Evidence map›Paper›PMID 34018332›Full record

ArticleMolecular systems biology2021

A method for benchmarking genetic screens reveals a predominant mitochondrial bias.

Mahfuzur Rahman, Maximilian Billmann, Michael Costanzo, Michael Aregger, Amy H Y Tong, Katherine Chan, Henry N Ward, Kevin R Brown, Brenda J Andrews, Charles Boone and 2 more

Open access · goldAbstract read
In one paragraph

Article in Molecular systems biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

17 citing papers in PubMed, 23 citations in OpenAlex.

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  5. mtKO: A dedicated guide RNA library for mitochondria research.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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

12 authors at 2 institutions in 2 countries.

Mahfuzur RahmanDepartment of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.ORCID 0000-0002-9226-3988
Maximilian BillmannDepartment of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.ORCID 0000-0002-6556-9594
Michael CostanzoDonnelly Centre, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-7906-2604
Michael AreggerDonnelly Centre, University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-6443-9870
Amy H Y TongDonnelly Centre, University of Toronto, Toronto, ON, Canada.
Katherine ChanDonnelly Centre, University of Toronto, Toronto, ON, Canada.
Henry N WardBioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN, USA.ORCID 0000-0002-9869-9790
Kevin R BrownDonnelly Centre, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-5514-2538
Brenda J AndrewsDonnelly Centre, University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-6427-6493
Charles BooneDonnelly Centre, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-3542-6760
Jason MoffatDonnelly Centre, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-5663-8586
Chad L MyersDepartment of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.ORCID 0000-0002-1026-5972
University of Toronto · CAUniversity of Minnesota · US

Funding

Mapping the reference genetic network of a eukaryotic cellR01HG005853 · NHGRI · UNIVERSITY OF TORONTO · PI ANDREWS, BRENDA JEAN, BOONE, CHARLES · 2010 to 2025
$7.6M
Methods for large-scale analysis of chemical-genetic interactionsR01HG005084 · NHGRI · UNIVERSITY OF MINNESOTA · PI MYERS, CHAD L · 2010 to 2022
$2.9M
Integrative methods for finding genetic interactions associated with cancer riskR21CA235352 · NCI · UNIVERSITY OF MINNESOTA · PI MYERS, CHAD L · 2019 to 2020
$355k
CIHR MOP-142375NCI NIH HHS R21 CA235352NHGRI NIH HHS R01 HG005084NHGRI NIH HHS R01 HG005853
6 · The paper itself

Abstract

We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome-wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene-pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria-associated signal within co-essentiality networks derived from these data and explore the basis of this signal. Our analysis and time-resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them.

Indexed as

Gene Regulatory NetworksAlgorithmsBenchmarkingBiasCell LineCRISPR-Cas SystemsGenetic TestingHEK293 CellsHumansMitochondriaSystems Biologycomputational evaluationCRISPR screenselectron transport chain

Identifiers

PMID34018332
PMCPMC8138267
OpenAlexW3161985476

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.