In one paragraphArticle in PLoS computational biology, 2021. 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, top 98% of its field
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
10 authors at 3 institutions in 2 countries.
Grace Hui Ting YeoComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-8920-8208 Oscar JuezDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
Qing ChenDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
Budhaditya BanerjeeDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
Lendy ChuDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
Max W ShenComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-4226-1861 Ive LogisterHubrecht Institute, Utrecht, the Netherlands.
Richard I SherwoodDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0001-7427-7713 David K GiffordComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.ORCID 0000-0003-1709-4034 Brigham and Women's Hospital · USMassachusetts Institute of Technology · USHubrecht Institute for Developmental Biology and Stem Cell Research · NL
Funding
Graduate Training in Computational and Systems BiologyT32GM087237 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BURGE, CHRISTOPHER B · 2009 to 2023
$4.6MHigh-Throughput Native Context Mapping and Modeling of Regulatory DNAR01HG008754 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GIFFORD, DAVID K · 2016 to 2019
$2.7MHigh-throughput methods for elucidating the control of chromatin accessibilityR01HG008363 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GIFFORD, DAVID K · 2015 to 2017
$2.3MPredictive transcription factor modeling to program endodermal cell fatesK01DK101684 · NIDDK · BRIGHAM AND WOMEN'S HOSPITAL · PI SHERWOOD, RICHARD I · 2014 to 2018
$670kSystematic unlocking of airway stem cell differentiation programR21OD025309 · OD · BRIGHAM AND WOMEN'S HOSPITAL · PI SHERWOOD, RICHARD I · 2019 to 2020
$462kNHGRI NIH HHS R01 HG008363NHGRI NIH HHS R01 HG008754NIDDK NIH HHS K01 DK101684NIGMS NIH HHS T32 GM087237NIH HHS R21 OD025309
6 · The paper itselfAbstract
We introduce poly-adenine CRISPR gRNA-based single-cell RNA-sequencing (pAC-Seq), a method that enables the direct observation of guide RNAs (gRNAs) in scRNA-seq. We use pAC-Seq to assess the phenotypic consequences of CRISPR/Cas9 based alterations of gene cis-regulatory regions. We show that pAC-Seq is able to detect cis-regulatory-induced alteration of target gene expression even when biallelic loss of target gene expression occurs in only ~5% of cells. This low rate of biallelic loss significantly increases the number of cells required to detect the consequences of changes to the regulatory genome, but can be ameliorated by transcript-targeted sequencing. Based on our experimental results we model the power to detect regulatory genome induced transcriptomic effects based on the rate of mono/biallelic loss, baseline gene expression, and the number of cells per target gRNA.
Indexed as
AlgorithmsAnimalsClustered Regularly Interspaced Short Palindromic RepeatsComputational BiologyCRISPR-Cas SystemsDatabases, FactualHumansMiceRegulatory Elements, TranscriptionalRNA, Guide, CRISPR-Cas SystemsSequence Analysis, RNASingle-Cell AnalysisTranscriptomeRNA, Guide, CRISPR-Cas Systems
Identifiers
PMID33711017
PMCPMC8011753
OpenAlexW3135612183
What OpenQuestion holds
Textmetadata
LicenceCC BY
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