Evidence map›Paper›PMID 33718886›Full record

ArticleSTAR protocols2021

An optimized protocol for rapid, sensitive and robust on-bead ChIP-seq from primary cells.

Lorane Texari, Nathanael J Spann, Ty D Troutman, Mashito Sakai, Jason S Seidman, Sven Heinz

Abstract read
In one paragraph

Article in STAR protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Article
  2. Article
  3. The Disordered Region of ASXL1 Acts as an Auto-Regulator Through Condensation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. The Wild West of spike-in normalization.Nature biotechnology · 2024
    Article
  15. Article
  16. Article
  17. 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

6 authors.

Lorane TexariDepartment of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
Nathanael J SpannDepartment of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
Ty D TroutmanDepartment of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
Mashito SakaiDepartment of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
Jason S SeidmanDepartment of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
Sven HeinzDepartment of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
VirologyP30AI036214 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Maile Ann Young Karris · 1994 to 2026
$78.4M
Transgenic & Knock-out MouseP30DK063491 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PETER J TONTONOZ · 2003 to 2026
$40.4M
Contemporary Approaches to Cancer Cell Signaling and CommunicationT32CA009523 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DONOGHUE, DANIEL J, YANG, JING · 1985 to 2025
$12.1M
San Diego Digestive Diseases Research CenterP30DK120515 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LARS ECKMANN, Bernd G. Schnabl · 2019 to 2026
$10.8M
EXPERIMENTAL ENDOCRINOLOGY AND METABOLISMT32DK007044 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Amit Majithia · 1986 to 2026
$4.6M
MOLECULAR BIOLOGICAL APPROACHES TO ENDOCRINOLOGYT32DK007541 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GAULTON, KYLE JEFFRIE, MAJITHIA, AMIT · 1987 to 2022
$4.5M
How transcription disrupts genome 3D organizationR01GM129523 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HEINZ, SVEN W · 2020 to 2023
$1.3M
NCATS NIH HHS UL1 TR001442NCI NIH HHS T32 CA009523NIAID NIH HHS P30 AI036214NIDDK NIH HHS P30 DK063491NIDDK NIH HHS P30 DK120515NIDDK NIH HHS T32 DK007044NIDDK NIH HHS T32 DK007541NIGMS NIH HHS R01 GM129523
6 · The paper itself

Abstract

Integrative analysis of next-generation sequencing data can help understand disease mechanisms. Specifically, ChIP-seq can illuminate where transcription regulators bind to regulate transcription. A major obstacle to performing this assay on primary cells is the low numbers obtained from tissues. The extensively validated ChIP-seq protocol presented here uses small volumes and single-pot on-bead library preparation to generate diverse high-quality ChIP-seq data. This protocol allows for medium-to-high-throughput ChIP-seq of low-abundance cells and can also be applied to other mammalian cells. For complete details on the use and execution of this protocol, please refer to Brigidi et al. (2019), Carlin et al. (2018), Heinz et al. (2018), Nott et al. (2019), Sakai et al. (2019), and Seidman et al. (2020).

Indexed as

Chromatin Immunoprecipitation SequencingAnimalsCells, CulturedHumansMiceChIP-seqGenomicsMolecular biologySequencing

Identifiers

PMID33718886
PMCPMC7921621

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.