Patel_2015_BMC.Bioinformatics_16_186

Reference

Title : Using Gene Ontology to describe the role of the neurexin-neuroligin-SHANK complex in human, mouse and rat and its relevance to autism - Patel_2015_BMC.Bioinformatics_16_186
Author(s) : Patel S , Roncaglia P , Lovering RC
Ref : BMC Bioinformatics , 16 :186 , 2015
Abstract :

BACKGROUND: People with an autistic spectrum disorder (ASD) display a variety of characteristic behavioral traits, including impaired social interaction, communication difficulties and repetitive behavior. This complex neurodevelopment disorder is known to be associated with a combination of genetic and environmental factors. Neurexins and neuroligins play a key role in synaptogenesis and neurexin-neuroligin adhesion is one of several processes that have been implicated in autism spectrum disorders.
RESULTS: In this report we describe the manual annotation of a selection of gene products known to be associated with autism and/or the neurexin-neuroligin-SHANK complex and demonstrate how a focused annotation approach leads to the creation of more descriptive Gene Ontology (GO) terms, as well as an increase in both the number of gene product annotations and their granularity, thus improving the data available in the GO database.
CONCLUSIONS: The manual annotations we describe will impact on the functional analysis of a variety of future autism-relevant datasets. Comprehensive gene annotation is an essential aspect of genomic and proteomic studies, as the quality of gene annotations incorporated into statistical analysis tools affects the effective interpretation of data obtained through genome wide association studies, next generation sequencing, proteomic and transcriptomic datasets.

PubMedSearch : Patel_2015_BMC.Bioinformatics_16_186
PubMedID: 26047810

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Citations formats

Patel S, Roncaglia P, Lovering RC (2015)
Using Gene Ontology to describe the role of the neurexin-neuroligin-SHANK complex in human, mouse and rat and its relevance to autism
BMC Bioinformatics 16 :186

Patel S, Roncaglia P, Lovering RC (2015)
BMC Bioinformatics 16 :186