Genie -: Gene finding in Drosophila melanogaster
Genie -: Gene finding in Drosophila melanogaster
复制标题
DOI：
10.1101/gr.10.4.529
复制
发表时间：
2000-04-01
期刊：
影响因子：
7
通讯作者：
Haussler, D
中科院分区：
文献类型：
--
作者：
Reese, MG;Kulp, D;Haussler, D
作为基因组注释评估计划（GASP）的一部分，一个基于隐马尔可夫模型的基因发现系统Genie被应用于果蝇基因组Adh区域。提交了三个版本的Genie基因发现系统的预测，一个基于编码基因的统计特性，第二个包括EST比对信息，第三个整合了蛋白质序列同源性信息。所有三个程序都在所提供的果蝇训练数据上训练。此外，提交了来自集成神经网络的启动子分配。在222个注释基因中，基因分配重叠>90%，预测了26个可能的新基因，其中一些可能是过度预测。该系统正确地确定了70%的外显子在cDNA确认的基因和77%的外显子与EST序列比对的外显子边界。三个Genie提交的最好的预测了注释的43个基因结构中的19个完全正确（44%）。在启动子类别中，只能检测到30%的转录起始位点，但通过将该程序作为传感器集成到Genie中，假阳性率可以降至1/16，786（0.006%）。对长连续基因组序列的实验结果揭示了Genie中基因组装的一些问题。研究结果用于改进系统。我们表明，精灵是一个强大的隐马尔可夫模型系统，允许从不同的来源，如信号传感器（剪接位点，起始密码子等），内容传感器（外显子，内含子，基因间）和对齐的mRNA，EST和肽序列的信息的广义整合。评估表明，Genie可以有效地用于注释来自高等生物的完整基因组。
A hidden Markov model-based gene-finding system called Genie Was applied to the genomic Adh region in Drosophila melanogaster as a part of the Genome Annotation Assessment Project (GASP). Predictions from three versions of the Genie gene-Finding system were submitted, one based on statistical properties of coding genes, a second included EST alignment information, and a third that integrated protein sequence homology information. Ail three programs were trained on the provided Drosophila training data. In addition, promoter assignments from an integrated neural network were submitted. The gene assignments overlapped >90% of the 222 annotated genes and 26 possibly novel genes were predicted, of which some might be overpredictions. The system correctly identified the exon boundaries of 70% of the exons in cDNA-confirmed genes and 77% of the exons with the addition of EST sequence alignments. The best of the three Genie submissions predicted 19 of the annotated 43 gene structures entirely correct (44%). In the promoter category, only 30% of the transcription start sites could be detected, but by integrating this program as a sensor into Genie the false-positive rate could be dropped to 1/16,786 (0.006%). The results of the experiment on the long contiguous genomic sequence revealed some problems concerning gene assembly in Genie. The results were used to improve the system. We show that Genie is a robust hidden Markov model system that allows for a generalized integration of information from different sources such as signal sensors (splice sites, start codon, etc), content sensors (exons, introns, intergenic) and alignments of mRNA, EST, and peptide sequences. The assessment showed that Genie could effectively be used For the annotation of complete genomes From higher organisms.