The dynamic features of gene expression are crucial for cancer studies. To access precise causal effects of disease related networks, integration of additional time-series data is needed. Single nucleotide variants as a major cause of cancer are well-studied using advanced NGS technology. More functional SNVs are identified according to the preferentially expressed feature of functional variants. To date, there is no temporal dynamic data for variant allele expression. Generating time-series variant allele expression data and integration of temporal gene expression data and variant allele expression data are in demand. Here we provide an approach to access such data for the first time. We introduce a data pre-processing and analysis pipeline using in-house scripts, MCL and MetaCore. We implement the whole pipeline on melanoma cell line data. Possible biological interpretations are presented.
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|Commitee:||Zhang, Qianqian, Spurr, Liam|
|School:||The George Washington University|
|Department:||Molecular Biochemistry & Bioinformatics|
|School Location:||United States -- District of Columbia|
|Source:||MAI 81/4(E), Masters Abstracts International|
|Keywords:||gene expression, MCL, melanoma, SNV, temporal allele expression, time course|
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