• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The Python Papers Source Codes >Bactome III: OLIgonucleotide Variable Expression Ranker (OLIVER) 1.0, Tool for Identifying Suitable Reference (Invariant) Genes from Large Microarray Datasets
【24h】

Bactome III: OLIgonucleotide Variable Expression Ranker (OLIVER) 1.0, Tool for Identifying Suitable Reference (Invariant) Genes from Large Microarray Datasets

机译：Bactome III：寡核苷酸可变表达等级（OLIVER）1.0，从大型微阵列数据集中识别合适的参考（不变）基因的工具

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Reference genes are crucial in gene expression analysis where varying gene expressions are reported as changes with respect to the expression of reference genes. Hence, reference genes are assumed to be stably expressed under most circumstances. Previous studies have shown that common algorithms for identifying of potential reference genes are not suitable for large microarray-sized datasets. Our previous work had derived methods for identification of reference genes from large microarray-sized datasets and the performances of these methods are linear to sample size. These methods had been implemented as a tool, OLIgonucleotide Variable Expression Ranker (OLIVER), which can be downloaded from http://sourceforge. net/projects/bactome/files/OLIVER/OLIVER_1.zip . This manuscript documents the implementation of OLIVER, which had been incorporated as part of Bactome project ( http://www.sf.net/projects/bactome ). These codes are licensed under GNU General Public License version 3 for academic and non-for-profit use.
机译：参考基因在基因表达分析中至关重要，在基因表达分析中，变化的基因表达被报告为相对于参考基因表达的变化。因此，假定参考基因在大多数情况下稳定表达。先前的研究表明，用于识别潜在参考基因的通用算法不适用于大型微阵列大小的数据集。我们以前的工作已经衍生出了从大型微阵列大小的数据集中识别参考基因的方法，这些方法的性能与样本量呈线性关系。这些方法已被实现为一种工具，寡核苷酸可变表达分级器（OLIVER），可以从http：// sourceforge下载。 net / projects / bactome / files / OLIVER / OLIVER_1.zip。该手稿记录了OLIVER的实现，该功能已作为Bactome项目（http://www.sf.net/projects/bactome）的一部分并入。这些代码已获得GNU通用公共许可证版本3的许可，可用于学术和非营利性用途。

著录项

相似文献

  • 外文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号