Evidence map›Paper›PMID 38148337›Full record

ArticleNature communications2023

ACIDES: on-line monitoring of forward genetic screens for protein engineering.

Takahiro Nemoto, Tommaso Ocari, Arthur Planul, Muge Tekinsoy, Emilia A Zin, Deniz Dalkara, Ulisse Ferrari

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Takahiro NemotoInstitut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France. nemoto.takahiro.prime@osaka-u.ac.jp.ORCID 0000-0003-2981-4035
Tommaso OcariInstitut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France.
Arthur PlanulInstitut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France.
Muge TekinsoyInstitut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France.
Emilia A ZinInstitut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France.ORCID 0000-0002-1275-3697
Deniz DalkaraInstitut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France. deniz.dalkara@inserm.fr.ORCID 0000-0003-4112-9321
Ulisse FerrariInstitut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012, Paris, France. ulisse.ferrari@inserm.fr.ORCID 0000-0002-3131-5537

Funding

Agence Nationale de la Recherche (French National Research Agency) ANR-10-LABX-65Agence Nationale de la Recherche (French National Research Agency) ANR-18-IAHU-01EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) REGENETHER 639888EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) 863214MEXT | Japan Society for the Promotion of Science (JSPS) 22K17994MEXT | Japan Society for the Promotion of Science (JSPS) WPI-PRIMe
6 · The paper itself

Abstract

Forward genetic screens of mutated variants are a versatile strategy for protein engineering and investigation, which has been successfully applied to various studies like directed evolution (DE) and deep mutational scanning (DMS). While next-generation sequencing can track millions of variants during the screening rounds, the vast and noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in the library selection process by attributing accurate statistical scores to individual variants. We tested ACIDES first on a random-peptide-insertion experiment and then on multiple public datasets from DE and DMS studies. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering and research pipelines in a range of applications, including gene therapy.

Indexed as

High-Throughput Nucleotide SequencingProtein EngineeringComputer SimulationMutation

Identifiers

PMID38148337
PMCPMC10751290

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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.