Evidence map›Paper›PMID 30104565›Full record

ArticleNature communications2018

Apparent bias toward long gene misregulation in MeCP2 syndromes disappears after controlling for baseline variations.

Ayush T Raman, Amy E Pohodich, Ying-Wooi Wan, Hari Krishna Yalamanchili, William E Lowry, Huda Y Zoghbi, Zhandong Liu

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
5.2field-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

30 citing papers in PubMed, 44 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. MeCP2 regulateseLife · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Review
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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 at 3 institutions in 1 country.

Ayush T RamanGraduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, 77030, USA.
Amy E PohodichJan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, 77030, USA.ORCID http://orcid.org/0000-0002-1802-7995
Ying-Wooi WanJan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, 77030, USA.
Hari Krishna YalamanchiliJan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, 77030, USA.
William E LowryDepartment of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Huda Y ZoghbiJan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX, 77030, USA. hzoghbi@bcm.edu.ORCID http://orcid.org/0000-0002-0700-3349
Zhandong LiuGraduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, 77030, USA. zhandong.liu@bcm.edu.ORCID http://orcid.org/0000-0002-7608-0831
Baylor College of Medicine · USHoward Hughes Medical Institute · USUniversity of California, Los Angeles · US

Funding

MOLECULAR PATHOGENESIS STUDIES OF RETT SYNDROMER01NS057819 · NINDS · BAYLOR COLLEGE OF MEDICINE · PI ZOGHBI, HUDA Y · 2006 to 2025
$7.5M
Functional Dissection of Alzheimer's Disease Networks in Drosophila: from Association to Causal Modulators of Age-Dependent NeurodegerationR01AG057339 · NIA · BAYLOR COLLEGE OF MEDICINE · PI BOTAS, JUAN, LIU, ZHANDONG · 2017 to 2021
$3.8M
Imaging Mass Spectrometry for metabolome mappingR01GM120033 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI LIU, ZHANDONG, MALETIC-SAVATIC, MIRJANA · 2017 to 2021
$2.5M
NIA NIH HHS R01 AG057339NIGMS NIH HHS R01 GM120033NINDS NIH HHS R01 NS057819U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) 5R01GM120033U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) 5R01NS057819U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) 1R01AG057339
6 · The paper itself

Abstract

Recent studies have suggested that genes longer than 100 kb are more likely to be misregulated in neurological diseases associated with synaptic dysfunction, such as autism and Rett syndrome. These length-dependent transcriptional changes are modest in MeCP2-mutant samples, but, given the low sensitivity of high-throughput transcriptome profiling technology, here we re-evaluate the statistical significance of these results. We find that the apparent length-dependent trends previously observed in MeCP2 microarray and RNA-sequencing datasets disappear after estimating baseline variability from randomized control samples. This is particularly true for genes with low fold changes. We find no bias with NanoString technology, so this long gene bias seems to be particular to polymerase chain reaction amplification-based platforms. In contrast, authentic long gene effects, such as those caused by topoisomerase inhibition, can be detected even after adjustment for baseline variability. We conclude that accurate characterization of length-dependent (or other) trends requires establishing a baseline from randomized control samples.

Indexed as

Gene Expression RegulationAnimalsBase PairingBiasDatabases, GeneticDisease Models, AnimalGene Expression ProfilingHumansMethyl-CpG-Binding Protein 2MicePrincipal Component AnalysisRett SyndromeRNA, NuclearSequence Analysis, RNASyndromeTopotecanMethyl-CpG-Binding Protein 2RNA, NuclearTopotecan

Identifiers

PMID30104565
PMCPMC6089998
OpenAlexW2885945321

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.