Evidence map›Paper›PMID 27402361›Full record

ArticleG3 (Bethesda, Md.)2016

Sources of Error in Mammalian Genetic Screens.

Laura Magill Sack, Teresa Davoli, Qikai Xu, Mamie Z Li, Stephen J Elledge

Open access · goldAbstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed
5.7field-weighted citation impact, top 4% 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

58 citing papers in PubMed, 88 citations in OpenAlex.

  1. Review
  2. The diverse functions of viral microproteins.bioRxiv : the preprint server for biology · 2026
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  9. A unified genetic perturbation language for human cellular programming.bioRxiv : the preprint server for biology · 2025
    Article
  10. Article
  11. Article
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  13. Article
  14. Article
  15. Review
  16. Article
  17. Decoding RNA Metabolism by RNA-linked CRISPR Screening in Human Cells.bioRxiv : the preprint server for biology · 2024
    Article
  18. Article
  19. Article
  20. 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

5 authors at 2 institutions in 1 country.

Laura Magill SackDepartment of Genetics, Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts 02115 Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts 02115.
Teresa DavoliDepartment of Genetics, Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts 02115 Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts 02115.
Qikai XuDepartment of Genetics, Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts 02115 Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts 02115.
Mamie Z LiDepartment of Genetics, Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts 02115 Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts 02115.
Stephen J ElledgeDepartment of Genetics, Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts 02115 Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts 02115 selledge@genetics.med.harvard.edu.
Harvard University · USHoward Hughes Medical Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic screens are invaluable tools for dissection of biological phenomena. Optimization of such screens to enhance discovery of candidate genes and minimize false positives is thus a critical aim. Here, we report several sources of error common to pooled genetic screening techniques used in mammalian cell culture systems, and demonstrate methods to eliminate these errors. We find that reverse transcriptase-mediated recombination during retroviral replication can lead to uncoupling of molecular tags, such as DNA barcodes (BCs), from their associated library elements, leading to chimeric proviral genomes in which BCs are paired to incorrect ORFs, shRNAs, etc This effect depends on the length of homologous sequence between unique elements, and can be minimized with careful vector design. Furthermore, we report that residual plasmid DNA from viral packaging procedures can contaminate transduced cells. These plasmids serve as additional copies of the PCR template during library amplification, resulting in substantial inaccuracies in measurement of initial reference populations for screen normalization. The overabundance of template in some samples causes an imbalance between PCR cycles of contaminated and uncontaminated samples, which results in a systematic artifactual depletion of GC-rich library elements. Elimination of contaminating plasmid DNA using the bacterial endonuclease Benzonase can restore faithful measurements of template abundance and minimize GC bias.

Indexed as

AnimalsCell Culture TechniquesDNA Barcoding, TaxonomicGenetic TestingGenetic VectorsGenomeMammalsPlasmidsPolymerase Chain ReactionRNA, Small InterferingRNA, Small Interferingbarcode screeningGC biasgenetic librarieslentiviruspooled shRNA or CRISPR screens

Identifiers

PMID27402361
PMCPMC5015935
OpenAlexW2485511343

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.