Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project.

Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project.
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将统计测量数据导入 Artemis 可以增强利什曼原虫基因组计划中的基因识别。

DOI:
10.1186/1471-2105-4-23
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发表时间:
2003-06-07
期刊:
影响因子:
3
通讯作者:
Myler PJ
Myler PJ
中科院分区:
生物学4区
文献类型:
--
作者:
Aggarwal G;Worthey EA;McDonagh PD;Myler PJ

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西雅图生物医学研究所(SBRI)作为利什曼原虫基因组网络(LGN)的一部分,正在对主要利什曼原虫的锥虫原虫物种进行染色体测序。在SBRI,染色体序列的注释使用训练和未训练的非共识基因预测算法与ARTEMIS(一个具有丰富和用户友好界面的注释平台)的组合进行。在这里,我们描述了一种方法,用于将三种不同的蛋白质编码基因预测算法(GLIMMER, TESTCODE和GENESCAN)的结果导入ARTEMIS序列查看器和注释工具。将这些方法与内置在ARTEMIS中的CODONUSAGE算法进行比较,可以看出组合方法对更准确地注释L. major基因组序列的重要性。通过将广泛使用的算法中的数据导入现有的注释平台,开发了一种简易而强大的基因预测工具。这种方法在利什曼原虫基因组计划中尤其有效,因为那里有很大比例的新基因需要手工注释。
Seattle Biomedical Research Institute (SBRI) as part of the Leishmania Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species Leishmania major. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with ARTEMIS, an annotation platform with rich and user-friendly interfaces. Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (GLIMMER, TESTCODE and GENESCAN) into the ARTEMIS sequence viewer and annotation tool. Comparison of these methods, along with the CODONUSAGE algorithm built into ARTEMIS, shows the importance of combining methods to more accurately annotate the L. major genomic sequence. An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the Leishmania genome project where there is large proportion of novel genes requiring manual annotation.
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发表时间: 1982-01-01
影响因子: 14.9
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