Evaluation of analytical performance of the STANDARDTM M10 MPX/OPX assay for the simultaneous DNA detection and clade attribution of Monkeypox virus

Monkeypox virus (MPXV) infection confirmation needs reliable polymerase chain reaction (PCR) assays; in addition, viral clade attribution is a key factor in containment measures, considering a more severe syndrome in clade I and the possibility of simultaneous circulation. This study evaluates the performance of all-in-one STANDARD M10 MPX/OPX (SD BIOSENSOR, South Korea - M10). Frozen samples from 205 subjects were selected and stratified according to routine test results (RealStar® Orthopoxvirus PCR Kit 1.0, Altona DIAGNOTICS, Germany - RS; RS-1): in detail, 100 negative skin lesions (SL) and 200 positive samples at the variable stage of infection were analysed. Positive samples were retested with RS (RS-2). Positive and Negative Percent Agreements (PPA, NPA) were calculated. The median (IQR) Ct values of RS and M10 (OPXV target) assays were highly similar. The PPA of M10 compared to RS-1 was 89.5% considering system interpretation, and 96.0% when the operator classified results as positive if any target was detected; NPA was 100%. Comparing the RS-2 run and M10, an overall concordance of 95.3% between assays was found; however, considering operator interpretation, M10 returned more positive results than RS-2. The occurrence of False-Negative results was likely associated with the influence of thawing on low viral concentration; no False-Positive tests were observed. All samples collected at the time of Mpox diagnosis were positive and M10 correctly attributed the clade (West-Africa/II). The M10 MPX/OPX assay demonstrated high reliability in confirming MPXV infection and clade attribution.

Comparison of commonly used and new methods to determine small molecule non-specific binding to human liver microsomes

Accurate measurement of non-specific binding of a drug candidate to human liver microsomes (HLM) can be critical for the accurate determination of key enzyme kinetic parameters such as Michaelis-Menton (Km), reversible inhibition (Ki), or inactivation (KI) constants. Several methods have been developed to determine non-specific binding of small molecules to HLM, such as rapid equilibrium dialysis (RED), ultrafiltration (UF), HLM bound to magnetizable beads (HLM-beads), ultracentrifugation (UC), the linear extrapolation stability assay (LESA), and the Transil™ system. Despite various differences in methodology between these methods, it is generally presumed that similar free fraction values (fu,mic) should be generated. To evaluate this hypothesis, a test set of 9 compounds were selected, representing low (high fu,mic value) and significant (low fu,mic value) HLM binding, respectively, across HLM concentrations tested in this manuscript. The fu,mic values were determined using a single compound concentration (1.0 µM) and three HLM concentrations (0.025, 0.50, and 1.0 mg/mL). When the HLM non-specific binding event is not extensive resulting in high fu,mic values, all methods generated similar fu,mic values. However, fu,mic values varied markedly across assay formats when high binding to HLM occurred, where fu,mic values differed by up to 33-fold depending on the method used. Potential causes for such discrepancies across the various methods employed, practical implications related to conduct the different assays, and implications to clinical drug-drug interaction (DDI) predictions are discussed.

Applicability of the cobas 6800 System for Epstein-Barr viral load quantitation using whole-blood specimens

OBJECTIVES

This study aimed to evaluate the analytical performance of the cobas 6800 System (Roche Diagnostics) and assess the feasibility of using whole-blood specimens instead of plasma.

METHODS

The analytical performance of the cobas EBV test (Roche Diagnostics) was evaluated. Thereafter, 120 clinical samples were collected to compare the cobas EBV test and the artus EBV RG PCR Kit (Qiagen). The results of the cobas EBV test conducted using paired plasma as well as 5× and 10× diluted whole-blood specimens were compared with those of the artus EBV RG PCR Kit performed using whole blood.

RESULTS

The precision of the cobas EBV test was acceptable, and its linearity was confirmed to be within the range of 2.85 to 6.89 log IU/mL. Cross-reactivity was not observed. The best qualitative agreement (Cohen κ = 0.733) was observed using 5× diluted whole blood; the best quantitative correlation (Spearman correlation coefficient = 0.6865) was observed using 10× diluted whole blood.

