Browsing by Subject "Quality control"

Now showing 1 - 8 of 8
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    Characterization of 137 Genomic DNA Reference Materials for 28 Pharmacogenetic Genes: A GeT-RM Collaborative Project
    (Elsevier, 2016-01) Pratt, Victoria M.; Everts, Robin E.; Aggarwal, Praful; Beyer, Brittany N.; Broeckel, Ulrich; Epstein-Baak, Ruth; Hujsak, Paul; Kornreich, Ruth; Liao, Jun; Lorier, Rachel; Scott, Stuart A.; Smith, Chingying Huang; Toji, Lorraine H.; Turner, Amy; Kalman, Lisa V.; Department of Medical and Molecular Genetics, IU School of Medicine
    Pharmacogenetic testing is increasingly available from clinical laboratories. However, only a limited number of quality control and other reference materials are currently available to support clinical testing. To address this need, the Centers for Disease Control and Prevention-based Genetic Testing Reference Material Coordination Program, in collaboration with members of the pharmacogenetic testing community and the Coriell Cell Repositories, has characterized 137 genomic DNA samples for 28 genes commonly genotyped by pharmacogenetic testing assays (CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, CYP3A5, CYP4F2, DPYD, GSTM1, GSTP1, GSTT1, NAT1, NAT2, SLC15A2, SLC22A2, SLCO1B1, SLCO2B1, TPMT, UGT1A1, UGT2B7, UGT2B15, UGT2B17, and VKORC1). One hundred thirty-seven Coriell cell lines were selected based on ethnic diversity and partial genotype characterization from earlier testing. DNA samples were coded and distributed to volunteer testing laboratories for targeted genotyping using a number of commercially available and laboratory developed tests. Through consensus verification, we confirmed the presence of at least 108 variant pharmacogenetic alleles. These samples are also being characterized by other pharmacogenetic assays, including next-generation sequencing, which will be reported separately. Genotyping results were consistent among laboratories, with most differences in allele assignments attributed to assay design and variability in reported allele nomenclature, particularly for CYP2D6, UGT1A1, and VKORC1. These publicly available samples will help ensure the accuracy of pharmacogenetic testing.
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    Effects of Rare Microbiome Taxa Filtering on Statistical Analysis
    (Frontiers Media, 2021-01-12) Cao, Quy; Sun, Xinxin; Rajesh, Karun; Chalasani, Naga; Gelow, Kayla; Katz, Barry; Shah, Vijay H.; Sanyal, Arun J.; Smirnova, Ekaterina; Medicine, School of Medicine
    Background: The accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Removing contaminants and filtering rare features are two common approaches to deal with this problem. While contaminant detection methods use auxiliary sequencing process information to identify known contaminants, filtering methods remove taxa that are present in a small number of samples and have small counts in the samples where they are observed. The latter approach reduces the extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured “mock” datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation as well as its impact on identifying taxa that discriminate between disease states. Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where the same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs while preserving the between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially abundant across groups of samples, and random forest models were used to rank features with the largest contribution toward disease classification. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between the filtering and the contaminant removal method shows that they have complementary effects and are advised to be used in conjunction. Conclusions: Filtering reduces the complexity of microbiome data while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.
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    Heregulin Activity Assays for Residual Testing of Cell Therapy Products
    (BMC, 2021-11-12) Monje, Paula V.; Bacallao, Ketty; Aparicio, Gabriela I.; Lalwani, Anil; Neurological Surgery, School of Medicine
    Background: Heregulin is a ligand for the protooncogene product ErbB/HER that acts as a key mitogenic factor for human Schwann cells (hSCs). Heregulin is required for sustained hSC growth in vitro but must be thoroughly removed before cell collection for transplantation due to potential safety concerns. The goal of this study was to develop simple cell-based assays to assess the effectiveness of heregulin addition to and removal from aliquots of hSC culture medium. These bioassays were based on the capacity of a β1-heregulin peptide to elicit ErbB/HER receptor signaling in adherent ErbB2+/ErbB3+ cells. Results: Western blotting was used to measure the activity of three different β1-heregulin/ErbB-activated kinases (ErbB3/HER3, ERK/MAPK and Akt/PKB) using phospho-specific antibodies against key activating residues. The duration, dose-dependency and specificity of β1-heregulin-initiated kinase phosphorylation were investigated, and controls were implemented for assay optimization and reproducibility to detect β1-heregulin activity in the nanomolar range. Results from these assays showed that the culture medium from transplantable hSCs elicited no detectable activation of the aforementioned kinases in independent rounds of testing, indicating that the implemented measures can ensure that the final hSC product is devoid of bioactive β1-heregulin molecules prior to transplantation. Conclusions: These assays may be valuable to detect impurities such as undefined soluble factors or factors for which other biochemical or biological assays are not yet available. Our workflow can be modified as necessary to determine the presence of ErbB/HER, ERK, and Akt activators other than β1-heregulin using native samples, such as fresh isolates from cell- or tissue extracts in addition to culture medium.
