ArticleMethods in molecular biology (Clifton, N.J.)2021
μIVC-Seq: A Method for Ultrahigh-Throughput Development and Functional Characterization of Small RNAs.
Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For a long time, artificial RNAs have been developed by in vitro selection methodologies like Systematic Evolution of Ligands by EXponential enrichment (SELEX). Yet, even though this technology is extremely powerful to isolate specific and high-affinity binders, it is less suited for the isolation of RNAs optimized for more complex functions such as fluorescence emission or multiple-turnover catalysis. Whereas such RNAs should ideally be developed by screening approaches, conventional microtiter plate assays become rapidly cost-prohibitive. However, the advent of droplet-based microfluidics recently enabled us to devise microfluidic-assisted In Vitro Compartmentalization (μIVC), a strongly miniaturized and highly parallelized screening technology allowing to functionally screen millions of mutants in a single day while using a very low amount of reagent. Used in combination with high-throughput sequencing, the resulting μIVC-seq pipeline described in this chapter now allows rapid and semiautomated screening to be performed at low cost and in an ultrahigh-throughput regime.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.