Evidence map›Paper›PMID 35314663›Full record

ArticleMethods and protocols2022

Genomic Fishing and Data Processing for Molecular Evolution Research.

Héctor Lorente-Martínez, Ainhoa Agorreta, Diego San Mauro

Abstract read
In one paragraph

Article in Methods and protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
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

3 authors.

Héctor Lorente-MartínezDepartment of Biodiversity, Ecology and Evolution, Complutense University of Madrid, 28040 Madrid, Spain.ORCID 0000-0003-0232-5370
Ainhoa AgorretaDepartment of Biodiversity, Ecology and Evolution, Complutense University of Madrid, 28040 Madrid, Spain.ORCID 0000-0002-8940-7717
Diego San MauroDepartment of Biodiversity, Ecology and Evolution, Complutense University of Madrid, 28040 Madrid, Spain.ORCID 0000-0002-2214-1125

Funding

Ministerio de Ciencia e Innovación (Spain) PID2020-115481GB-I00
6 · The paper itself

Abstract

Molecular evolution analyses, such as detection of adaptive/purifying selection or ancestral protein reconstruction, typically require three inputs for a target gene (or gene family) in a particular group of organisms: sequence alignment, model of evolution, and phylogenetic tree. While modern advances in high-throughput sequencing techniques have led to rapid accumulation of genomic-scale data in public repositories and databases, mining such vast amount of information often remains a challenging enterprise. Here, we describe a comprehensive, versatile workflow aimed at the preparation of genome-extracted datasets readily available for molecular evolution research. The workflow involves: (1) fishing (searching and capturing) specific gene sequences of interest from taxonomically diverse genomic data available in databases at variable levels of annotation, (2) processing and depuration of retrieved sequences, (3) production of a multiple sequence alignment, (4) selection of best-fit model of evolution, and (5) solid reconstruction of a phylogenetic tree.

Indexed as

blast searchdata mininggene familygenomicshigh-throughput sequencingmolecular evolutionphylogenysequence alignment

Identifiers

PMID35314663
PMCPMC8938851

What OpenQuestion holds

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Registered trials

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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.