Evidence map›Paper›PMID 24128763›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2013

Multiplatform single-sample estimates of transcriptional activation.

Stephen R Piccolo, Michelle R Withers, Owen E Francis, Andrea H Bild, W Evan Johnson

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 1 pooled it
–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

51 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  12. A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes.Proceedings of the National Academy of Sciences of the United States of America · 2020
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  20. Ambient Pollution-related Reprogramming of the Human Small Airway Epithelial Transcriptome.American journal of respiratory and critical care medicine · 2018
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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

5 authors.

Stephen R PiccoloDepartments of Pharmacology and Toxicology, and Oncological Sciences, The University of Utah, Salt Lake City, UT 84112.
Michelle R Withers
Owen E Francis
Andrea H Bild
W Evan Johnson

Funding

MULTIDISCIPLINARY CANCER RESEARCH TRAINING PROGRAMT32CA093247 · NCI · UNIVERSITY OF UTAH · PI AYER, DONALD E · 2002 to 2011
$5.2M
Integrative signaling models to decipher complex cancer phenotypesU01CA164720 · NCI · UNIVERSITY OF UTAH · PI BILD, ANDREA HOPE, GRAY, JOE W. · 2012 to 2016
$2.9M
Epigenetic changes and phenotype-specific therapeutic strategies in breast cancerR01GM085601 · NIGMS · UNIVERSITY OF UTAH · PI BILD, ANDREA HOPE, JEFFREY, STEFANIE S. · 2008 to 2011
$1.7M
Statistical Tools and Methods for Next-Generation Sequencing in EpigeneticsR01HG005692 · NHGRI · BRIGHAM YOUNG UNIVERSITY · PI JOHNSON, WILLIAM EVAN · 2010 to 2014
$1.7M
NCI NIH HHS 1U01CA164720NCI NIH HHS 5T32CA093247NCI NIH HHS T32 CA093247NCI NIH HHS U01 CA164720NHGRI NIH HHS 1R01HG005692NHGRI NIH HHS R01 HG005692NIGMS NIH HHS R01 GM085601NIGMS NIH HHS R01GM085601
6 · The paper itself

Abstract

Over the past two decades, many biotechnology platforms have been developed for high-throughput gene expression profiling. However, because each platform is subject to technology-specific biases and produces distinct raw-data distributions, researchers have experienced difficulty in integrating data across platforms. Data integration is crucial to data-generating consortiums, researchers transitioning to newer profiling technologies, and individuals seeking to aggregate data across experiments. We address this need with our Universal exPression Code (UPC) approach, which corrects for platform-specific background noise using models that account for the genomic base composition and length of target regions; this approach also uses a mixture model to estimate whether a gene is active in a particular profiling sample. The latter produces standardized UPC values on a zero-to-one scale, so that they can be interpreted consistently, irrespective of profiling technology, thus enabling downstream analysis pipelines to be developed in a platform-agnostic manner. The UPC method can be applied to one- and two-channel expression microarrays and to next-generation sequencing data (RNA sequencing). Furthermore, UPCs are derived using information from within a given sample only--no ancillary samples are required at processing time. Thus, UPCs are suitable for personalized-medicine workflows where samples must be processed individually rather than in batches. In a variety of analyses and comparisons, UPCs perform comparably to other methods designed specifically for microarrays or RNA sequencing in most settings. Software for calculating UPCs is freely available at www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html.

Indexed as

AlgorithmsModels, GeneticSoftwareBase CompositionDNA Barcoding, TaxonomicGene Expression ProfilingGenesTranscriptional Activation

Identifiers

PMID24128763
PMCPMC3816418

What OpenQuestion holds

Textmetadata
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.