Addressing Open Challenges of Computational Genome Annotation

我们建议利用生物信息学正在进行的合作的成功 格雷夫斯瓦尔德大学(德国)和佐治亚理工学院(美国)的团队 并解决了计算基因组注释中的开放挑战。在这个过程中 发展，我们计划实施新的算法思想，并满足无偏见的需求 集成不同类型的OMICS数据。 我们计划解决生物信息学接口上的一个长期存在的问题 机器学习.基因发现的自动生成性和判别性参数化 算法。当前结合OMICS证据的方法经常导致预测不足 或者过度使用预测工具。充分了解……的困难和特点 不同类型的OMICS证据我们提出了一种优化的方法来完全无监督地， 生成性和鉴别性训练。 我们将引入新的方法来优化多个OMICS证据到基因的整合 预测。这些想法将进一步发展实施的基于蛋白质家族的基因发现 在奥古斯都-PPX。我们建议创建用于基因发现的蛋白质家族的表示 这是第一次包括跨物种的基因结构信息。 我们将开发一种新的方法，将两个先进的研究领域统一起来-文字记录 从RNA-Seq重建和整合RNA-Seq和同源性的统计基因发现 信息。我们将描述一种新的、全面的模型和类似EM的算法技术 (“整体”方法)确定最适合的转录组及其表达水平 现有的OMICS证据。 我们还将开发一种全内容元基因组的自动基因查找算法 包括真核生物和病毒的元基因组序列。这项任务通常被认为是 太有挑战性了。我们提出了一种利用和改进算法思想的解决方案，并 我们在创建原核元基因组基因搜索器的过程中掌握的方法 以及真核生物的基因组。 所有新工具都将在开放源码许可下向社区开放。

We propose to capitalize on success of ongoing collaboration between the bioinformatics teams at the University of Greifswald (Germany) and at the Georgia Institute of Technology (USA) and address open challenges in computational genome annotation. In the course of this development, we plan to implement new algorithmic ideas and satisfy the needs of unbiased integration of different types of OMICS data. We plan to address one of the long-standing problems at interface of bioinformatics and machine learning – automatic generative and discriminative parameterization of gene finding algorithms. Current methods of combining OMICS evidence frequently result in under predicting or over predicting tools. Having good understanding of the difficulties and the properties of different types of OMICS evidence we propose an optimized approach to the full unsupervised, generative and discriminative training. We will introduce novel means to optimize integration of multiple OMICS evidence into gene prediction. These ideas will develop further the protein family-based gene finding implemented in AUGUSTUS-PPX. We propose to create representations of protein families for gene finding that for the first time include cross-species gene structure information. We will develop a new approach that will unify two advanced research areas - transcript reconstruction from RNA-Seq and statistical gene finding that integrates RNA-Seq and homology information. We will describe a new, comprehensive model and EM-like algorithmic technique (the “wholistic” approach) to identify the sets of transcripts and their expression levels that best fit the available OMICS evidence. We will also develop an automatic gene-finding algorithm for a full content of metagenomes including eukaryotic and viral metagenomic sequences. This task is conventionally considered too challenging. We propose a solution exploiting and advancing algorithmic ideas and approaches that we mastered in the course of creating gene finders for prokaryotic metagenomes as well as eukaryotic genomes. All new tools will be available to the community under open source licenses.