Evidence map›Paper›PMID 21880147›Full record

ArticleBMC bioinformatics2011

An integrative approach to ortholog prediction for disease-focused and other functional studies.

Yanhui Hu, Ian Flockhart, Arunachalam Vinayagam, Clemens Bergwitz, Bonnie Berger, Norbert Perrimon, Stephanie E Mohr

Abstract read
In one paragraph

Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 579 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
579citing papers in PubMed, 1 pooled it
–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

579 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  18. Developmental patterning of adipose tissue bybioRxiv : the preprint server for biology · 2026
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519 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

7 authors.

Yanhui HuDrosophila RNAi Screening Center, Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA.
Ian Flockhart
Arunachalam Vinayagam
Clemens Bergwitz
Bonnie Berger
Norbert Perrimon
Stephanie E Mohr

Funding

New collections of dsRNAs and overexpression miRNA constructs to supplement RNAiR01GM067761 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI PERRIMON, NORBERT · 2003 to 2019
$15.5M
Network analysis of Signal TransductionR01DK088718 · NIDDK · HARVARD MEDICAL SCHOOL · PI PERRIMON, NORBERT · 2010 to 2014
$2.5M
Role of the renal sodium-phosphate co-transporter NaPi-IIc in phosphate homeostasK08DK078361 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI BERGWITZ, CLEMENS · 2007 to 2011
$741k
NIDDK NIH HHS 5K08DK78361NIDDK NIH HHS R01 DK088718NIGMS NIH HHS R01 GM067761
6 · The paper itself

Abstract

backgroundMapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward.

resultsWe report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist).

conclusionsDIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

Indexed as

Disease Models, AnimalAnimalsDatabases, GeneticDiseaseEvolution, MolecularGenetic Predisposition to DiseaseGenome-Wide Association StudyHumans

Identifiers

PMID21880147
PMCPMC3179972

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.