ArticleNucleic acids research2014
Manteia, a predictive data mining system for vertebrate genes and its applications to human genetic diseases.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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.
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Who cites it
21 citing papers in PubMed, 30 citations in OpenAlex.
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- Molecular mechanisms underlying nuchal hump formation in dolphin cichlid, Cyrtocara moorii.Scientific reports · 2019Article
- Heterogeneous Ribosomes Preferentially Translate Distinct Subpools of mRNAs Genome-wide.Molecular cell · 2017Article
- New data and features for advanced data mining in Manteia.Nucleic acids research · 2017Article
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- Phosphoproteome and Transcriptome of RA-Responsive and RA-Resistant Breast Cancer Cell Lines.PloS one · 2016Article
- Applications of comparative evolution to human disease genetics.Current opinion in genetics & development · 2015Review
- Differentiation of pluripotent stem cells to muscle fiber to model Duchenne muscular dystrophy.Nature biotechnology · 2015Article
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Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
The function of genes is often evolutionarily conserved, and comparing the annotation of ortholog genes in different model organisms has proved to be a powerful predictive tool to identify the function of human genes. Here, we describe Manteia, a resource available online at http://manteia.igbmc.fr. Manteia allows the comparison of embryological, expression, molecular and etiological data from human, mouse, chicken and zebrafish simultaneously to identify new functional and structural correlations and gene-disease associations. Manteia is particularly useful for the analysis of gene lists produced by high-throughput techniques such as microarrays or proteomics. Data can be easily analyzed statistically to characterize the function of groups of genes and to correlate the different aspects of their annotation. Sophisticated querying tools provide unlimited ways to merge the information contained in Manteia along with the possibility of introducing custom user-designed biological questions into the system. This allows for example to connect all the animal experimental results and annotations to the human genome, and take advantage of data not available for human to look for candidate genes responsible for genetic disorders. Here, we demonstrate the predictive and analytical power of the system to predict candidate genes responsible for human genetic diseases.
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Registered trials
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.