Evidence map›Paper›PMID 29069589›Full record

ArticleCell reports2017

Understanding Tissue-Specific Gene Regulation.

Abhijeet Rajendra Sonawane, John Platig, Maud Fagny, Cho-Yi Chen, Joseph Nathaniel Paulson, Camila Miranda Lopes-Ramos, Dawn Lisa DeMeo, John Quackenbush, Kimberly Glass, Marieke Lydia Kuijjer

Open access · goldAbstract read
In one paragraph

Article in Cell reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 263 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
263citing papers in PubMed, 3 pooled it
14.3field-weighted citation impact, top 1% 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

263 citing papers in PubMed, 3 syntheses or guidelines pooled it, 457 citations in OpenAlex.

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203 more citing papers are in PubMed but not listed here.

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

10 authors at 2 institutions in 1 country.

Abhijeet Rajendra SonawaneChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
John PlatigDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Maud FagnyDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Cho-Yi ChenDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Joseph Nathaniel PaulsonDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Camila Miranda Lopes-RamosDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Dawn Lisa DeMeoChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
John QuackenbushChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Kimberly GlassChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Electronic address: kimberly.glass@channing.harvard.edu.
Marieke Lydia KuijjerDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Electronic address: mkuijjer@jimmy.harvard.edu.
Harvard University · USDana-Farber Cancer Institute · US

Funding

VectorP30CA006516 · NCI · DANA-FARBER CANCER INSTITUTE · PI A. Heather Eliassen · 1985 to 2026
$330.6M
Targeting the Vasular SystemP50CA165962 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI Benjamin Harris Kann · 2013 to 2026
$33.5M
Tissue and Pathology ResourcesP50CA127003 · NCI · DANA-FARBER CANCER INST · PI EL-BARDEESY, NABEEL, SELLERS, WILLIAM R · 2007 to 2023
$33.2M
Respiratory Computational Discovery CoreP01HL114501 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI CHOI, MARY E · 2013 to 2025
$24.9M
Systems Biology, Bioinformatics, and BiostatisticsP01HL105339 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2011 to 2015
$12.2M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Chemical Inhibitors to Define an Essential M. Tuberculosis Signaling NetworkR01AI099204 · NIAID · BOSTON CHILDREN'S HOSPITAL · PI HUSSON, ROBERT N · 2012 to 2016
$6.1M
Using Networks to Assign Gene Function in Lung DiseaseR01HL111759 · NHLBI · DANA-FARBER CANCER INST · PI QUACKENBUSH, JOHN, SILVERMAN, EDWIN K · 2012 to 2015
$3.4M
Genotype and Imaging Phenotype Biomarkers in Lung CancerU01CA190234 · NCI · DANA-FARBER CANCER INST · PI AERTS, HUGO, QUACKENBUSH, JOHN · 2015 to 2019
$3.3M
Using Integrative Networks to Explore Heterogeneous Phenotypes in COPDK25HL133599 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI GLASS, KIMBERLY RENEE · 2016 to 2020
$945k
NCI NIH HHS P30 CA006516NCI NIH HHS P50 CA127003NCI NIH HHS P50 CA165962NCI NIH HHS R35 CA197449NCI NIH HHS U01 CA190234NHLBI NIH HHS K25 HL133599NHLBI NIH HHS P01 HL105339NHLBI NIH HHS P01 HL114501NHLBI NIH HHS R01 HL111759NIAID NIH HHS R01 AI099204
6 · The paper itself

Abstract

Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

Indexed as

Gene Regulatory NetworksTranscriptional ActivationGenome, HumanHumansOrgan SpecificityProtein Interaction MapsTranscription FactorsTranscriptomeTranscription Factorsgene expressiongene regulationGTExnetwork biologynetwork medicineregulatory networkstissue specificitytranscriptional regulationtranscription factorstranscriptome

Identifiers

PMID29069589
PMCPMC5828531
OpenAlexW2949475756

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.