CONCLUSIONS

A significant discrepancy was observed in the results obtained from the 2 assays because of the different specimens used. We observed, however, that diluting whole blood before conducting the cobas EBV test effectively resolved polymerase chain reaction inhibition and viscosity issues, leading to an acceptable correlation with the results from the artus EBV RG PCR Kit conducted using whole blood.

Methodological differences between studies confound one-size-fits-all approaches to managing surface waterways for food and water safety

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform meta-analyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quantified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological differences between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies.IMPORTANCEThe microbial ecology of water is already complex, without the added complications of methodological differences between studies. This study highlights the difficulty in comparing water quality data from projects that used different sampling or laboratory methods. These findings have direct implications for end users as there is no clear way to generalize findings in order to characterize broad-scale ecological phenomenon and develop science-based guidance. To best support development of risk assessments and guidance for monitoring and managing waters, data collection and methods need to be standardized across studies. A minimum set of data attributes that all studies should collect and report in a standardized way is needed. Given the diversity of methods used within applied and environmental microbiology, similar studies are needed for other microbiology subfields to ensure that guidance and policy are based on a robust interpretation of the literature.

A platform for comparing subgroup identification methodologies

Since the advent of the phrase "subgroup identification," there has been an explosion of methodologies that seek to identify meaningful subgroups of patients with exceptional response in order to further the realization of personalized medicine. However, to perform fair comparison and understand what methods work best under different clinical trials situations, a common platform is needed for comparative effectiveness of these various approaches. In this paper, we describe a comprehensive project that created an extensive platform for evaluating subgroup identification methods as well as a publicly posted challenge that was used to elicit new approaches. We proposed a common data-generating model for creating virtual clinical trial datasets that contain subgroups of exceptional responders encompassing the many dimensions of the problem or null scenarios in which there are no such subgroups. Furthermore, we created a common scoring system for evaluating performance of purported methods for identifying subgroups. This makes it possible to benchmark methodologies in order to understand what methods work best under different clinical trial situations. The findings from this project produced considerable insights and allow us to make recommendations for how the statistical community can better compare and contrast old and new subgroup identification methodologies.

Evaluation of the BioFire® FilmArray® Pneumonia plus Panel for Detecting Bacterial Etiological Agents of Lower Respiratory Tract Infections in an Oncologic Hospital. Comparison with Conventional Culture Method

Conventional methods used to determine pneumonia pathogens are characterized by low sensitivity and long turnaround times. Introducing new tests with better parameters in patients at higher risk of infections is highly anticipated. The results of the conventional quantitative culture method (CM) in determining the bacterial etiology of pneumonia were compared with the results of the Pneumonia plus Panel test (PNP; BioFire® Diagnostics, USA) in 79 samples of bronchoalveolar lavage (BAL). Materials were collected from 79 patients with suspected pneumonia treated in an oncologic hospital due to solid tumors. Only 16/79 BAL samples (20.3%) were true positive (TP) for bacterial etiology in CM vs. 27/79 samples (34.2%) true positive in the PNP test. The total agreement between methods of interpreting the result (positive or negative) was 84.8%. The most prevalent pathogens in both methods were Staphylococcus aureus, followed by Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae. The PNP test identified several respiratory pathogens that were not grown in culture. The semiquantitative value reported by the PNP test was higher than that reported by culture. The PNP test vs. combined test (PNP test and CM methods) demonstrated positive predictive value (PPV) and negative predictive value (NPV) values of 100.0% and 98.1%, and the sensitivity and specificity were 96.4% and 100.0%. The PNP test is a good tool for determining the etiology of bacterial pneumonia and may support the care of an oncologic patient. However, further large-sample studies are needed to research in strictly defined groups of oncologic patients.

Application of serum pools in insulin harmonization: Commutability and stability

BACKGROUND

Recalibration using serum pools assigned by higher-order reference methods had been demonstrated to be effective in improving the agreement among insulin immunoassays. To promote the application of serum pools in insulin harmonization, this study analyzed serum pools' commutability between insulin immunoassays, and their short- and long-term stability at different temperatures. The agreement between commonly used immunoassays was also evaluated.

