Evidence map›Paper›PMID 21034472›Full record

ArticleBMC medical genomics2010

Genotator: a disease-agnostic tool for genetic annotation of disease.

Dennis P Wall, Rimma Pivovarov, Mark Tong, Jae-Yoon Jung, Vincent A Fusaro, Todd F DeLuca, Peter J Tonellato

Abstract read
In one paragraph

Article in BMC medical genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
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  3. RareNeurology. Genetics · 2018
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  15. Identification of key regulators for the migration and invasion of rheumatoid synoviocytes through a systems approach.Proceedings of the National Academy of Sciences of the United States of America · 2014
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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.

Dennis P WallCenter for Biomedical informatics, Harvard Medical School, Boston, MA 02115, USA. dpwall@hms.harvard.edu
Rimma Pivovarov
Mark Tong
Jae-Yoon Jung
Vincent A Fusaro
Todd F DeLuca
Peter J Tonellato

Funding

Characterizing the genetic systems of autism through multi-disease analysisR01MH090611 · NIMH · STANFORD UNIVERSITY · PI WALL, DENNIS PAUL · 2010 to 2014
$2.8M
Method for Prediction of Efficacy of Genetic-Based Prediction Models of PersonaliR01LM010130 · NLM · HARVARD MEDICAL SCHOOL · PI TONELLATO, PETER J. · 2009 to 2010
$967k
NIMH NIH HHS 1R01MH090611-01A1NLM NIH HHS R01 LM010130
6 · The paper itself

Abstract

backgroundDisease-specific genetic information has been increasing at rapid rates as a consequence of recent improvements and massive cost reductions in sequencing technologies. Numerous systems designed to capture and organize this mounting sea of genetic data have emerged, but these resources differ dramatically in their disease coverage and genetic depth. With few exceptions, researchers must manually search a variety of sites to assemble a complete set of genetic evidence for a particular disease of interest, a process that is both time-consuming and error-prone.

methodsWe designed a real-time aggregation tool that provides both comprehensive coverage and reliable gene-to-disease rankings for any disease. Our tool, called Genotator, automatically integrates data from 11 externally accessible clinical genetics resources and uses these data in a straightforward formula to rank genes in order of disease relevance. We tested the accuracy of coverage of Genotator in three separate diseases for which there exist specialty curated databases, Autism Spectrum Disorder, Parkinson's Disease, and Alzheimer Disease. Genotator is freely available at http://genotator.hms.harvard.edu.

resultsGenotator demonstrated that most of the 11 selected databases contain unique information about the genetic composition of disease, with 2514 genes found in only one of the 11 databases. These findings confirm that the integration of these databases provides a more complete picture than would be possible from any one database alone. Genotator successfully identified at least 75% of the top ranked genes for all three of our use cases, including a 90% concordance with the top 40 ranked candidates for Alzheimer Disease.

conclusionsAs a meta-query engine, Genotator provides high coverage of both historical genetic research as well as recent advances in the genetic understanding of specific diseases. As such, Genotator provides a real-time aggregation of ranked data that remains current with the pace of research in the disease fields. Genotator's algorithm appropriately transforms query terms to match the input requirements of each targeted databases and accurately resolves named synonyms to ensure full coverage of the genetic results with official nomenclature. Genotator generates an excel-style output that is consistent across disease queries and readily importable to other applications.

Indexed as

AlgorithmsAlzheimer DiseaseChildChild Development Disorders, PervasiveDiseaseHumansInternetMaleMolecular Sequence AnnotationParkinson DiseaseReproducibility of Results

Identifiers

PMID21034472
PMCPMC2990725

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LicenceCC BY
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Registered trials

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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.