Evidence map›Paper›PMID 32425512›Full record

ArticleBioinformatics and biology insights2020

Discovering Selected Antibodies From Deep-Sequenced Phage-Display Antibody Library Using ATTILA.

Andréa Queiroz Maranhão, Heidi Muniz Silva, Waldeyr Mendes Cordeiro da Silva, Renato Kaylan Alves França, Thais Canassa De Leo, Marcelo Dias-Baruffi, Rafael Trindade Burtet, Marcelo Macedo Brigido

Open access · goldAbstract read
In one paragraph

Article in Bioinformatics and biology insights, 2020. 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
0.9field-weighted citation impact, top 23% of its field
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, 10 citations in OpenAlex.

  1. Review
  2. Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2024
    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

8 authors at 2 institutions in 1 country.

Andréa Queiroz MaranhãoDepartment of Cellular Biology, Institute of Biological Science, University of Brasília, Brasília, Brazil.
Heidi Muniz SilvaDepartment of Cellular Biology, Institute of Biological Science, University of Brasília, Brasília, Brazil.
Waldeyr Mendes Cordeiro da SilvaDepartment of Cellular Biology, Institute of Biological Science, University of Brasília, Brasília, Brazil.ORCID https://orcid.org/0000-0002-8660-6331
Renato Kaylan Alves FrançaDepartment of Cellular Biology, Institute of Biological Science, University of Brasília, Brasília, Brazil.
Thais Canassa De LeoSchool of Pharmaceutical Sciences of Ribeirão Preto, USP, Ribeirão Preto, Brazil.
Marcelo Dias-BaruffiSchool of Pharmaceutical Sciences of Ribeirão Preto, USP, Ribeirão Preto, Brazil.
Rafael Trindade BurtetDepartment of Cellular Biology, Institute of Biological Science, University of Brasília, Brasília, Brazil.
Marcelo Macedo BrigidoDepartment of Cellular Biology, Institute of Biological Science, University of Brasília, Brasília, Brazil.
Universidade de Brasília · BRUniversidade de Ribeirão Preto · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phage display is a powerful technique to select high-affinity antibodies for different purposes, including biopharmaceuticals. Next-generation sequencing (NGS) presented itself as a robust solution, making it possible to assess billions of sequences of the variable domains from selected sublibraries. Handling this process, a central difficulty is to find the selected clones. Here, we present the AutomaTed Tool For Immunoglobulin Analysis (ATTILA), a new tool to analyze and find the enriched variable domains throughout a biopanning experiment. The ATTILA is a workflow that combines publicly available tools and in-house programs and scripts to find the fold-change frequency of deeply sequenced amplicons generated from selected VH and VL domains. We analyzed the same human Fab library NGS data using ATTILA in 5 different experiments, as well as on 2 biopanning experiments regarding performance, accuracy, and output. These analyses proved to be suitable to assess library variability and to list the more enriched variable domains, as ATTILA provides a report with the amino acid sequence of each identified domain, along with its complementarity-determining regions (CDRs), germline classification, and fold change. Finally, the methods employed here demonstrated a suitable manner to combine amplicon generation and NGS data analysis to discover new monoclonal antibodies (mAbs).

Indexed as

antibody variable domainsbiopanningcombinatorial librarynext-generation sequencingPhage display

Identifiers

PMID32425512
PMCPMC7218273
OpenAlexW3022674773

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.