have no significant matches to the human genome sequence. RESULTS: In blinded testing on spiked samples with single or multiple viruses, the MDA was able to correctly identify species or strains. In clinical fecal, serum, and respiratory samples, the MDA was able to detect and characterize multiple viruses, phage, and bacteria in a sample to the family and species level, as confirmed by PCR. CONCLUSIONS: The MDA can be used to identify the suite of viruses and bacteria present in complex samples

Activation of alpha4* nAChRs is necessary and sufficient for varenicline-induced reduction of alcohol consumption

Recently, the smoking cessation therapeutic varenicline, a nicotinic acetylcholine receptor (nAChR) partial agonist, has been shown to reduce alcohol consumption. However, the mechanism and nAChR subtype(s) involved are unknown. Here we demonstrate that varenicline and alcohol exposure, either alone or in combination, selectively activates dopaminergic (DAergic) neurons within the posterior, but not the anterior, ventral tegmental area (VTA). To gain insight into which nAChR subtypes may be involved in the response to alcohol, we analyzed nAChR subunit gene expression in posterior VTA DAergic neurons. Ethanol-activated DAergic neurons expressed higher levels of alpha4, alpha6, and beta3 subunit genes compared with nonactivated neurons. To examine the role of nicotinic receptors containing the alpha4 subunit (alpha4* nAChRs) in varenicline-induced reduction of alcohol consumption, we examined the effect of the drug in two complementary mouse models, a knock-out line that does not express the alpha4 subunit (alpha4 KO) and another line that expresses alpha4* nAChRs hypersensitive to agonist (Leu9\u27Ala). While varenicline (0.1-0.3 mg/kg, i.p.) reduced 2% and 20% alcohol consumption in wild-type (WT) mice, the drug did not significantly reduce consumption in alpha4 KO animals. Conversely, low doses of varenicline (0.0125-0.05 mg/kg, i.p.) that had little effect in WT mice dramatically reduced ethanol intake in Leu9\u27Ala mice. Infusion of varenicline into the posterior, but not the anterior VTA was sufficient to reduce alcohol consumption. Together, our data indicate that activation of alpha4* nAChRs is necessary and sufficient for varenicline reduction of alcohol consumption

Technology and Consumer Engagement for LOOP

We are exploring how LOOP can develop their technology to increase consumer engagement. Our group will achieve this by exploring the following objectives: To investigate consumer comfort levels on privacy sharing, to assess how sellers find value in an online consignment store, to explore how the user experience on a company\u27s website affects consumer loyalty, to evaluate how brands interact with consumers through technology, to explore how consumers can engage with online small-businesses, to explore buyer-seller communication in e-commerce, and to evaluate consumer engagement in online sustainable shopping

Draft versus finished sequence data for DNA and protein diagnostic signature development

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10(−3)–10(−5) (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures