DetectiV: visualization, normalization and significance testing for pathogen-detection microarray data

MAGpy: a reproducible pipeline for the downstream analysis of metagenome-assembled genomes (MAGs)

MOTIVATION

Metagenomics is a powerful tool for assaying the DNA from every genome present in an environment. Recent advances in bioinformatics have enabled the rapid assembly of near-complete metagenome-assembled genomes (MAGs), and there is a need for reproducible pipelines that can annotate and characterize thousands of genomes simultaneously, to enable identification and functional characterization.

RESULTS

Here we present MAGpy, a scalable and reproducible pipeline that takes multiple genome assemblies as FASTA and compares them to several public databases, checks quality, suggests a taxonomy and draws a phylogenetic tree.

AVAILABILITY AND IMPLEMENTATION

MAGpy is available on github: https://github.com/WatsonLab/MAGpy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bioinformatics and Microarray-Based Technologies to Viral Genome Sequence Analysis

Identification of microbial pathogen is an important event which lead to diagnosis, treatment, and control of infections produce by them. The high-throughput technology like microarray and new-generation sequencing machine are able to generate huge amount of nucleotide sequences of viral and bacterial genome of both known and unknown pathogens. Few years ago it was the DNA microarrays which had great potential to screen all the known pathogens and yet to be identified pathogen simultaneously. But after the development of a new generation sequencing, technologies and advance computational approach researchers are looking forward for a complete understanding of microbes and host interactions. The powerful sequencing platform is rapidly transforming the landscape of microbial identification and characterization. As bioinformatics analysis tools and databases are easily available to researchers, the enormous amount of data generated can be meaningfully handled for better understanding of the microbial world. Here in this chapter, we present commentary on how the computational method incorporated with sequencing technique made easy for microbial detection and characterization.

Development and Assessment of a Diagnostic DNA Oligonucleotide Microarray for Detection and Typing of Meningitis-Associated Bacterial Species

Meningitis is commonly caused by infection with a variety of bacterial or viral pathogens. Acute bacterial meningitis (ABM) can cause severe disease, which can progress rapidly to a critical life-threatening condition. Rapid diagnosis of ABM is critical, as this is most commonly associated with severe sequelae with associated high mortality and morbidity rates compared to viral meningitis, which is less severe and self-limiting. We have designed a microarray for detection and diagnosis of ABM. This has been validated using randomly amplified DNA targets (RADT), comparing buffers with or without formamide, in glass slide format or on the Alere ArrayTube^TM (Alere Technologies GmbH) microarray platform. Pathogen-specific signals were observed using purified bacterial nucleic acids and to a lesser extent using patient cerebral spinal fluid (CSF) samples, with some technical issues observed using RADT and glass slides. Repurposing the array onto the Alere ArrayTube^TM platform and using a targeted amplification system increased specific and reduced nonspecific hybridization signals using both pathogen nucleic and patient CSF DNA targets, better revealing pathogen-specific signals although sensitivity was still reduced in the latter. This diagnostic microarray is useful as a laboratory diagnostic tool for species and strain designation for ABM, rather than for primary diagnosis.

DNA Microarray Platform for Detection and Surveillance of Viruses Transmitted by Small Mammals and Arthropods

Viruses transmitted by small mammals and arthropods serve as global threats to humans. Most emergent and re-emergent viral agents are transmitted by these groups; therefore, the development of high-throughput screening methods for the detection and surveillance of such viruses is of great interest. In this study, we describe a DNA microarray platform that can be used for screening all viruses transmitted by small mammals and arthropods (SMAvirusChip) with nucleotide sequences that have been deposited in the GenBank. SMAvirusChip was designed with more than 15,000 oligonucleotide probes (60-mers), including viral and control probes. Two SMAvirusChip versions were designed: SMAvirusChip v1 contains 4209 viral probes for the detection of 409 viruses, while SMAvirusChip v2 contains 4943 probes for the detection of 416 viruses. SMAvirusChip was evaluated with 20 laboratory reference-strain viruses. These viruses could be specifically detected when alone in a sample or when artificially mixed within a single sample. The sensitivity of SMAvirusChip was evaluated using 10-fold serial dilutions of dengue virus (DENV). The results showed a detection limit as low as 2.6E3 RNA copies/mL. Additionally, the sensitivity was one log10 lower (2.6E2 RNA copies/mL) than quantitative real-time RT-PCR and sufficient to detect viral genomes in clinical samples. The detection of DENV in serum samples of DENV-infected patients (n = 6) and in a whole blood sample spiked with DENV confirmed the applicability of SMAvirusChip for the detection of viruses in clinical samples. In addition, in a pool of mosquito samples spiked with DENV, the virus was also detectable. SMAvirusChip was able to specifically detect viruses in cell cultures, serum samples, total blood samples and a pool of mosquitoes, confirming that cellular RNA/DNA did not interfere with the assay. Therefore, SMAvirusChip may represent an innovative surveillance method for the rapid identification of viruses transmitted by small mammals and arthropods.

Ribosomal RNA depletion or exclusion has negligible effect on the detection of viruses in a pan viral microarray

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Ribosomal RNA depletion protocols were assessed to improve microarray performance.

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The outcome was compared with random amplification protocol.

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Ribosomal RNA depletion had little effect on the microarray performance.

Pan viral DNA microarrays, which can detect known, novel and multiple viral infections, are major laboratory assets contributing to the control of infectious diseases. The large quantity of ribosomal RNA (rRNA) found in tissue samples is thought to be a major factor contributing to the comparatively lower sensitivity of detecting RNA viruses, as a sequence-independent PCR is used to amplify unknown samples for microarray analysis. This study aimed to determine whether depletion or exclusion of rRNA can improve microarray detection and simplify its analysis. Therefore, two different rRNA depletion and exclusion protocols, RiboMinus™ technology and non-rRNA binding hexanucleotides, were applied to the microarray sample processing and the outcome was compared with those of the sequence-independent amplification protocol. This study concludes that the two procedures, described to deplete or exclude rRNA, have negligible effect on the microarrays detection and analysis and might only in combination with further techniques result in a significant enhancement of sensitivity. Currently, existing protocols of random amplification and background adjustment are pertinent for the purpose of sample processing for microarray analysis.

Investigation into an outbreak of encephalomyelitis caused by a neuroinvasive porcine sapelovirus in the United Kingdom

An outbreak of neurological disease in grower pigs characterised by ataxia and paraparesis was investigated in this study. The outbreak occurred 3-4 weeks post weaning in grower pigs which displayed signs of spinal cord damage progressing to recumbency. Pathology in the affected spinal cords and to a lesser extent in the brainstem was characterised by pronounced inflammation and neuronophagia in the grey matter. Molecular investigation using a pan-virus microarray identified a virus related to porcine sapelovirus (PSV) in the spinal cord of the two affected pigs examined. Analysis of 802 nucleotides of the virus polymerase gene showed the highest homology with those of viruses in the genus Sapelovirus of Picornaviridae. This PSV, strain G5, shared 91-93%, 67-69% and 63% nucleotide homology with porcine, simian and avian sapeloviruses, respectively. The nucleotide homology to other members of the Picornaviridae ranged from 41% to 62%. Furthermore, viral antigen was detected and co-localised in the spinal cord lesions of affected animals by an antibody known to react with PSV. In conclusion, clinical and laboratory observations of the diseased pigs in this outbreak are consistent with PSV-G5 being the causative agent. To the best of the authors' knowledge, this is the first unequivocal report of polioencephalomyelitis in pigs by a neuroinvasive PSV in the United Kingdom.

VIPR HMM: a hidden Markov model for detecting recombination with microbial detection microarrays

MOTIVATION

Current methods in diagnostic microbiology typically focus on the detection of a single genomic locus or protein in a candidate agent. The presence of the entire microbe is then inferred from this isolated result. Problematically, the presence of recombination in microbial genomes would go undetected unless other genomic loci or protein components were specifically assayed. Microarrays lend themselves well to the detection of multiple loci from a given microbe; furthermore, the inherent nature of microarrays facilitates highly parallel interrogation of multiple microbes. However, none of the existing methods for analyzing diagnostic microarray data has the capacity to specifically identify recombinant microbes. In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data.

RESULTS

We have expanded upon our previous implementation of VIPR by incorporating a hidden Markov model (HMM) to detect recombinant genomes. We trained our HMM on a set of non-recombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM correctly identified 95% of the 62 inter-species recombination breakpoints in the validation set and only two false-positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of detecting recombinant viruses based on diagnostic microarray hybridization patterns.

AVAILABILITY

VIPR HMM is freely available for academic use and can be downloaded from http://ibridgenetwork.org/wustl/vipr.

CONTACT

davewang@borcim.wustl.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Modeling formamide denaturation of probe-target hybrids for improved microarray probe design in microbial diagnostics

Application of high-density microarrays to the diagnostic analysis of microbial communities is challenged by the optimization of oligonucleotide probe sensitivity and specificity, as it is generally unfeasible to experimentally test thousands of probes. This study investigated the adjustment of hybridization stringency using formamide with the idea that sensitivity and specificity can be optimized during probe design if the hybridization efficiency of oligonucleotides with target and non-target molecules can be predicted as a function of formamide concentration. Sigmoidal denaturation profiles were obtained using fluorescently labeled and fragmented 16S rRNA gene amplicon of Escherichia coli as the target with increasing concentrations of formamide in the hybridization buffer. A linear free energy model (LFEM) was developed and microarray-specific nearest neighbor rules were derived. The model simulated formamide melting with a denaturant m-value that increased hybridization free energy (ΔG°) by 0.173 kcal/mol per percent of formamide added (v/v). Using the LFEM and specific probe sets, free energy rules were systematically established to predict the stability of single and double mismatches, including bulged and tandem mismatches. The absolute error in predicting the position of experimental denaturation profiles was less than 5% formamide for more than 90 percent of probes, enabling a practical level of accuracy in probe design. The potential of the modeling approach for probe design and optimization is demonstrated using a dataset including the 16S rRNA gene of Rhodobacter sphaeroides as an additional target molecule. The LFEM and thermodynamic databases were incorporated into a computational tool (ProbeMelt) that is freely available at http://DECIPHER.cee.wisc.edu.

Microarrays for pathogen detection and analysis

DNA microarrays have emerged as a viable platform for detection of pathogenic organisms in clinical and environmental samples. These microbial detection arrays occupy a middle ground between low cost, narrowly focused assays such as multiplex PCR and more expensive, broad-spectrum technologies like high-throughput sequencing. While pathogen detection arrays have been used primarily in a research context, several groups are aggressively working to develop arrays for clinical diagnostics, food safety testing, environmental monitoring and biodefense. Statistical algorithms that can analyze data from microbial detection arrays and provide easily interpretable results are absolutely required in order for these efforts to succeed. In this article, we will review the most promising array designs and analysis algorithms that have been developed to date, comparing their strengths and weaknesses for pathogen detection and discovery.

VIPR: A probabilistic algorithm for analysis of microbial detection microarrays

BACKGROUND

All infectious disease oriented clinical diagnostic assays in use today focus on detecting the presence of a single, well defined target agent or a set of agents. In recent years, microarray-based diagnostics have been developed that greatly facilitate the highly parallel detection of multiple microbes that may be present in a given clinical specimen. While several algorithms have been described for interpretation of diagnostic microarrays, none of the existing approaches is capable of incorporating training data generated from positive control samples to improve performance.

RESULTS

To specifically address this issue we have developed a novel interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), which uses Bayesian inference to capitalize on empirical training data to optimize detection sensitivity. To illustrate this approach, we have focused on the detection of viruses that cause hemorrhagic fever (HF) using a custom HF-virus microarray. VIPR was used to analyze 110 empirical microarray hybridizations generated from 33 distinct virus species. An accuracy of 94% was achieved as measured by leave-one-out cross validation.

