Evidence map›Paper›PMID 39091757›Full record

ArticlebioRxiv : the preprint server for biology2024

Optimized reporters for multiplexed detection of transcription factor activity.

Max Trauernicht, Teodora Filipovska, Chaitanya Rastogi, Bas van Steensel

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Max TrauernichtOncode Institute, Division of Gene regulation and Division of Molecular Genetics, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands.ORCID 0000-0002-4085-4567
Teodora FilipovskaOncode Institute, Division of Gene regulation and Division of Molecular Genetics, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands.
Chaitanya RastogiDepartment of Biological Sciences, Columbia University, New York, NY, USA.ORCID 0000-0001-9155-9341
Bas van SteenselOncode Institute, Division of Gene regulation and Division of Molecular Genetics, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands.ORCID 0000-0002-0284-0404

Funding

Statistical models for fine-mapping and interpretation of schizophrenia-associated lociR01MH106842 · NIMH · NEW YORK GENOME CENTER · PI BUSSEMAKER, HARMEN J, LAPPALAINEN, TUULI · 2015 to 2024
$5.0M
NIMH NIH HHS R01 MH106842
6 · The paper itself

Abstract

In any given cell type, dozens of transcription factors (TFs) act in concert to control the activity of the genome by binding to specific DNA sequences in regulatory elements. Despite their considerable importance in determining cell identity and their pivotal role in numerous disorders, we currently lack simple tools to directly measure the activity of many TFs in parallel. Massively parallel reporter assays (MPRAs) allow the detection of TF activities in a multiplexed fashion; however, we lack basic understanding to rationally design sensitive reporters for many TFs. Here, we use an MPRA to systematically optimize transcriptional reporters for 86 TFs and evaluate the specificity of all reporters across a wide array of TF perturbation conditions. We thus identified critical TF reporter design features and obtained highly sensitive and specific reporters for 60 TFs, many of which outperform available reporters. The resulting collection of "prime" TF reporters can be used to uncover TF regulatory networks and to illuminate signaling pathways.

Indexed as

massively parallel reporter assayMPRAmultiplexed TF reporter assayreportersignaling pathwaysspecificityTFTF reporter assaytranscription factor

Identifiers

PMID39091757
PMCPMC11291157

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.