METHODS

Insulin in 69 individual serum samples, 10 serum pools, and three EQA samples (lyophilized powder of serum pools) were detected by six widely used immunoassays. The commutability of serum pools and EQA samples was evaluated according to the IFCC-recommended approach. Serum pools' stability at different temperatures was investigated by placing them at various temperatures for varying lengths of time. Individual serum samples' results were analyzed using the Bland-Altman and Passing and Bablok regression analyses.

RESULTS

Serum pools were commutable among most assays, the EQA samples-lyophilized serum pools-were non-commutable among most assays. Serum pools can be stably stored at -20°C and -80°C for at least one year, but can only be stably stored at room temperature for twenty-four hours. Significant relative differences were observed among assays. Recalibration using serum pools can only improve the assays' agreement at middle and high insulin levels, but not at low levels.

CONCLUSIONS

Serum pools were commutable and stable for insulin measurement and can be used in insulin harmonization. The existing EQA materials were non-commutable between most assays, and other EQA materials, such as serum pools, should be studied.

Using A-Mode Ultrasound to Assess the Body Composition of Soccer Players: A Comparative Study of Prediction Formulas

For elite athletes, monitoring body composition is important for maximizing performance without health risks. Amplitude (A)-mode ultrasound (AUS) has attracted increasing attention as an alternative to skinfold thickness measurements commonly used for assessing the amount of body fat in athletes. AUS accuracy and precision, however, depend on the formula used to predict body fat percentage (%BF) from subcutaneous fat layer thicknesses. Therefore, this study evaluates the accuracy of the 1-point biceps (B1), 9-sites Parrillo, 3-sites Jackson and Pollock (JP3), and 7-sites Jackson and Pollock (JP7) formulas. Relying on the previous validation of the JP3 formula in college-aged male athletes, we took AUS measurements in 54 professional soccer players (aged 22.9 ± 3.83 y, mean ± SD) and compared the results given by different formulas. The Kruskal-Wallis test indicated significant differences (p < 10^-6), and Conover's post hoc test revealed that the JP3 and JP7 data come from the same distribution, whereas the data given by B1 and P9 differ from all the others. Lin's concordance correlation coefficients for B1 vs. JP7, P9 vs. JP7, and JP3 vs. JP7 were 0.464, 0.341, and 0.909, respectively. The Bland-Altman analysis indicated mean differences of -0.5 %BF between JP3 and JP7, 4.7 %BF between P9 and JP7, and 3.1 %BF between B1 and JP7. This study suggests that JP7 and JP3 are equally valid, whereas P9 and B1 overestimate %BF in athletes.

Comparison of the ELISA method with the nephelometric method for determination of serum amyloid A in patients with COVID 19

This study aimed to examine the agreement of the serum amyloid A (SAA) values determined using the ELISA test and the nephelometric automated method. This study included 80 serum samples obtained from patients with COVID-19. Samples were determined using ELISA and the nephelometric method. Wilcoxon signed ranks test showed a statistically significant difference in the calculated median values (Z = -2.432, p = 0.015). The correlation between methods was statistically significant (r = 0.603, p < 0.0001). Bland Altman analysis showed a bias of 56.6 mg/L and a relative bias of 7.4% between the methods. The results of this study indicate that further studies are needed that will examine the compliance between the ELISA and the nephelometric method for determining SAA, and the results must be carefully interpreted based on the method used.

Validation of new automated turbidimetric immunoassays for the measurement of haptoglobin and inter-α-trypsin inhibitor heavy chain H4 specific for the bovine species

BACKGROUND

Good strategical programs are required for the early detection of disease even in the absence of evident clinical signs, which is crucial in satisfying animal welfare. Haptoglobin (Hp) and inter-α-trypsin inhibitor heavy chain H4 (ITIH4) are acute phase proteins and good biomarkers of early inflammation in cattle, with plasma levels that significantly increase after injury or infection.

OBJECTIVES

We aimed to develop and validate two new immunoturbidimetric methods for Hp and ITIH4.

