Evidence map›Paper›PMID 26510397›Full record

ArticleScientific reports2015

A high-throughput RNA-seq approach to profile transcriptional responses.

G A Moyerbrailean, G O Davis, C T Harvey, D Watza, X Wen, R Pique-Regi, F Luca

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast.Proceedings of the National Academy of Sciences of the United States of America · 2024
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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

7 authors.

G A MoyerbraileanWayne State University, Center for Molecular Medicine and Genetics, Detroit, 48201, USA.
G O DavisWayne State University, Center for Molecular Medicine and Genetics, Detroit, 48201, USA.
C T HarveyWayne State University, Center for Molecular Medicine and Genetics, Detroit, 48201, USA.
D WatzaWayne State University, Center for Molecular Medicine and Genetics, Detroit, 48201, USA.
X WenUniversity of Michigan, Department of Biostatistics, Ann Arbor, postcode, USA.
R Pique-RegiWayne State University, Center for Molecular Medicine and Genetics, Detroit, 48201, USA.
F LucaWayne State University, Center for Molecular Medicine and Genetics, Detroit, 48201, USA.

Funding

Functional Characterization of the Genetic and Environmental Determinants of Complex TraitsR01GM109215 · NIGMS · WAYNE STATE UNIVERSITY · PI Francesca Luca, Roger Pique-Regi · 2014 to 2026
$3.2M
NIGMS NIH HHS 5R01GM109215NIGMS NIH HHS R01 GM109215
6 · The paper itself

Abstract

In recent years RNA-seq protocols have been developed to investigate a variety of biological problems by measuring the abundance of different RNAs. Many study designs involve performing expensive preliminary studies to screen or optimize experimental conditions. Testing a large number of conditions in parallel may be more cost effective. For example, analyzing tissue/environment-specific gene expression generally implies screening a large number of cellular conditions and samples, without prior knowledge of which conditions are most informative (e.g., some cell types may not respond to certain treatments). To circumvent these challenges, we have established a new two-step high-throughput RNA-seq approach: the first step consists of gene expression screening of a large number of conditions, while the second step focuses on deep sequencing of the most relevant conditions (e.g., largest number of differentially expressed genes). This study design allows for a fast and economical screen in step one, with a more efficient allocation of resources for the deep sequencing of the most biologically relevant libraries in step two. We have applied this approach to study the response to 23 treatments in three lymphoblastoid cell lines demonstrating that it should also be useful for other high-throughput transcriptome profiling applications requiring iterative refinement or screening.

Indexed as

Cell Line, TumorGene Expression ProfilingGene Expression RegulationHigh-Throughput Nucleotide SequencingHumans

Identifiers

PMID26510397
PMCPMC4625130

What OpenQuestion holds

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Registered trials

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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.