CONCLUSIONS

VIPR outperformed previously described algorithms for this dataset. The VIPR algorithm has potential to be broadly applicable to clinical diagnostic settings, wherein positive controls are typically readily available for generation of training data.

Nonparametric methods for the analysis of single-color pathogen microarrays

BACKGROUND

The analysis of oligonucleotide microarray data in pathogen surveillance and discovery is a challenging task. Target template concentration, nucleic acid integrity, and host nucleic acid composition can each have a profound effect on signal distribution. Exploratory analysis of fluorescent signal distribution in clinical samples has revealed deviations from normality, suggesting that distribution-free approaches should be applied.

RESULTS

Positive predictive value and false positive rates were examined to assess the utility of three well-established nonparametric methods for the analysis of viral array hybridization data: (1) Mann-Whitney U, (2) the Spearman correlation coefficient and (3) the chi-square test. Of the three tests, the chi-square proved most useful.

CONCLUSIONS

The acceptance of microarray use for routine clinical diagnostics will require that the technology be accompanied by simple yet reliable analytic methods. We report that our implementation of the chi-square test yielded a combination of low false positive rates and a high degree of predictive accuracy.

Metagenomics for the discovery of novel human viruses

Modern laboratory techniques for the detection of novel human viruses are greatly needed as physicians and epidemiologists increasingly deal with infectious diseases caused by new or previously unrecognized pathogens. There are many clinical syndromes in which viruses are suspected to play a role, but for which traditional microbiology techniques routinely fail in uncovering the etiologic agent. In addition, new viruses continue to challenge the human population owing to the encroachment of human settlements into animal and livestock habitats, globalization, climate change, growing numbers of immunocompromised people and bioterrorism. Metagenomics-based tools, such as microarrays and high-throughput sequencing are ideal for responding to these challenges. Pan-viral microarrays, containing representative sequences from all known viruses, have been used to detect novel and distantly-related variants of known viruses. Sequencing-based methods have also been successfully employed to detect novel viruses and have the potential to detect the full spectrum of viruses, including those present in low numbers.

Emerging technologies for the detection of rabies virus: challenges and hopes in the 21st century

The diagnosis of rabies is routinely based on clinical and epidemiological information, especially when exposures are reported in rabies-endemic countries. Diagnostic tests using conventional assays that appear to be negative, even when undertaken late in the disease and despite the clinical diagnosis, have a tendency, at times, to be unreliable. These tests are rarely optimal and entirely dependent on the nature and quality of the sample supplied. In the course of the past three decades, the application of molecular biology has aided in the development of tests that result in a more rapid detection of rabies virus. These tests enable viral strain identification from clinical specimens. Currently, there are a number of molecular tests that can be used to complement conventional tests in rabies diagnosis. Indeed the challenges in the 21st century for the development of rabies diagnostics are not of a technical nature; these tests are available now. The challenges in the 21st century for diagnostic test developers are two-fold: firstly, to achieve internationally accepted validation of a test that will then lead to its acceptance by organisations globally. Secondly, the areas of the world where such tests are needed are mainly in developing regions where financial and logistical barriers prevent their implementation. Although developing countries with a poor healthcare infrastructure recognise that molecular-based diagnostic assays will be unaffordable for routine use, the cost/benefit ratio should still be measured. Adoption of rapid and affordable rabies diagnostic tests for use in developing countries highlights the importance of sharing and transferring technology through laboratory twinning between the developed and the developing countries. Importantly for developing countries, the benefit of molecular methods as tools is the capability for a differential diagnosis of human diseases that present with similar clinical symptoms. Antemortem testing for human rabies is now possible using molecular techniques. These barriers are not insurmountable and it is our expectation that if such tests are accepted and implemented where they are most needed, they will provide substantial improvements for rabies diagnosis and surveillance. The advent of molecular biology and new technological initiatives that combine advances in biology with other disciplines will support the development of techniques capable of high throughput testing with a low turnaround time for rabies diagnosis.