Evidence map›Paper›PMID 25172479›Full record

ReviewInternational review of neurobiology2014

RNA-Seq reveals novel transcriptional reorganization in human alcoholic brain.

Sean P Farris, R Dayne Mayfield

Open access · greenAbstract readReview
In one paragraph

Review in International review of neurobiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
7.4field-weighted citation impact, top 4% 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

42 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. FACTORS CONTRIBUTING TO THE ESCALATION OF ALCOHOL CONSUMPTION.Neuroscience and biobehavioral reviews · 2022
    Review
  11. Noncoding RNA therapeutics for substance use disorder.Advances in drug and alcohol research · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  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

2 authors at 1 institution in 1 country.

Sean P FarrisWaggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712.
R Dayne MayfieldWaggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, TX 78712. Electronic address: dayne.mayfield@austin.utexas.edu.
The University of Texas at Austin · US

Funding

GENE EXPRESSION IN THE HUMAN ALCOHOLIC BRAINR01AA012404 · NIAAA · UNIVERSITY OF TEXAS AUSTIN · PI MAYFIELD, ROY DAYNE · 2000 to 2025
$10.7M
Neurochemical & Behavioral Correlates of ETOH EffectsT32AA007471 · NIAAA · UNIVERSITY OF TEXAS AUSTIN · PI Kimberly Nixon · 1987 to 2026
$9.2M
Brain Tissue Resource Centre for Alcohol ResearchR28AA012725 · NIAAA · UNIVERSITY OF SYDNEY · PI Greg Trevor Sutherland · 2012 to 2026
$7.0M
Next Generation Sequencing of Human Alcoholic BrainU01AA020926 · NIAAA · UNIVERSITY OF TEXAS AT AUSTIN · PI Roy DAYNE MAYFIELD · 2011 to 2026
$6.7M
Novel molecular and cellular approaches for alcoholism medication developmentP01AA020683 · NIAAA · UNIVERSITY OF TEXAS AT AUSTIN · PI PONOMAREV, IGOR · 2012 to 2016
$3.3M
Next-gen sequencing: searching for mechanisms of alcohol and nicotine dependenceRC2AA019382 · NIAAA · UNIVERSITY OF TEXAS AT AUSTIN · PI MAYFIELD, R. DAYNE · 2009 to 2010
$1.7M
NIAAA NIH HHS P01 AA020683NIAAA NIH HHS R01 AA012404NIAAA NIH HHS R28 AA012725NIAAA NIH HHS RC2 AA019382NIAAA NIH HHS T32 AA007471NIAAA NIH HHS U01 AA020926
6 · The paper itself

Abstract

DNA microarrays have been used for over a decade to profile gene expression on a genomic scale. While this technology has advanced our understanding of complex cellular function, the reliance of microarrays on hybridization kinetics results in several technical limitations. For example, knowledge of the sequences being probed is required, distinguishing similar sequences is difficult because of cross-hybridization, and the relatively narrow dynamic range of the signal limits sensitivity. Recently, new technologies have been introduced that are based on novel sequencing methodologies. These next-generation sequencing methods do not have the limitations inherent to microarrays. Next-generation sequencing is unique since it allows the detection of all known and novel RNAs present in biological samples without bias toward known transcripts. In addition, the expression of coding and noncoding RNAs, alternative splicing events, and expressed single nucleotide polymorphisms (SNPs) can be identified in a single experiment. Furthermore, this technology allows for remarkably higher throughput while lowering sequencing costs. This significant shift in throughput and pricing makes low-cost access to whole genomes possible and more importantly expands sequencing applications far beyond traditional uses (Morozova & Marra, 2008) to include sequencing the transcriptome (RNA-Seq), providing detail on gene structure, alternative splicing events, expressed SNPs, and transcript size (Mane et al., 2009; Tang et al., 2009; Walter et al., 2009), in a single experiment, while also quantifying the absolute abundance of genes, all with greater sensitivity and dynamic range than the competing cDNA microarray technology (Mortazavi, Williams, McCue, Schaeffer, & Wold, 2008).

Indexed as

High-Throughput Nucleotide SequencingAlcoholismBrainGenomeHumansTranscriptomeAlcohol dependenceAlcoholismPostmortem brain tissueRNA-SeqRNA sequencingTranscriptomeWeighted gene coexpression network analysis

Identifiers

PMID25172479
PMCPMC4267562
OpenAlexW1867589655

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.