Evidence map›Paper›PMID 17038185›Full record

ArticleBMC genomics2006

Integration of curated databases to identify genotype-phenotype associations.

Chern-Sing Goh, Tara A Gianoulis, Yang Liu, Jianrong Li, Alberto Paccanaro, Yves A Lussier, Mark Gerstein

Abstract read
In one paragraph

Article in BMC genomics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
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  3. Comparative Genomics ofFrontiers in plant science · 2017
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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.

Chern-Sing GohMolecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA. chernsing.goh@gmail.com
Tara A Gianoulis
Yang Liu
Jianrong Li
Alberto Paccanaro
Yves A Lussier
Mark Gerstein

Funding

Semantic Approaches to Phenotypic Databases AnalysesK22LM008308 · NLM · UNIVERSITY OF CHICAGO · PI LUSSIER, YVES A · 2004 to 2006
$468k
NLM NIH HHS K22 LM008308
6 · The paper itself

Abstract

backgroundThe ability to rapidly characterize an unknown microorganism is critical in both responding to infectious disease and biodefense. To do this, we need some way of anticipating an organism's phenotype based on the molecules encoded by its genome. However, the link between molecular composition (i.e. genotype) and phenotype for microbes is not obvious. While there have been several studies that address this challenge, none have yet proposed a large-scale method integrating curated biological information. Here we utilize a systematic approach to discover genotype-phenotype associations that combines phenotypic information from a biomedical informatics database, GIDEON, with the molecular information contained in National Center for Biotechnology Information's Clusters of Orthologous Groups database (NCBI COGs).

resultsIntegrating the information in the two databases, we are able to correlate the presence or absence of a given protein in a microbe with its phenotype as measured by certain morphological characteristics or survival in a particular growth media. With a 0.8 correlation score threshold, 66% of the associations found were confirmed by the literature and at a 0.9 correlation threshold, 86% were positively verified.

conclusionOur results suggest possible phenotypic manifestations for proteins biochemically associated with sugar metabolism and electron transport. Moreover, we believe our approach can be extended to linking pathogenic phenotypes with functionally related proteins.

Indexed as

Databases, GeneticComputational BiologyGenes, BacterialGenomicsGenotypePhenotypeReproducibility of Results

Identifiers

PMID17038185
PMCPMC1630430

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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.