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Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces.

ABSTRACT: While it is universally accepted that intact RNA constitutes the best representation of the steady-state of transcription, there is no gold standard to define RNA quality prior to gene expression analysis. In this report, we evaluated the reliability of conventional methods for RNA quality assessment including UV spectroscopy and 28S:18S area ratios, and demonstrated their inconsistency. We then used two new freely available classifiers, the Degradometer and RIN systems, to produce user-independent RNA quality metrics, based on analysis of microcapillary electrophoresis traces. Both provided highly informative and valuable data and the results were found highly correlated, while the RIN system gave more reliable data. The relevance of the RNA quality metrics for assessment of gene expression differences was tested by Q-PCR, revealing a significant decline of the relative expression of genes in RNA samples of disparate quality, while samples of similar, even poor integrity were found highly comparable. We discuss the consequences of these observations to minimize artifactual detection of false positive and negative differential expression due to RNA integrity differences, and propose a scheme for the development of a standard operational procedure, with optional registration of RNA integrity metrics in public repositories of gene expression data.

SUBMITTER: Imbeaud S

PROVIDER: S-EPMC1072807 | biostudies-literature | 2005

REPOSITORIES: biostudies-literature

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Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces.

Imbeaud Sandrine S Graudens Esther E Boulanger Virginie V Barlet Xavier X Zaborski Patrick P Eveno Eric E Mueller Odilo O Schroeder Andreas A Auffray Charles C

Nucleic acids research 20050330 6

While it is universally accepted that intact RNA constitutes the best representation of the steady-state of transcription, there is no gold standard to define RNA quality prior to gene expression analysis. In this report, we evaluated the reliability of conventional methods for RNA quality assessment including UV spectroscopy and 28S:18S area ratios, and demonstrated their inconsistency. We then used two new freely available classifiers, the Degradometer and RIN systems, to produce user-independ ...[more]

PMID: 15800207

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Project description:BackgroundAssessing RNA quality is essential for gene expression analysis, as the inclusion of degraded samples may influence the interpretation of expression levels in relation to biological and/or clinical parameters. RNA quality can be analyzed by agarose gel electrophoresis, UV spectrophotometer, or microcapillary electrophoresis traces, and can furthermore be evaluated using different methods. No generally accepted recommendations exist for which technique or evaluation method is the best choice. The aim of the present study was to use microcapillary electrophoresis traces from the Bioanalyzer to compare three methods for evaluating RNA quality in 24 fresh frozen invasive breast cancer tissues: 1) Manual method = subjective evaluation of the electropherogram, 2) Ratio Method = the ratio between the 28S and 18S peaks, and 3) RNA integrity number (RIN) method = objective evaluation of the electropherogram. The results were also related to gene expression profiling analyses using 27K oligonucleotide microarrays, unsupervised hierarchical clustering analysis and ontological mapping.ResultsComparing the methods pair-wise, Manual vs. Ratio showed concordance (good vs. degraded RNA) in 20/24, Manual vs. RIN in 23/24, and Ratio vs. RIN in 21/24 samples. All three methods were concordant in 20/24 samples. The comparison between RNA quality and gene expression analysis showed that pieces from the same tumor and with good RNA quality clustered together in most cases, whereas those with poor quality often clustered apart. The number of samples clustering in an unexpected manner was lower for the Manual (n = 1) and RIN methods (n = 2) as compared to the Ratio method (n = 5). Assigning the data into two groups, RIN > or = 6 or RIN < 6, all but one of the top ten differentially expressed genes showed decreased expression in the latter group; i.e. when the RNA became degraded. Ontological mapping using GoMiner (p < or = 0.05; > or = 3 genes changed) revealed deoxyribonuclease activity, collagen, regulation of cell adhesion, cytosolic ribosome, and NADH dehydrogenase activity, to be the five categories most affected by RNA quality.ConclusionThe results indicate that the Manual and RIN methods are superior to the Ratio method for evaluating RNA quality in fresh frozen breast cancer tissues. The objective measurement when using the RIN method is an advantage. Furthermore, the inclusion of samples with degraded RNA may profoundly affect gene expression levels.

