BACKGROUND:Accurate and automatic gene identification in eukaryotic genomic DNA is more than ever of crucial importance to efficiently exploit the large volume of assembled genome sequences available to the community. Automatic methods have always been considered less reliable than human expertise. This is illustrated in the EGASP project, where reference annotations against which all automatic methods are measured are generated by human annotators and experimentally verified. We hypothesized that replicating the accuracy of human annotators in an automatic method could be achieved by formalizing the rules and decisions that they use, in a mathematical formalism. RESULTS:We have developed Exogean, a flexible framework based on directed acyclic colored multigraphs (DACMs) that can represent biological objects (for example, mRNA, ESTs, protein alignments, exons) and relationships between them. Graphs are analyzed to process the information according to rules that replicate those used by human annotators. Simple individual starting objects given as input to Exogean are thus combined and synthesized into complex objects such as protein coding transcripts. CONCLUSION:We show here, in the context of the EGASP project, that Exogean is currently the method that best reproduces protein coding gene annotations from human experts, in terms of identifying at least one exact coding sequence per gene. We discuss current limitations of the method and several avenues for improvement.

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背景:在真核生物基因组DNA中进行准确,自动的基因鉴定比以往任何时候都更重要,它对于有效利用社区可利用的大量组装基因组序列至关重要。一直以来,人们一直认为自动方法不如人类专业技术可靠。这在EGASP项目中得到了说明,其中由人工注释者生成了针对其测量所有自动方法的参考注释,并对其进行了实验验证。我们假设可以通过以数学形式主义形式化规则和决策来实现以自动方式复制人类注释器的准确性。
结果:我们开发了Exogean,这是一个基于有向无环彩色多图(DACM)的灵活框架,可以表示生物对象(例如,mRNA,EST,蛋白质比对,外显子)及其之间的关系。根据复制人类注释者使用的规则的规则,对图形进行分析以处理信息。因此,作为输入到Exogean的简单单个起始对象将被合并并合成为复杂的对象,例如蛋白质编码转录本。
结论:在EGASP项目的背景下,我们证明了Exogean目前是从人类专家中复制蛋白质编码基因注释的最佳方法,因为每个基因至少可以识别一个确切的编码序列。我们讨论了该方法的当前局限性和改进的几种途径。

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