Evidence map›Paper›PMID 24048353›Full record

ArticleBioinformatics (Oxford, England)2013

Assessing the validity and reproducibility of genome-scale predictions.

Lauren A Sugden, Michael R Tackett, Yiannis A Savva, William A Thompson, Charles E Lawrence

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Lauren A SugdenCenter for Computational Molecular Biology and the Division of Applied Mathematics, Brown University, Providence, RI 02912, USA, St. Laurent Institute, 317 New Boston St, Woburn, MA 01801, USA and Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA.
Michael R Tackett
Yiannis A Savva
William A Thompson
Charles E Lawrence

Funding

Genetic Suppression studies of human epilepsy mutations in a model genetic systemR01NS074686 · NINDS · BROWN UNIVERSITY · PI REENAN, ROBERT A. · 2011 to 2014
$1.3M
NINDS NIH HHS NS074686NINDS NIH HHS R01 NS074686
6 · The paper itself

Abstract

motivationValidation and reproducibility of results is a central and pressing issue in genomics. Several recent embarrassing incidents involving the irreproducibility of high-profile studies have illustrated the importance of this issue and the need for rigorous methods for the assessment of reproducibility.

resultsHere, we describe an existing statistical model that is very well suited to this problem. We explain its utility for assessing the reproducibility of validation experiments, and apply it to a genome-scale study of adenosine deaminase acting on RNA (ADAR)-mediated RNA editing in Drosophila. We also introduce a statistical method for planning validation experiments that will obtain the tightest reproducibility confidence limits, which, for a fixed total number of experiments, returns the optimal number of replicates for the study. AVAILABILITY: Downloadable software and a web service for both the analysis of data from a reproducibility study and for the optimal design of these studies is provided at http://ccmbweb.ccv.brown.edu/reproducibility.html .

Indexed as

Models, StatisticalAdenosine DeaminaseAnimalsDrosophilaGenomeGenomicsReproducibility of ResultsRNA EditingSoftwareAdenosine Deaminase

Identifiers

PMID24048353
PMCPMC3810853

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.