Project description:BACKGROUND:With the accessibility and widespread use of mobile phones, mobile phone apps targeting medication adherence may be useful tools to help patients take medications as prescribed. OBJECTIVE:Our objectives were to (1) characterize and assess mobile phone medication adherence apps guided by a conceptual framework on the focus of adherence interventions and (2) conduct a content analysis of Web-based reviews to explore users' perspectives and experiences with mobile phone medication adherence apps. METHODS:We searched for mobile phone medication adherence apps using keyword searches in Apple and Android operating systems. We characterized all apps in terms of number of downloads, ratings, languages, cost, and disease target. We categorized apps according to 4 key features of (1) alerting to take medication, (2) tracking medication taking, (3) reminding to refill or indicating amount of medication left, and (4) storing medication information. We then selected representative apps from each operating system for detailed quality assessment and user testing. We also downloaded Web-based reviews for these selected apps and conducted a qualitative content analysis using an inductive approach involving steps of initial open coding, construction of categories, and abstraction into themes. RESULTS:We identified 704 apps (443 from Apple and 261 from Android). The majority of apps across both operating systems had 1 or 2 features-specifically, 37.2% (165/443) and 38.1% (169/443) of Apple apps, respectively, and 41.4% (108/261) and 31.4% (108/261) of Android apps, respectively. Quality assessment and user testing of 20 selected apps revealed apps varied in quality and commonly focused on behavioral strategies to enhance medication adherence through alerts, reminders, and logs. A total of 1323 eligible Web-based reviews from these 20 selected apps were analyzed, and the following themes emerged: (1) features and functions appreciated by users, which included the ability to set up customized medication regimen details and reminders, monitor other health information (eg, vitals, supplements, and manage multiple people or pets), support health care visits (eg, having a list of medications and necessary health information in 1 app); (2) negative user experiences that captured technical difficulties (glitches, confusing app navigation, and poor interoperability), dosage schedule, and reminder setup inflexibility; and (3) desired functions and features related to optimization of information input, improvement of reminders, and upgrading app performance (better synchronization or backup of data and interoperability). CONCLUSIONS:A large number of mobile phone medication adherence apps are currently available. The majority of apps have features representing a behavioral approach to intervention. Findings of the content analysis offer mostly positive feedback as well as insights into current limitations and improvements that could be addressed in current and future medication adherence apps.

Project description:The paradigm of variational quantum classifiers (VQCs) encodes classical information as quantum states, followed by quantum processing and then measurements to generate classical predictions. VQCs are promising candidates for efficient utilizations of noisy intermediate scale quantum (NISQ) devices: classifiers involving M-dimensional datasets can be implemented with only [Formula: see text] qubits by using an amplitude encoding. A general framework for designing and training VQCs, however, is lacking. An encouraging specific embodiment of VQCs, quantum circuit learning (QCL), utilizes an ansatz: a circuit with a predetermined circuit geometry and parametrized gates expressing a time-evolution unitary operator; training involves learning the gate parameters through a gradient-descent algorithm where the gradients themselves can be efficiently estimated by the quantum circuit. The representational power of QCL, however, depends strongly on the choice of the ansatz, as it limits the range of possible unitary operators that a VQC can search over. Equally importantly, the landscape of the optimization problem may have challenging properties such as barren plateaus and the associated gradient-descent algorithm may not find good local minima. Thus, it is critically important to estimate (i) the price of ansatz; that is, the gap between the performance of QCL and the performance of ansatz-independent VQCs, and (ii) the price of using quantum circuits as classical classifiers: that is, the performance gap between VQCs and equivalent classical classifiers. This paper develops a computational framework to address both these open problems. First, it shows that VQCs, including QCL, fit inside the well-known kernel method. Next it introduces a framework for efficiently designing ansatz-independent VQCs, which we call the unitary kernel method (UKM). The UKM framework enables one to estimate the first known computationally-determined bounds on both the price of ansatz and the price of any speedup advantages of VQCs: numerical results with datatsets of various dimensions, ranging from 4 to 256, show that the ansatz-induced gap can vary between 10 and 20[Formula: see text], while the VQC-induced gap (between VQC and kernel method) can vary between 10 and 16[Formula: see text]. To further understand the role of ansatz in VQCs, we also propose a method of decomposing a given unitary operator into a quantum circuit, which we call the variational circuit realization (VCR): given any parameterized circuit block (as for example, used in QCL), it finds optimal parameters and the number of layers of the circuit block required to approximate any target unitary operator with a given precision.

