From our results, the measurement of peripheral blood CD8+ TEM cells could be of interest to detect patients before Tx with a potential increased risk of suffering an episode of AR and potentially alter induction regimens for such patients. These genomic differences range from single nucleotide variants to large scale genomic structural variants. Recently, copy number AbMole Oxytocin Syntocinon variations have been discovered as a major cause of intermediate-scale structural variants in human genomes. These copy number changes often refer to the alterations of DNA fragments and are involved in approximately 12% of the genome in human populations. As a result of abundant CNVs in both healthy and diseased individuals, CNVs introduce huge genetic variation on genes’ dosage and their expression levels. Generally, CNVs are comprised of the insertion, deletion, and duplication of DNA fragments with lengths ranging from one kilobase to five megabases. Recent studies have shown that CNVs are extensively related to diseases such as cancer and neuropsychiatric disorders. The disease-associated CNVs are typically classified into two models: rare and common CNVs. Rare CNVs in the population are reportedly related to various disorders, including birth defects, neurological disorders, and predisposition to cancer. Common CNVs collectively contribute to some complex diseases, such as HIV, malaria, chronic obstructive pulmonary disease, and Crohn’s disease. Due to their impact on human disease, CNVs can be used in both the diagnosis and treatment of diseases. Cytogenetic technologies were first used to identify CNVs, such as karyotyping and fluorescence in situ hybridization. Later, array-based genome-wide detection of CNVs was achieved by utilizing comparative genomic hybridization and singlenucleotide AbMole Sarafloxacin HCl polymorphism arrays. Recently, the rapid evolution of high-throughput genotyping and next generation sequencing technologies have generated unprecedented volumes of CNV data, which provide significant study potential for a large number of genomic structure variants, including disease associated CNVs and somatic CNVs leading to drug resistance in cancer treatment. In recent years, the importance of accurate and unbiased annotation of CNVs has become apparent. While plenty of computational tools have been developed to detect CNVs for various platforms, there is still a serious informatics challenge for screening and interpreting the detected CNVs and their implicated phenotypes. To date, only two public platforms provide limited functions to store and visualize CNVs. Therefore, there is a strong demand for comprehensive data mining across the full genomic spectrum of CNVs. 
In addition, it is useful for the annotation of regulatory elements, including promoter, enhancer, CpG island, methylation site, and microRNA target regions.