METHODS

Species-specific antibodies were obtained and used to develop the immunoassays. For the Hp assay, antibodies were fixed to latex microparticles to enhance detection. The immunoassays were set up in an automated analyzer to carry out validation studies. Reference intervals were calculated using Reference Value Advisor.

RESULTS

The Hp immunoturbidimetric method had a linear analytical range up to 0.40 mg/mL. The limit of detection (LoD) was 0.005 mg/mL, and the limit of quantification (LoQ) was 0.007 mg/mL. Total imprecision was less than 7%. Comparison with ELISA and single radial immunodiffusion (SRID) showed good correlation, whereas the comparison with the colorimetric method showed constant and proportional differences. The ITIH4 immunoassay showed linearity up to 5 mg/mL, and the LoD was 0.002 mg/mL. Total imprecision was less than 6%. Method comparison showed a good correlation with single radial immunodiffusion, both methods being equivalent. Bilirubin, triglycerides, and hemoglobin presented no interference in any of the assays. Reference intervals were 0.007-0.017 mg/mL for Hp and 0.2-0.7 mg/mL for ITIH4 in dairy cows 10 days before parturition.

CONCLUSIONS

Immunoturbidimetric methods developed for Hp and ITIH4 can measure basal and increased levels of these proteins, showing adequate precision, accuracy, and robustness.

A comparison of intracranial volume estimation methods and their cross-sectional and longitudinal associations with age

Intracranial volume (ICV) is frequently used in volumetric magnetic resonance imaging (MRI) studies, both as a covariate and as a variable of interest. Findings of associations between ICV and age have varied, potentially due to differences in ICV estimation methods. Here, we compared five commonly used ICV estimation methods and their associations with age. T1-weighted cross-sectional MRI data was included for 651 healthy individuals recruited through the NORMENT Centre (mean age = 46.1 years, range = 12.0-85.8 years) and 2410 healthy individuals recruited through the UK Biobank study (UKB, mean age = 63.2 years, range = 47.0-80.3 years), where longitudinal data was also available. ICV was estimated with FreeSurfer (eTIV and sbTIV), SPM12, CAT12, and FSL. We found overall high correlations across ICV estimation method, with the lowest observed correlations between FSL and eTIV (r = .87) and between FSL and CAT12 (r = .89). Widespread proportional bias was found, indicating that the agreement between methods varied as a function of head size. Body weight, age, sex, and mean ICV across methods explained the most variance in the differences between ICV estimation methods, indicating possible confounding for some estimation methods. We found both positive and negative cross-sectional associations with age, depending on dataset and ICV estimation method. Longitudinal ICV reductions were found for all ICV estimation methods, with annual percentage change ranging from -0.293% to -0.416%. This convergence of longitudinal results across ICV estimation methods offers strong evidence for age-related ICV reductions in mid- to late adulthood.

A Comparative Study of Two In Vivo PET Verification Methods in Clinical Cases

Purpose

Positron emission tomography (PET) range verification is an important method that can help improve the confidence in proton therapy for clinical applications. Two kinds of verification methods are implemented and compared based on clinical cases in this study.

Method

The study is conducted on 14 breast cancer patients following proton irradiation treatment. Verification is done by calculating the depth error between the numerically predicted values with the measured PET image along the beam direction. Point-based and segment-based methods are applied and compared. The verification results are presented as depth error means and standard deviations in a region of interest (ROI).

Results

The mean value of the depth error of all 14 cases is within the range of [-3, 3] mm for both point-based and segment-based methods, and only one case result calculated by the point-based method is slightly beyond -3 mm. When comparing the mean depth error from the two methods, the paired t-test result shows that the p-value is 0.541, and the standard deviation of the segment-based method is smaller than that of the point-based method.

Conclusion

In breast cancer case verification application, point-based and segment-based methods show no significant difference in the mean value of results. Both methods can quantify the accuracy of proton radiotherapy to the millimeter level.

