Evidence map›Paper›PMID 33292397›Full record

ArticleGenome biology2020

STARRPeaker: uniform processing and accurate identification of STARR-seq active regions.

Donghoon Lee, Manman Shi, Jennifer Moran, Martha Wall, Jing Zhang, Jason Liu, Dominic Fitzgerald, Yasuhiro Kyono, Lijia Ma, Kevin P White and 1 more

Abstract read
In one paragraph

Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing 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

49 citing papers in PubMed.

  1. Article
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  8. Quantifying the impact of genetic mutations on enhancer dynamics.bioRxiv : the preprint server for biology · 2025
    Article
  9. Review
  10. A genome-wide survey reveals a diverse array of enhancers coordinate thebioRxiv : the preprint server for biology · 2025
    Article
  11. Article
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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

11 authors.

Donghoon LeeDepartment of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Manman ShiInstitute for Genomics and System Biology, University of Chicago, Chicago, IL, 60637, USA.
Jennifer MoranInstitute for Genomics and System Biology, University of Chicago, Chicago, IL, 60637, USA.
Martha WallInstitute for Genomics and System Biology, University of Chicago, Chicago, IL, 60637, USA.
Jing ZhangSchool of Information and Computer Sciences, University of California, Irvine, CA, 92697, USA.
Jason LiuProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
Dominic FitzgeraldInstitute for Genomics and System Biology, University of Chicago, Chicago, IL, 60637, USA.
Yasuhiro KyonoInstitute for Genomics and System Biology, University of Chicago, Chicago, IL, 60637, USA.
Lijia MaInstitute for Genomics and System Biology, University of Chicago, Chicago, IL, 60637, USA.
Kevin P WhiteInstitute for Genomics and System Biology, University of Chicago, Chicago, IL, 60637, USA. kevin@tempus.com.
Mark GersteinProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA. mark@gersteinlab.org.ORCID http://orcid.org/0000-0002-9746-3719

Funding

EDAC: ENCODE Data Analysis CenterU24HG009446 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GERSTEIN, MARK BENDER, WENG, ZHIPING · 2017 to 2022
$10.4M
Center for Functional Validation and Evaluation of ENCODE Enhancer Regions, Grant Number 5UM1HG009426-03UM1HG009426 · NHGRI · UNIVERSITY OF CHICAGO · PI MORAN, JENNIFER ROBIN · 2017 to 2021
$3.1M
A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disordersK01MH123896 · NIMH · UNIVERSITY OF CALIFORNIA-IRVINE · PI ZHANG, JING · 2020 to 2023
$431k
NHGRI NIH HHS U24 HG009446NHGRI NIH HHS UM1 HG009426NIMH NIH HHS K01 MH123896
6 · The paper itself

Abstract

STARR-seq technology has employed progressively more complex genomic libraries and increased sequencing depths. An issue with the increased complexity and depth is that the coverage in STARR-seq experiments is non-uniform, overdispersed, and often confounded by sequencing biases, such as GC content. Furthermore, STARR-seq readout is confounded by RNA secondary structure and thermodynamic stability. To address these potential confounders, we developed a negative binomial regression framework for uniformly processing STARR-seq data, called STARRPeaker. Moreover, to aid our effort, we generated whole-genome STARR-seq data from the HepG2 and K562 human cell lines and applied STARRPeaker to comprehensively and unbiasedly call enhancers in them.

Indexed as

SoftwareEnhancer Elements, GeneticGenomic LibraryHep G2 CellsHigh-Throughput Nucleotide SequencingHumansK562 CellsMalePromoter Regions, GeneticSequence Analysis, DNA

Identifiers

PMID33292397
PMCPMC7722316

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.