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Zhang, T.

Publications and source records attributed to Zhang, T..

632 records · Page 36Linked to original sources

Gene expression profiling reveals U1 snRNA regulates cancer gene expression

U1 small nuclear RNA (U1 snRNA), as one of the most abundant noncoding RNA in eukaryotic cells plays an important role in splicing of pre-mRNAs. Compared to other studies which have focused on the primary function of U1 snRNA and the neurodegenerative diseases caused by the abnormalities of U1 snRNA, this study is to investigate how the U1 snRNA over-expression affects the expression of genes on a genome-wide scale. In this study, we built a model of U1 snRNA over-expression in a rat cell line. By comparing the gene expression profiles of U1 snRNA over-expressed cells with those of their controls using the microarray experiments, 916 genes or loci were identified significantly differentially expressed. These 595 up-regulated genes and 321 down-regulated genes were further analyzed using the annotations from the GO terms and the KEGG database. As a result, three of 12 enriched pathways are well-known cancer pathways, while nine of them were associated to cancers in previous studies. The further analysis of 73 genes involved in 12 pathways suggests that U1 snRNA regulates cancer gene expression. The microarray data with ID GSE84304 is available in the NCBI GEO database.

cancer biology↗

EthSEQ: ethnicity annotation from whole exome sequencing data

Whole exome sequencing (WES) is widely utilized both in translational cancer genomics studies and in the setting of precision medicine. Stratification of individual's ethnicity is fundamental for the correct interpretation of personal genomic variation impact. We implemented EthSEQ to provide reliable and rapid ethnicity annotation from whole exome sequencing individual's data and validated it on 1,000 Genome Project and TCGA data demonstrating high precision (>99%). EthSEQ can be integrated into any WES based processing pipeline and exploits multi-core capabilities. Source code, manual and other data is available at http://demichelislab.unitn.it/EthSEQ.

bioinformatics↗