Improving and Comparing Probiotic Plate Count Methods by Analytical Procedure Lifecycle Management

Probiotics are live microorganisms that confer a health benefit to the host when administered in adequate amounts. This definition links probiotic efficacy to microbial viability. The current gold standard assay for probiotic potency is enumeration using classical microbiology plating-based procedures, yielding results in colony-forming units (CFU). One drawback to plating-based procedures is high variability due to intrinsic and extrinsic uncertainties. These uncertainties make comparison between analytical procedures challenging. In this article, we provide tools to reduce measurement uncertainty and strengthen the reliability of probiotic enumerations by using analytical procedure lifecycle management (APLM). APLM is a tool that uses a step-by-step process to define procedure performance based on the concept that the reportable value (final CFU result) must be fit for its intended use. Once the procedure performance is defined, the information gathered through APLM can be used to evaluate and compare procedures. Here, we discuss the theory behind applying APLM and give practical information about its application to CFU enumeration procedures for probiotics using a simulated example and data set. Data collected in a manufacturer’s development laboratory is included to support application of the concept. Implementation of APLM can lead to reduced variability by identifying specific factors (e.g., the dilution step) with significant impact on the variability and providing insights to procedural modifications that lead to process improvement. Understanding and control of the analytical procedure is improved by using these tools. The probiotics industry can confidently apply the information and analytical results generated to make decisions about processes and formulation, including overage requirements. One benefit of this approach is that companies can reduce overage costs. More reliable procedures for viable cell count determinations will improve the quality evaluation of probiotic products, and hence manufacturing procedures, while ensuring that products deliver clinically demonstrated beneficial doses.

Comprehensive evaluation of methods for small extracellular vesicles separation from human plasma, urine and cell culture medium

One of the challenges that restricts the evolving extracellular vesicle (EV) research field is the lack of a consensus method for EV separation. This may also explain the diversity of the experimental results, as co-separated soluble proteins and lipoproteins may impede the interpretation of experimental findings. In this study, we comprehensively evaluated the EV yields and sample purities of three most popular EV separation methods, ultracentrifugation, precipitation and size exclusion chromatography combined with ultrafiltration, along with a microfluidic tangential flow filtration device, Exodisc, in three commonly used biological samples, cell culture medium, human urine and plasma. Single EV phenotyping and density-gradient ultracentrifugation were used to understand the proportion of true EVs in particle separations. Our findings suggest Exodisc has the best EV yield though it may co-separate contaminants when the non-EV particle levels are high in input materials. We found no 100% pure EV preparations due to the overlap of their size and density with many non-EV particles in biofluids. Precipitation has the lowest sample purity, regardless of sample type. The purities of the other techniques may vary in different sample types and are largely dependent on their working principles and the intrinsic composition of the input sample. Researchers should choose the proper separation method according to the sample type, downstream analysis and their working scenarios.

Comparison of nine heart rate-based models to predict work metabolism of Forest workers

Predicted work metabolism (WM) from 9 heart rate (HR)-based models were compared to measured WM obtained during work in 39 forest workers. Using measured (i.e. raw) HR in these models can overestimate actual WM since the HR increase associated with body heat accumulation is non-metabolic. Hence, accuracy of WM prediction was assessed on all possible combinations of models using raw HR and corrected HR (thermal component removed) and with five different estimates of maximum work capacity (MWC) for the models that require it as an input. The 50 model combinations produced a wide range of WM estimates. Three models using individual calibration produced the lowest RMSE and narrowest LoA with corrected HR (rRMSE ≤13%; LoA [rBias <5% ± 25%]). One of the models that requires neither determination of the thermal component nor individual calibration performed very well (rRMSE = 18%; LoA [rBias = 1% ± 36%]). Practitioner Summary: These results provide a better understanding of the accuracy of various HR-based work metabolism (WM) estimation models. This information should prove particularly useful to ergonomics professionals wishing to select a method that provides accurate estimation of WM from HR measurements during work in varied thermal environments. Abbreviations: BMI: body mass index; HR: heart rate (beats per min); HR99: HR value exceeded during 99% of the duration of the HR recording period; HRcorr: heart rate without thermal pulses; HRraw: measured heart rate; HRres: heart rate reserve; HRrest: heart rate at rest; LoA: limits of agreement; Mrest: resting metabolism; MWC: maximum work capacity; RMSE: root mean square error; VO2: oxygen consumption; VO2 max: maximum oxygen consumption; WM: work metabolism.

Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse's Limb

The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.

Capillary blood tests may overestimate ketosis: triangulation between three different measures of β-hydroxybutyrate

The ketone body β-hydroxybutyrate (BHB), assessed by a point-of-care meter in venous whole blood (BHBv), was used as the main outcome in a study on nutritional ketosis in healthy older adults. Two other BHB measures were also used in the study for validation and exploratory purposes, and here we report findings on correlation and agreement between those three methods. Ketosis in the range of 0-1.5 mmol/L was induced in 15 healthy volunteers by intake of medium-chain fatty acids after a 12-h fast. BHBv was assessed at 12 time points for 4 h. The same point-of-care meter was also used to test capillary blood (BHBc) at three time points, and a laboratory test determined total ketones (TK) in plasma (BHBp + acetoacetate) at four time points. A total of 180 cases included simultaneous data on BHBv, BHBc, BHBp, and TK. TK correlated with BHBp (Pearson's r = 0.99), BHBv (r = 0.91), and BHBc (r = 0.91), all P < 0.0001. BHBv and BHBp had good agreement in absolute values. However, the slope between BHBc and BHBv, measured with the same device, was in the range of 0.64-0.78 in different regression models, indicating substantially higher BHB concentrations in capillary versus venous blood. We conclude that all three methods are valid to detect relative changes in ketosis, but our results highlight the importance of method considerations and the possible need to adjust cutoffs, e.g., in the management of ketoacidosis and in the evaluation and comparison of dietary interventions.

Salmonella Serotyping; Comparison of the Traditional Method to a Microarray-Based Method and an in silico Platform Using Whole Genome Sequencing Data

Salmonella is one of the most common causes of food-borne diseases worldwide. While Salmonella molecular subtyping by Whole Genome Sequencing (WGS) is increasingly used for outbreak and source tracking investigations, serotyping remains as a first-line characterization of Salmonella isolates. The traditional phenotypic method for serotyping is logistically challenging, as it requires the use of more than 150 specific antisera and well trained personnel to interpret the results. Consequently, it is not a routine method for the majority of laboratories. Several rapid molecular methods targeting O and H loci or surrogate genomic markers have been developed as alternative solutions. With the expansion of WGS, in silico Salmonella serotype prediction using WGS data is available. Here, we compared a microarray method using molecular markers, the Check and Trace Salmonella assay (CTS) and a WGS-based serotype prediction tool that targets molecular determinants of serotype (SeqSero) to the traditional phenotypic method using 100 strains representing 45 common and uncommon serotypes. Compared to the traditional method, the CTS assay correctly serotyped 97% of the strains, four strains gave a double serotype prediction. Among the inconclusive data, one strain was not predicted and two strains were incorrectly identified. SeqSero was evaluated with two versions (SeqSero 1 and the alpha test version of SeqSero 2). The correct antigenic formula was predicted by SeqSero 1 for 96 and 95% of strains using raw reads and assembly, respectively. However, 34 and 33% of these predictions included multiple serotypes by raw reads and assembly. With raw reads, one strain was not identified and three strains were discordant with phenotypic serotyping result. With assembly, three strains were not predicted and two strains were incorrectly predicted. While still under development, SeqSero 2 maintained the accuracy of antigenic formula prediction at 98% and reduced multiple serotype prediction rate to 13%. One strain had no prediction and one strain was incorrectly predicted. Our study indicates that the CTS assay is a good alternative for routine laboratories as it is an easy to use method with a short turn-around-time. SeqSero is a reliable replacement for phenotypic serotyping if WGS is routinely implemented.

Comparative evaluation of molecular methods for the quantitative measure of torquetenovirus viremia, the new surrogate marker of immune competence

BACKGROUND

Torquetenovirus (TTV) viremia is emerging as a promising tool to assess functional immune competence, to predict posttransplant immune-related complications, and eventually to customize immunosuppression.

METHODS

In this study, 327 blood samples were tested using two real-time PCR (rtPCR) assays both targeted to the untranslated region of the TTV genome. The first assay was an in-house rtPCR developed by our group, the second one was the recently marketed TTV R-GENE assay.

