Bioinformatics Analysis of Mouse Cancer Model

小鼠癌症模型的生物信息学分析

• 批准号: 7969968
• 负责人: Keiko Akagi
• 金额: $ 17.42万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7969968
• 关键词: Affect; Bioinformatics; Biological; Biological Process; Cancer Etiology; Cancer Model; Communities; Copy Number Polymorphism; Data; Data Set; Databases; Development; Disease; Gene Dosage; Genes; Genetic; Genomics; Goals; Insertional Mutagenesis; Knowledge; Malignant Neoplasms; Microarray Analysis; Modeling; Mus; Normal tissue morphology; Oncogenes; Pathway Analysis; Process; Publishing; Running; Sampling; Sequence Alignment; Sequence Analysis; Speed; Statistical Methods; Techniques; Time; Variant; anticancer research; data mining; next generation; programs; tumor; web-accessible

[1] High-throughput sequence analysis: To identify cancer-causing disease genes, we run high-throughput sequences analysis for mouse large-scale insertional mutagenesis projects. We reduced the time for the sequence alignment and cancer-gene identification processes. Recently, we developed a new pipeline program to handle short sequences from next-generation sequencing machines to enhance the speed of cancer-gene identification process. [2] Database development: We develop and maintain databases for data from microarray and sequencing projects. We published sequence data for cancer gene candidates in web-accessible databases to share the knowledge with wide cancer research community. [3] Statistical analysis of microarrays: We run statistical analysis of microarrays. We used latest statistical methods to analyze expression arrays and CGH arrays from many mouse tumor models. We identified differentially expressed genes and copy number variations. Further, we applied biological pathway analysis toward these variation and identified the affected biological functions in these samples.

[1] 高通量序列分析：为了识别致癌基因，我们对小鼠大规模插入突变项目进行了高通量序列分析。我们减少了序列比对和癌症基因识别过程的时间。 最近，我们开发了一个新的管道程序来处理来自下一代测序机的短序列，以提高癌症基因识别过程的速度。 [2] 数据库开发：我们开发和维护微阵列和测序项目数据的数据库。我们在可通过网络访问的数据库中发布了候选癌症基因的序列数据，以便与广泛的癌症研究界分享知识。 [3] 微阵列的统计分析：我们对微阵列进行统计分析。我们使用最新的统计方法来分析来自许多小鼠肿瘤模型的表达阵列和 CGH 阵列。我们鉴定了差异表达的基因和拷贝数变异。此外，我们对这些变异应用了生物途径分析，并确定了这些样本中受影响的生物功能。