GigaAssay – An adaptable high-throughput saturation mutagenesis assay platform

Benjamin, Ronald; Giacoletto, Christopher J.; FitzHugh, Zachary T.; Eames, Danielle; Buczek, Lindsay; Wu, Xiaogang; Newsome, Jacklyn; Han, Mira V.; Pearson, Tony; Wei, Zhi; Banerjee, Atoshi; Brown, Lancer; Valente, Liz J.; Shen, Shirley; Deng, Hong‐Wen; Schiller, Martin

doi:10.1016/j.ygeno.2022.110439

Cited by 9 publications

(32 citation statements)

References 63 publications

(74 reference statements)

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“…Mutants that had low activity (<50%) were classified as LOF, and mutants with >50% activity were classified as WT, This figure can be compared to a similar figure for LentiX293T/LTR-GFP cells reported in Benjamin et al. [1] . …”

Section: Data Descriptionmentioning

confidence: 64%

“…All methods to produce the data are published in Benjamin et al . [1] . Additional details for the bioinformatics methods are provided.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Tat mutant activities in the GigaAssay (see data file 1)were compared to 164 known mutants reported in the literature (see data file 2). We also compared the activities of GigaAssay results to the activity of Tat nonsense mutants measured in the GigaAssay and the known activities of these truncation (see data file 3) [1] . The GigaAssay was further validated by comparing the measured activities against mutants that were independently measured and blinded prior to the GigaAssay experiment.…”

Section: Data Descriptionmentioning

confidence: 99%

“… The data in this article are associated with the research paper “GigaAssay – an adaptable high-throughput saturation mutagenesis assay” [1] . The raw data are sequence reads of HIV-1 Tat cDNA amplified from cellular genomic DNA in a new single-pot saturation mutagenesis assay designated the “GigaAssay”.…”

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confidence: 99%

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Data supporting a saturation mutagenesis assay for Tat-driven transcription with the GigaAssay

et al. 2022

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Section: Data Descriptionmentioning

confidence: 64%

“…All methods to produce the data are published in Benjamin et al . [1] . Additional details for the bioinformatics methods are provided.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

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confidence: 99%

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Data supporting a saturation mutagenesis assay for Tat-driven transcription with the GigaAssay

et al. 2022

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“…Flow cytometry, high content screening, and the GigaAssay all meet the throughput qualification (n > 1,000 samples), and have accuracies >90%, thus are close to becoming HAT assays. [17,23,[48][49][50][51][52] Flow cytometry is a high throughput approach that separates cell based on a fluorescent signal in which 10,000s of single cells can be measured and sorted rapidly. However, even under optimal conditions the best achievable accuracies are ∼92%.…”

Section: Discussionmentioning

confidence: 99%

The history and conceptual framework of assays and screens

Giacoletto

Schiller

2023

BioEssays

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Since the 16th century, assays and screens have been essential for scientific investigation. However, most methods could be significantly improved, especially in accuracy, scalability, and often lack adequate comparisons to negative controls. There is a lack of consistency in distinguishing assays, in which accuracy is the main goal, from screens, in which scalability is prioritized over accuracy. We dissected and modernized the original definitions of assays and screens based upon recent developments and the conceptual framework of the original definitions. All methods have three components: design/measurement, performance, and interpretation. We propose a model of method development in which reproducible observations become new methods, initially assessed by sensitivity. Further development can proceed along a path to either screens or assays. The screen path focuses on scalability first, but can later prioritize analysis of negatives. Alternatively, the assay path first compares results to negative controls, assessing specificity and accuracy, later adding scalability. Both pathways converge on a high-accuracy and throughput (HAT) assay, like next generation sequencing, which we suggest should be the ultimate goal of all testing methods. Our model will help scientists better select among available methods, as well as improve existing methods, expanding their impact on science.

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