Bioinformatics Core

生物信息学核心

基本信息

批准号：
10703344
负责人：
Vaughn Cooper
金额：
$ 55.93万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-12 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10703344
关键词：
Address Antibiotic Therapy Antimicrobial Resistance Bacterial Genome Bar Codes Bioinformatics CRISPR interference Cells Communities Custom DNA analysis DNA methylation profiling Data Databases Democracy Deposition Development Educational process of instructing Educational workshop Failure Genes Genome Genomic DNA Genomics Genotype Goals Guppy Heterogeneity High-Throughput Nucleotide Sequencing Immunity Institution Leadership Libraries Maps Mediating Metagenomics Methodology Methods Microbe Modification Nomenclature Nucleic Acids Orthologous Gene Performance Population Preparation Protein Isoforms Publishing RNA Reproducibility Research Research Personnel Resource Sharing Resources Salmon Shotguns Stream Training Transcript Treatment Failure Universities Visualization automated analysis comparative genomics data exploration data sharing data to knowledge deep sequencing droplet sequencing flexibility forest graphical user interface host-microbe interactions innovation member methylome nanopore pathogen portability public database screening tool transcriptome sequencing transposon sequencing web app

项目摘要

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.

概括抗菌耐药性的发展涉及在微生物通过不确定的机制。该单元的重点是理解细胞的兴起指出我们称会导致治疗失败的推动力。识别这些细胞状态需要广泛的开发，共享，实施支持和分析培训每个科学项目的基因组学和生物信息学方法套件。此关键功能核心是将方法，数据和支持团队成员与他们的分析目标集成。这基因组学和生物信息学核心致力于通过为广泛的核酸文库制剂建立严格的工作流程。此外，我们将开发和共享一系列创新的生物信息学分析方法，用于组装，定量阅读图，社区分析和便携式细菌基因组命名法。最重要的是，对于该项目的更广泛影响，我们将实现迭代，可再现的查询“对培训，员工和合作者的生物信息学培训的OMICS数据团队。在AIM 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，液滴-TNSEQ（DTNSEQ），DEPLETE-SEQ（计算在此处启用seq）和crispri/tn-seq。我们还将继续发展和经济 RNASEQ方法，例如rnatag-seq，双架 - 摩卡RNA-seq（DRNA-Seq），3'-End Map- 测序（3pmap / term-seq），并将开发直接RNA测序的使用以识别驱动启用器类型的成绩单修改或同工型。在AIM 2中，我们开发和支持实施各种分析方法将这些'OMIC数据转化为知识，包括一个名为Aerobio的创新管道，该管道解决了整合多个的挑战数据和分析流以及诸如lolipop和bfclust之类的新推理平台。在目标3中我们专注于整个团队的数据探索，并最终以更广泛的方式进行民主化通过提供便携式方法培训来研究社区。这些努力包括一个新的灵活用图形接口（Orthopathdb）构建的直系同源识别工具发光，共享用于分布式安装的资源以及基因组DNA的定期培训分析，RNASEQ，扩增子测序和宿主免疫史 - 微生物相互作用。总之，该核心将始终产生高质量的数据，并提供对这两个湿的不受限制访问通过公开共享和训练。