The current growth in DNA sequencing techniques makes of genome annotation a crucial task in the genomic era. Traditional gene finders focus on protein-coding sequences, but they are far from being exhaustive. The number of this kind of genes continuously increases due to new experimental data and development of improved bioinformatics algorithms. In this context, AnABlast represents a novel in silico strategy, based on the accumulation of short evolutionary signals identified by protein sequence alignments of low score. This strategy potentially highlights protein-coding regions in genomic sequences regardless of traditional homology or translation signatures. Here, we analyze the evolutionary information that the accumulation of these short signals encloses. Using the Drosophila melanogaster genome, we stablish optimal parameters for the accurate gene prediction with AnABlast and show that this new strategy significantly contributes to add genes, exons and pseudogenes regions, yet to be discovered in both already annotated and new genomes. AnABlast can be freely used to analyze genomic regions of whole genomes where it contributes to complete the previous annotation.

中文翻译：

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古老的蛋白质编码序列进化信号允许在果蝇果蝇基因组中发现新基因

DNA测序技术的当前发展使得基因组注释成为基因组时代的关键任务。传统的基因发现者专注于蛋白质编码序列，但远非详尽无遗。由于新的实验数据和改进的生物信息学算法的发展，此类基因的数量不断增加。在这种情况下，Anablast代表了一种新型的计算机策略，它基于通过低分蛋白质序列比对确定的短进化信号的积累。不管传统同源性或翻译签名如何，该策略都可能突出显示基因组序列中的蛋白质编码区域。在这里，我们分析了这些短信号的累积所包围的进化信息。使用果蝇果蝇基因组，我们使用AnABlast稳定了用于精确基因预测的最佳参数，并表明该新策略显着有助于添加基因，外显子和假基因区域，但尚未在已注释的基因组和新的基因组中发现。AnABlast可以自由地用于分析整个基因组的基因组区域，从而有助于完成先前的注释。