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    Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
    (Springer Nature, 2017-09) Petrov, Dmitry; Gutman, Boris A.; Yu, Shih-Hua (Julie); van Erp, Theo G.M.; Turner, Jessica A.; Schmaal, Lianne; Veltman, Dick; Wang, Lei; Alpert, Kathryn; Isaev, Dmitry; Zavaliangos-Petropulu, Artemis; Ching, Christopher R.K.; Calhoun, Vince; Glahn, David; Satterthwaite, Theodore D.; Andreasen, Ole Andreas; Borgwardt, Stefan; Howells, Fleur; Groenewold, Nynke; Voineskos, Aristotle; Radua, Joaquim; Potkin, Steven G.; Crespo-Facorro, Benedicto; Tordesillas-Gutirrez, Diana; Shen, Li; Lebedeva, Irina; Spalletta, Gianfranco; Donohoe, Gary; Kochunov, Peter; Rosa, Pedro G.P.; James, Anthony; Dannlowski, Udo; Baune, Berhard T.; Aleman, Andre; Gotlib, Ian H.; Walter, Henrik; Walter, Martin; Soares, Jair C.; Ehrlich, Stefan; Gur, Ruben C.; Doan, N. Trung; Agartz, Ingrid; Westlye, Lars T.; Harrisberger, Fabienne; Richer-Rossler, Anita; Uhlmann, Anne; Stein, Dan J.; Dickie, Erin W.; Pomarol-Clotet, Edith; Fuentes-Claramonte, Paola; Canales-Rodriguez, Erick Jorge; Salvador, Raymond; Huang, Alexander J.; Roiz-Santianez, Roberto; Cong, Shan; Tomyshev, Alexander; Piras, Fabrizio; Vecchio, Daniela; Banaj, Nerisa; Ciullo, Valentina; Hong, Elliot; Busatto, Geraldo; Zanetti, Marcus V.; Serpa, Mauricio H.; Cervenka, Simon; Kelly, Sinead; Grotegerd, Dominik; Sacchet, Matthew D.; Veer, Illya M.; Li, Meng; Wu, Mon-Ju; Irungu, Benson; Walton, Esther; Thompson, Paul M.; Medicine, School of Medicine
    As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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    mRNA Editing, Processing and Quality Control in Caenorhabditis elegans
    (Oxford University Press, 2020-07) Arribere, Joshua A.; Kuroyanagi, Hidehito; Hundley, Heather A.; Biology, School of Science
    While DNA serves as the blueprint of life, the distinct functions of each cell are determined by the dynamic expression of genes from the static genome. The amount and specific sequences of RNAs expressed in a given cell involves a number of regulated processes including RNA synthesis (transcription), processing, splicing, modification, polyadenylation, stability, translation, and degradation. As errors during mRNA production can create gene products that are deleterious to the organism, quality control mechanisms exist to survey and remove errors in mRNA expression and processing. Here, we will provide an overview of mRNA processing and quality control mechanisms that occur in Caenorhabditis elegans, with a focus on those that occur on protein-coding genes after transcription initiation. In addition, we will describe the genetic and technical approaches that have allowed studies in C. elegans to reveal important mechanistic insight into these processes.
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    Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets
    (ArXiv, 2023-11-13) Yalcinkaya, Dilek M.; Youssef, Khalid; Heydari, Bobak; Simonetti, Orlando; Dharmakumar, Rohan; Raman, Subha; Sharif, Behzad; Medicine, School of Medicine
    Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score (p < 0.001) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.
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    Titer estimation for quality control (TEQC) method: A practical approach for optimal production of protein complexes using the baculovirus expression vector system
    (Public Library of Science, 2018-04-03) Imasaki, Tsuyoshi; Wenzel, Sabine; Yamada, Kentaro; Bryant, Megan L.; Takagi, Yuichiro; Biochemistry and Molecular Biology, School of Medicine
    The baculovirus expression vector system (BEVS) is becoming the method of choice for expression of many eukaryotic proteins and protein complexes for biochemical, structural and pharmaceutical studies. Significant technological advancement has made generation of recombinant baculoviruses easy, efficient and user-friendly. However, there is a tremendous variability in the amount of proteins made using the BEVS, including different batches of virus made to express the same proteins. Yet, what influences the overall production of proteins or protein complexes remains largely unclear. Many downstream applications, particularly protein structure determination, require purification of large quantities of proteins in a repetitive manner, calling for a reliable experimental set-up to obtain proteins or protein complexes of interest consistently. During our investigation of optimizing the expression of the Mediator Head module, we discovered that the 'initial infectivity' was an excellent indicator of overall production of protein complexes. Further, we show that this initial infectivity can be mathematically described as a function of multiplicity of infection (MOI), correlating recombinant protein yield and virus titer. All these findings led us to develop the Titer Estimation for Quality Control (TEQC) method, which enables researchers to estimate initial infectivity, titer/MOI values in a simple and affordable way, and to use these values to quantitatively optimize protein expressions utilizing BEVS in a highly reproducible fashion.
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    Writing a wrong: Coupled RNA polymerase II transcription and RNA quality control
    (Wiley, 2019-07) Peck, Sarah A.; Hughes, Katlyn D.; Victorino, Jose F.; Mosley, Amber L.; Biochemistry and Molecular Biology, School of Medicine
    Processing and maturation of precursor RNA species is coupled to RNA polymerase II transcription. Co-transcriptional RNA processing helps to ensure efficient and proper capping, splicing, and 3' end processing of different RNA species to help ensure quality control of the transcriptome. Many improperly processed transcripts are not exported from the nucleus, are restricted to the site of transcription, and are in some cases degraded, which helps to limit any possibility of aberrant RNA causing harm to cellular health. These critical quality control pathways are regulated by the highly dynamic protein-protein interaction network at the site of transcription. Recent work has further revealed the extent to which the processes of transcription and RNA processing and quality control are integrated, and how critically their coupling relies upon the dynamic protein interactions that take place co-transcriptionally. This review focuses specifically on the intricate balance between 3' end processing and RNA decay during transcription termination. This article is categorized under: RNA Turnover and Surveillance > Turnover/Surveillance Mechanisms RNA Processing > 3' End Processing RNA Processing > Splicing Mechanisms RNA Processing > Capping and 5' End Modifications.