RESULTS

In the validation study, the TTV R-GENE showed good performances in precision and reproducibility, and sensitivity as low as 12 TTV DNA copies/mL, like previously reported for the in-house rtPCR. The Bland-Altman analysis showed that the mean difference between the two methods was -0.3 log copies/mL. In the comparison study, 69% and 72% of samples were detected positive by rtPCR and TTV R-GENE, respectively (94% concordance, κ = 0.88). Performances did not differ between the two rtPCRs by type of TTV group examined. When a newly-developed in-house digital droplet PCR was applied for TTV quantification and used as an alternative method of comparison on 94 samples, the results strongly correlated with those obtained by the two rtPCR methods (99% concordance).

CONCLUSION

In summary, all the molecular methods assayed are highly sensitive and accurate in quantitation of TTV DNA in blood samples.

Crohn disease risk prediction-Best practices and pitfalls with exome data

The Critical Assessment of Genome Interpretation (CAGI) experiment is the first attempt to evaluate the state-of-the-art in genetic data interpretation. Among the proposed challenges, Crohn disease (CD) risk prediction has become the most classic problem spanning three editions. The scientific question is very hard: can anybody assess the risk to develop CD given the exome data alone? This is one of the ultimate goals of genetic analysis, which motivated most CAGI participants to look for powerful new methods. In the 2016 CD challenge, we implemented all the best methods proposed in the past editions. This resulted in 10 algorithms, which were evaluated fairly by CAGI organizers. We also used all the data available from CAGI 11 and 13 to maximize the amount of training samples. The most effective algorithms used known genes associated with CD from the literature. No method could evaluate effectively the importance of unannotated variants by using heuristics. As a downside, all CD datasets were strongly affected by sample stratification. This affected the performance reported by assessors. Therefore, we expect that future datasets will be normalized in order to remove population effects. This will improve methods comparison and promote algorithms focused on causal variants discovery.

Identifying differential transcription factor binding in ChIP-seq

ChIP seq is a widely used assay to measure genome-wide protein binding. The decrease in costs associated with sequencing has led to a rise in the number of studies that investigate protein binding across treatment conditions or cell lines. In addition to the identification of binding sites, new studies evaluate the variation in protein binding between conditions. A number of approaches to study differential transcription factor binding have recently been developed. Several of these methods build upon established methods from RNA-seq to quantify differences in read counts. We compare how these new approaches perform on different data sets from the ENCODE project to illustrate the impact of data processing pipelines under different study designs. The performance of normalization methods for differential ChIP-seq depends strongly on the variation in total amount of protein bound between conditions, with total read count outperforming effective library size, or variants thereof, when a large variation in binding was studied. Use of input subtraction to correct for non-specific binding showed a relatively modest impact on the number of differential peaks found and the fold change accuracy to biological validation, however a larger impact might be expected for samples with more extreme copy number variations between them. Still, it did identify a small subset of novel differential regions while excluding some differential peaks in regions with high background signal. These results highlight proper scaling for between-sample data normalization as critical for differential transcription factor binding analysis and suggest bioinformaticians need to know about the variation in level of total protein binding between conditions to select the best analysis method. At the same time, validation using fold-change estimates from qRT-PCR suggests there is still room for further method improvement.

Evaluation of calling algorithms for array-CGH

Copy number variation (CNV) detection has become an integral part many of genetic studies and new technologies promise to revolutionize our ability to detect and link them to disease. However, recent studies highlight discrepancies in the genome wide CNV profile when measured by different technologies and even by the same technology. Furthermore, the change point algorithms used to call CNVs can have substantial disagreement on the same data set. We focus this article on comparative genomic hybridization (CGH) arrays because this platform lends itself well to accurate statistical modeling. We describe some newer methodological developments in local statistics that are well suited for CNV detection and calling on CGH arrays. Then we use both simulation studies and public data to compare these new local methods with the global methods that currently dominate literature. These results offer suggestions for choosing a particular method and provide insight to the lack of reproducibility that has been seen in the field so far.