Project description:As hormonal disorders are linked to several diseases, the accurate quantitation of steroid hormone levels in serum is crucial in order to provide patients with a reliable diagnosis. Mass spectrometry-based methods are regarded as having the highest level of specificity and sensitivity. However, immunoassays are more commonly used in routine diagnostics to measure steroid levels as they are more cost effective and straightforward to conduct. This study analyzes the external quality assessment results for the measurement of testosterone, progesterone and 17β-estradiol in serum using immunoassays between early 2020 and May 2022. As reference measurement procedures are available for the three steroid hormones, the manufacturer-specific biases were normalized to the reference measurement values. The manufacturer-specific coefficients of variation were predominantly inconspicuous, below 20% for the three hormones when outliers are disregarded, however there were large differences between the various manufacturer collectives. For some collectives, the median bias to the respective reference measurement value was repeatedly greater than ±35%, which is the acceptance limit defined by the German Medical Association. In the case of testosterone and progesterone determination, some collectives tended to consistently over- or underestimate analyte concentrations compared to the reference measurement value, however, for 17β-estradiol determination, both positive and negative biases were observed. This insufficient level of accuracy suggests that cross-reactivity continues to be a fundamental challenge when antibody detection is used to quantify steroids with a high structural similarity. Distinct improvements in standardization are required to provide accurate analysis and thus, reliable clinical interpretations. The increased accuracy of the AX immunoassay for testosterone measurement, as observed in the INSTAND EQAs between 2020 and 2022, could be the result of a recalibration of the assay and raises hope for further improvement of standardization of immunoassay-based steroid hormone analyses in the coming years.

Project description:BACKGROUND AND PURPOSE:Diffusion MRI tractography enables to investigate white matter pathways noninvasively by reconstructing estimated fiber pathways. However, such tractograms remain biased and nonquantitative. Several techniques have been proposed to reestablish the link between tractography and tissue microstructure by modeling the diffusion signal or fiber orientation distribution (FOD) with the given tractogram and optimizing each fiber or compartment contribution according to the diffusion signal or FOD. Nevertheless, deriving a reliable quantification of connectivity strength between different brain areas is still a challenge. Moreover, evaluating the quality of a tractogram and measuring the possible error sources contained in a specific reconstructed fiber bundle also remains difficult. Lastly, all of these optimization techniques fail if specific fiber populations within a tractogram are underrepresented, for example, due to algorithmic constraints, anatomical properties, fiber geometry or seeding patterns. METHODS:In this work, we propose an approach which enables the inspection of the quality of a tractogram optimization by evaluating the residual error signal and its FOD representation. The automated fiber quantification (AFQ) is applied, whereby the framework is extended to reflect not only scalar diffusion metrics along a fiber bundle, but also directionally dependent FOD amplitudes along and perpendicular to the fiber direction. Furthermore, we also present an up-sampling procedure to increase the number of streamlines of a given fiber population. The introduced error metrics and fiber up-sampling method are tested and evaluated on single-shell diffusion data sets of 16 healthy volunteers. RESULTS AND CONCLUSION:Analyzing the introduced error measures on specific fiber bundles shows a considerable improvement in applying the up-sampling method. Additionally, the error metrics provide a useful tool to spot and identify potential error sources in tractograms.

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Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces.

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Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces.

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