首页> 外文期刊>Current Protein and Peptide Science >Small Open Reading Frames: Current Prediction Techniques and Future Prospect
【24h】

Small Open Reading Frames: Current Prediction Techniques and Future Prospect

机译:小型开放阅读框:当前的预测技术和未来前景

获取原文
获取原文并翻译 | 示例
           

摘要

Evidence is accumulating that small open reading frames (sORF, <100 codons) play key roles in many important biological processes. Yet, they are generally ignored in gene annotation despite they are far more abundant than the genes with more than 100 codons. Here, we demonstrate that popular homolog search and codon-index techniques perform poorly for small genes relative to that for larger genes, while a method dedicated to sORF discovery has a similar level of accuracy as homology search. The result is largely due to the small dataset of experimentally verified sORF available for homology search and for training ab initio techniques. It highlights the urgent need for both experimental and computational studies in order to further advance the accuracy of sORF prediction.
机译:越来越多的证据表明,小的开放阅读框(sORF,<100个密码子)在许多重要的生物学过程中起着关键作用。然而,尽管它们比具有100个以上密码子的基因丰富得多,但通常在基因注释中被忽略。在这里,我们证明了流行的同源物搜索和密码子索引技术相对于较大基因而言,对于小基因而言表现较差,而专用于sORF发现的方法的准确性与同源物搜索相似。结果很大程度上归因于可用于同源搜索和训练从头算起的经实验验证的sORF的小型数据集。它强调了对实验和计算研究的迫切需求,以进一步提高sORF预测的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 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