Evidence map›Paper›PMID 41618720›Full record

ReviewBiological reviews of the Cambridge Philosophical Society2026

The power of many: when genetics met yeasts and high-throughput.

Víctor A Tallada, Víctor Carranco

Abstract readReview
In one paragraph

Review in Biological reviews of the Cambridge Philosophical Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Víctor A TalladaCentro Andaluz de Biología del Desarrollo, Universidad Pablo de Olavide - Consejo Superior de Investigaciones Científicas - Junta de Andalucía, Carretera de Utrera Km1, Seville, ES-41013, Spain.ORCID 0000-0001-9526-5957
Víctor CarrancoCentro Andaluz de Biología del Desarrollo, Universidad Pablo de Olavide - Consejo Superior de Investigaciones Científicas - Junta de Andalucía, Carretera de Utrera Km1, Seville, ES-41013, Spain.

Funding

Funding for open access publishing: Universidad Pablo de Olavide/CBUAUniversidad Pablo de Olavide PPI A-1 2407
6 · The paper itself

Abstract

In recent years, complex technological capabilities have evolved, driven by the need to solve complex and integrative biological questions through global analyses. New equipment allows the scaling up and automation of processes which previously were carried out on a very limited scale. Concomitant with the availability of sophisticated technology developed to increase experimental processes in parallel, it is essential to have a versatile biological model that allows us to generate and handle large collections of genetic variants with the greatest possible capacity. This is a critical aspect in order to approach saturation levels in each search, to minimise false-positive and false-negative rates. In this sense, yeasts represent a prototypical eukaryotic model with a remarkable genome editing potential: it is possible to generate and phenotype several thousand genetically diverse strains on a high-throughput scale. Here we review, with a focus on the most recent years, the combination of the power of yeasts with the technical advances in multi-level processing capability. This has resulted in unprecedented global mappings of a multitude of biological processes with important implications that reshape our knowledge of fundamental biology, evolution and biotechnological/biomedical applications.

Indexed as

Genetic EngineeringYeastsHigh-Throughput Nucleotide Sequencingadaptationbiotech productiondirected evolutiondisease modelsdrug discoveryhigh‐throughput screenssystems biologyyeast

Identifiers

PMID41618720
PMCPMC13671188

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.