Functional Analysis of Autism Candidate Genes Based on Comparative Genomics Analysis
In the post-genomics era, the rapid development of high-throughput technology makes data analysis become more and more important which could obtain some new biomedical knowledge, especially in understanding disease mechanism. In this work, we analyze the candidate genes related to autism by comparative genomics in two samples. We try to understand the autism disease pathology from molecular mechanism. We first select the confirmed autism susceptibility genes acting as positive sample, and the genes from biomedical literature related to autism by text mining technology acting as the unknown sample. By venn diagram analysis, the results obtain 25 autism susceptibility genes from the unknown sample. The results achieve some significant biomedical knowledge in the comparative functional analysis to the two samples, In GO analysis, we obtain that the two class of genes have some similar molecular functions including all kinds of binding functions. In the pathway analysis, VEGF signaling pathway and MAPK signaling pathway have significant enrichment about the two samples. The result also shows some genes between the two samples play a key role in the same signaling transduction pathway. It indicates that the functional analysis is helpful for candidate gene related to autism. This provides a way to study the disease from molecular mechanism.
KeywordsBioinformatics Biostatistics Genomics Functional analysis Data acquisition
This research is supported by the National Natural Science Foundation of China (Grant Nos: 61502243, 61502247, 61272084, 61300240, 61572263, 61502251, 61503195), Natural Science Foundation of the Jiangsu Province (Grant Nos: BK20130417, BK20150863, BK20140895, and BK20140875), China Postdoctoral Science Foundation (Grant No. 2016M590483), Jiangsu Province postdoctoral Science Foundation (Grant No. 1501072B), Scientific and Technological Support Project (Society) of Jiangsu Province (Grant No. BE2016776), Nanjing University of Posts and Telecommunications’ Science Foundation (Grant Nos: NY214068 and NY213088). This work is also supported in part by Zhejiang Engineering Research Center of Intelligent Medicine under 2016E10011.
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