Bioinformatics Core

总结 抗生素耐药性的发展涉及到人群异质性的增加， 微生物通过不确定的机制本单元的重点是了解细胞的崛起- 我们称之为导致治疗失败的促成因素。识别这些细胞状态 需要开发、共享、实施支持和广泛的分析培训， 为每个科学项目提供一套基因组学和生物信息学方法。这个的关键功能 核心是整合方法，数据，并支持团队成员与他们的分析目标。这 基因组学和生物信息学核心致力于提供这种支持和分析领导， 为广泛的核酸文库制备建立严格的工作流程。此外，本发明还提供了一种方法， 我们将开发和分享一系列创新的生物信息学分析方法， 定量读数作图、群落分析和便携式细菌基因组命名法。 最重要的是，为了使这个项目产生更广泛的影响，我们将实现迭代、可复制 通过对学员、工作人员和合作者的生物信息学培训，查询“组学数据”。 球队 在目标1中，我们开发并提供了分析基因组片段或扩增子的方法，包括 gDNA测序、条形码测序、Tn-Seq、Droplet-TnSeq（dTnseq）、耗尽测序（称为 Enabler-seq）和CRISPRi/Tn-seq。我们也将继续发展和节约 RNAseq方法，如RNAtag-Seq、双宿主微生物RNA-seq（dRNA-Seq）、3 '-末端图谱， 测序（3 pMap/ Term-Seq），并将开发使用直接RNA测序来识别 转录物修饰或驱动使能因子类型的同种型。在目标2中，我们开发并支持 实施各种分析方法，将这些组学数据转化为知识， 其中包括一个名为Aerobio的创新管道，该管道解决了整合多个 数据和分析流以及LOLIPoP和BFClust等新的推理平台。在目标3中， 专注于在整个团队中民主化数据探索，并最终为更广泛的 通过提供便携式方法的培训，研究社区。这些努力包括一个新的，灵活的 具有图形界面的直系同源物识别工具（OrthoPathDB）建立在我们发表的 ShinyOmics，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。总的来说， 该核心将持续生成高质量的数据，并提供不受限制的访问湿- 实验室和生物信息学方法，以及他们的工作流程状态，通过开放共享， 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.