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Informative group testing for multiplex assays.

ABSTRACT: Infectious disease testing frequently takes advantage of two tools-group testing and multiplex assays-to make testing timely and cost effective. Until the work of Tebbs et al. (2013) and Hou et al. (2017), there was no research available to understand how best to apply these tools simultaneously. This recent work focused on applications where each individual is considered to be identical in terms of the probability of disease. However, risk-factor information, such as past behavior and presence of symptoms, is very often available on each individual to allow one to estimate individual-specific probabilities. The purpose of our paper is to propose the first group testing algorithms for multiplex assays that take advantage of individual risk-factor information as expressed by these probabilities. We show that our methods significantly reduce the number of tests required while preserving accuracy. Throughout this paper, we focus on applying our methods with the Aptima Combo 2 Assay that is used worldwide for chlamydia and gonorrhea screening.

SUBMITTER: Bilder CR

PROVIDER: S-EPMC7959482 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Informative group testing for multiplex assays.

Bilder Christopher R CR Tebbs Joshua M JM McMahan Christopher S CS

Biometrics 20190328 1

Infectious disease testing frequently takes advantage of two tools-group testing and multiplex assays-to make testing timely and cost effective. Until the work of Tebbs et al. (2013) and Hou et al. (2017), there was no research available to understand how best to apply these tools simultaneously. This recent work focused on applications where each individual is considered to be identical in terms of the probability of disease. However, risk-factor information, such as past behavior and presence ...[more]

PMID: 30353548

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Project description:Analysis of p53 binding sites using multiplex enhancer reporter assays, ChIP-seq data and RNA-seq data. Transcription factors establish and maintain the specific transcriptome of a cell by binding to genomic regulatory regions, thereby regulating the transcription of their target genes. Like many transcription factors, the DNA sequence-specific binding preferences of p53 are known. However, it remains largely unclear what distinguishes functional enhancers from other bound genomic regions that have no regulatory activity. In addition, the genome is scattered with seemingly perfect recognition sequences that remain unoccupied. To disentangle the rules of genome-wide p53 binding, we employed two complementary techniques of multiplex enhancer-reporter assays, one using barcoded reporters and the other using enhancer self-transcription. We compared the activity of more than one thousand candidate p53 enhancers under loss and gain of p53 conditions and identified several hundred high-confidence p53-responsive enhancers. Strikingly, the large majority (99%) of these target enhancers can be characterized and distinguished from negative sequences by the occurrence of a single p53 binding site. By training a machine learning classifier on these data, and integrating the resulting genome-wide predictions with fifteen publicly available human p53 ChIP-seq data sets, we identified a consensus set of 1148 functional p53 binding sites in the human genome. Unexpectedly, this direct p53 cistrome is invariably used between cell types and experimental conditions, while differences between experiments can be largely attributed to indirect non-functional binding. Our data suggest that direct p53 enhancers function in a context-independent manner and do not contain obvious combinatorial complexity of binding sites for multiple transcription factors. They represent a class of unsophisticated cell-autonomous enhancers with a single binding site, distinct from complex developmental enhancers that integrate signals from multiple transcription factors. This suggests that context-dependent regulation of p53 target genes is not encoded in the p53 enhancer, but at different upstream or downstream layers of the cell's gene regulatory network.

Dataset Information

Informative group testing for multiplex assays.

Publications

Informative group testing for multiplex assays.

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