Data_Sheet_1_A Computational Pipeline for the Extraction of Actionable Biological Information From NGS-Phage Display Experiments.PDF

Phage Display is a powerful method for the identification of peptide binding to targets of variable complexities and tissues, from unique molecules to the internal surfaces of vessels of living organisms. Particularly for in vivo screenings, the resulting repertoires can be very complex and difficult to study with traditional approaches. Next Generation Sequencing (NGS) opened the possibility to acquire high resolution overviews of such repertoires and thus facilitates the identification of binders of interest. Additionally, the ever-increasing amount of available genome/proteome information became satisfactory regarding the identification of putative mimicked proteins, due to the large scale on which partial sequence homology is assessed. However, the subsequent production of massive data stresses the need for high-performance computational approaches in order to perform standardized and insightful molecular network analysis. Systems-level analysis is essential for efficient resolution of the underlying molecular complexity and the extraction of actionable interpretation, in terms of systemic biological processes and pathways that are systematically perturbed. In this work we introduce PepSimili, an integrated workflow tool, which performs mapping of massive peptide repertoires on whole proteomes and delivers a streamlined, systems-level biological interpretation. The tool employs modules for modeling and filtering of background noise due to random mappings and amplifies the biologically meaningful signal through coupling with BioInfoMiner, a systems interpretation tool that employs graph-theoretic methods for prioritization of systemic processes and corresponding driver genes. The current implementation exploits the Galaxy environment and is available online. A case study using public data is presented, with and without a control selection.

噬菌体展示技术是一种高效的方法，用于识别与复杂多变的目标和组织（从独特分子至生物体血管内表面）结合的肽段。特别是在体内筛选中，由此产生的库可能非常复杂，难以采用传统方法进行研究。下一代测序（NGS）为获取此类库的高分辨率概览提供了可能性，从而有助于识别感兴趣的结合物。此外，随着可用的基因组/蛋白质组信息的不断增多，在评估部分序列同源性时达到的规模已经足以满足对拟似蛋白质鉴定的需求。然而，大量数据的产生对高性能计算方法提出了需求，以执行标准化且富有洞见的分子网络分析。系统水平的分析对于有效解析潜在的分子复杂性以及提取具有可操作性的解释至关重要，这些解释涉及系统生物学过程中的系统性扰动途径。在本研究中，我们引入了PepSimili，这是一个集成的流程工具，它执行大量肽段库与全蛋白质组的映射，并提供了简化的系统水平生物学解释。该工具采用模块化建模和过滤由于随机映射产生的背景噪声，并通过与BioInfoMiner（一个采用图论方法进行系统过程及其相应驱动基因优先级排序的系统解释工具）的耦合，放大了具有生物学意义的信号。当前实现利用了Galaxy环境，并可供在线使用。我们通过使用公共数据的一个案例研究进行了展示，其中包含了控制选择和不包含控制选择的两种情况。