Evidence map›Paper›PMID 30799854›Full record

ArticleJournal of visualized experiments : JoVE2019

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences.

Shaleen Shrestha, Xing Liu, Clarissa Stephanie Santoso, Juan Ignacio Fuxman Bass

Abstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Shaleen ShresthaDepartment of Biology, Boston University.
Xing LiuDepartment of Biology, Boston University.
Clarissa Stephanie SantosoDepartment of Biology, Boston University.
Juan Ignacio Fuxman BassDepartment of Biology, Boston University; Bioinformatics Program, Boston University; fuxman@bu.edu.
Boston University · US

Funding

Structure and Function of Immune Gene Regulatory NetworksR35GM128625 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Juan Ignacio Fuxman Bass · 2018 to 2026
$4.0M
NIGMS NIH HHS R35 GM128625
6 · The paper itself

Abstract

Identifying the sets of transcription factors (TFs) that regulate each human gene is a daunting task that requires integrating numerous experimental and computational approaches. One such method is the yeast one-hybrid (Y1H) assay, in which interactions between TFs and DNA regions are tested in the milieu of the yeast nucleus using reporter genes. Y1H assays involve two components: a 'DNA-bait' (e.g., promoters, enhancers, silencers, etc.) and a 'TF-prey,' which can be screened for reporter gene activation. Most published protocols for performing Y1H screens are based on transforming TF-prey libraries or arrays into DNA-bait yeast strains. Here, we describe a pipeline, called enhanced Y1H (eY1H) assays, where TF-DNA interactions are interrogated by mating DNA-bait strains with an arrayed collection of TF-prey strains using a high density array (HDA) robotic platform that allows screening in a 1,536 colony format. This allows for a dramatic increase in throughput (60 DNA-bait sequences against >1,000 TFs takes two weeks per researcher) and reproducibility. We illustrate the different types of expected results by testing human promoter sequences against an array of 1,086 human TFs, as well as examples of issues that can arise during screens and how to troubleshoot them.

Indexed as

Base SequenceHumansTranscription FactorsTwo-Hybrid System TechniquesTranscription Factors

Identifiers

PMID30799854
PMCPMC6601638
OpenAlexW2911686940

What OpenQuestion holds

Textmetadata
LicenceTDM
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.