Bioinformatics Core

摘要 抗菌素耐药性的发展涉及到人群中异质性的上升。 微生物通过不确定的机制。本单位致力于了解细胞的兴起- 我们称之为导致治疗失败的促进剂。识别这些细胞状态 需要开发、共享、实施支持和分析培训 为每个科学项目提供一整套基因组学和生物信息学方法。它的关键功能是 核心是将方法、数据和支持团队成员与他们的分析目标集成在一起。这 基因组学和生物信息学核心承诺通过以下方式提供这种支持和分析领导 为广泛的核酸文库准备建立严格的工作流程。此外, 我们将开发和分享一系列用于组装的创新生物信息学分析方法， 定量阅读图谱、群落图谱和便携式细菌基因组命名法。 最重要的是，为了这个项目更广泛的影响，我们将实现迭代、可重现 通过对受训者、工作人员和合作者进行生物信息学培训来查询组学数据 这支队伍。 在目标1中，我们开发并提供了分析基因组片段或扩增片段的方法，包括 GDNA测序、条形码测序、TN-Seq、Droplet-TnSeq(DTnseq)、耗尽-Seq(称为 Enabler-seq此处)和CRISPRi/TN-seq我们还将继续发展和节约。 在RNAseq方法中，如RNAtag-Seq、双宿主微生物RNA-Seq(dRNA-Seq)、3‘端MAP-Seq等。 测序(3pMap/Term-Seq)，并将开发使用直接RNA测序来识别 驱动启动子类型的转录修饰或异构体。在目标2中，我们开发和支持 实施各种分析方法，将这些组学数据转化为知识， 包括一条名为Aerobio的创新管道，该管道解决了将多个 数据流和分析流以及新的推理平台，如Loliop和BFCluust。在《目标3》中我们 专注于在整个团队以及最终为更广泛的人实现数据探索的民主化 通过提供关于便携方法的培训，研究社区。这些努力包括一种新的、灵活的 具有图形界面(OrthoPathDB)的直角测井识别工具(OrthoPathDB)基于我们发布的 ShinyOmics，用于分布式安装的共享资源，以及基因组DNA的定期培训 分析、RNAseq、扩增子测序和宿主免疫-微生物相互作用。总而言之， 此核心将始终如一地生成高质量数据，并提供对湿式和 工作台和生物信息学方法，以及它们的工作流程状态，通过开放共享和 训练。

SUMMARY The development of antimicrobial resistance involves the rise of heterogeneity in populations of microbes by uncertain mechanisms. This Unit is focused on the understanding the rise of cell- states that we call enablers that give rise to treatment failure. Identifying these cell states requires the development, sharing, implementation support, and analytical training of a broad suite of genomics and bioinformatics methods for each scientific project. The key function of this Core is to integrate methods, data, and support team members with their analysis goals. This Genomics and Bioinformatics core commits to provide this support and analytical leadership by establishing rigorous workflows for a wide range of nucleic acid library preparations. In addition, we will develop and share a range of innovative bioinformatic analyses methods for assembly, quantitative read-mapping, community profiling, and portable bacterial genome nomenclature. Most importantly for the broader impact of this project, we will enable iterative, reproducible inquiry of ‘omics data with bioinformatics training for trainees, staff, and collaborators throughout the team. In Aim 1, we develop and provide methods to analyze genome fragments or amplicons, including gDNA sequencing, barcode sequencing, Tn-Seq, droplet-TnSeq (dTnseq), Deplete-seq (called Enabler-seq here) and CRISPRi/Tn-seq. We will also continue development and economization of RNAseq methods such as RNAtag-Seq, dual-host-microbe RNA-seq (dRNA-Seq), 3’-end map- sequencing (3pMap / Term-Seq), and will develop the use of direct RNA sequencing to identify transcript modifications or isoforms that drive enabler types. In Aim 2, we develop and support implementation of a variety of analysis methods to turn these ‘omics data into knowledge, including an innovative pipeline called Aerobio that addresses the challenge of integrating multiple data and analysis streams and new inference platforms like LOLIPoP and BFClust. In Aim 3 we focus on democratizing data exploration throughout the team and ultimately for the broader research community by offering training on portable methods. These efforts include a new, flexible ortholog identification tool with a graphical interface (OrthoPathDB) built upon our published ShinyOmics, shared resources for distributed installation, and regular training in genomic DNA analysis, RNAseq, amplicon sequencing, and host immunity - microbe interactions. In summary, this Core will consistently generate high-quality data and provide unfettered access to both wet- bench and bioinformatic methodology, as well as their workflow status, via open sharing and training.