Abstract
The first step in understanding the molecular biology of an inherited disease is to identify which gene or genes are carrying variants. This process starts with locating the mutations in a chromosomal band, as narrow as possible, and follows with the manual analysis of all the genes mapping in this region. Usually this is not an easy task, but it can be facilitated by complementary computational approaches that evaluate all genes in a region of interest. We present here a method that combines literature mining, gene annotations, and sequence homology searches to prioritize candidate genes involved in a given genetic disorder. The method progresses in two steps. Firstly, we compute associations of molecular and phenotypic features as taken from MEDLINE. Secondly, for a disease with a given phenotype and linked to a chromosomal region, sequence homology based searches are carried on the chromosomal region to identify potential candidates that are scored using the precomputed associations. The scoring of associations between biological concepts using links across databases can be extended to other databases in Molecular Biology and to nondisease phenotypes.
Keywords
- Monogenic Disease
- RefSeq Gene
- Sequence Homology Search
- Prioritize Candidate Gene
- International Human Genome Sequencing Consortium
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2006 Landes Bioscience and Springer Science+Business Media
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Perez-Iratxeta, C., Bork, P., Andrade, M.A. (2006). Literature and Genome Data Mining for Prioritizing Disease-Associated Genes. In: Discovering Biomolecular Mechanisms with Computational Biology. Molecular Biology Intelligence Unit. Springer, Boston, MA. https://doi.org/10.1007/0-387-36747-0_6
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DOI: https://doi.org/10.1007/0-387-36747-0_6
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-34527-7
Online ISBN: 978-0-387-36747-7
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