Evidence map›Paper›PMID 32376972›Full record

ArticleNPJ systems biology and applications2020

Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants.

Jenna E Gallegos, Neil R Adames, Mark F Rogers, Pavel Kraikivski, Aubrey Ibele, Kevin Nurzynski-Loth, Eric Kudlow, T M Murali, John J Tyson, Jean Peccoud

Open access · goldAbstract read
In one paragraph

Article in NPJ systems biology and applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.2field-weighted citation impact, top 51% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. The Involvement ofBiology · 2024
    Article
  4. Article
  5. Review
  6. APC/CmicroPublication biology · 2022
    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

10 authors at 4 institutions in 1 country.

Jenna E Gallegos *Colorado State University, Chemical and Biological Engineering, Fort Collins, CO, USA.
Neil R Adames *Colorado State University, Chemical and Biological Engineering, Fort Collins, CO, USA.
Mark F RogersGenoFAB, Inc., Fort Collins, CO, USA.
Pavel KraikivskiVirginia Tech, Academy of Integrated Sciences, Blacksburg, VA, USA.
Aubrey IbeleColorado State University, Chemical and Biological Engineering, Fort Collins, CO, USA.
Kevin Nurzynski-LothColorado State University, Chemical and Biological Engineering, Fort Collins, CO, USA.
Eric KudlowColorado State University, Chemical and Biological Engineering, Fort Collins, CO, USA.
T M MuraliVirginia Tech, Computer Science, Blacksburg, VA, USA.
John J TysonVirginia Tech, Biological Sciences, Blacksburg, VA, USA.ORCID http://orcid.org/0000-0001-7560-6013
Jean PeccoudColorado State University, Chemical and Biological Engineering, Fort Collins, CO, USA. jean.peccoud@colostate.edu.
Colorado State University · USVirginia Tech · USChinese Culture Center of San Francisco · USDean College · US

Funding

Stochastic Models of Cell Cycle Regulation in EukaryotesR01GM078989 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI PECCOUD, JEAN M, TYSON, JOHN J. · 2006 to 2018
$5.3M
NIGMS NIH HHS R01 GM078989
6 · The paper itself

Abstract

Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the 630 genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.

Indexed as

Cell CycleCell DivisionComputational BiologyEpistasis, GeneticGene Expression Regulation, FungalGene Regulatory NetworksHigh-Throughput Screening AssaysModels, TheoreticalSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSaccharomyces cerevisiae Proteins

Identifiers

PMID32376972
PMCPMC7203125
OpenAlexW3020948995

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.