Core C. Bioinformatics and data sharing.

核心C，抽象 尽管自结核分枝杆菌基因组(Mtb)首次被发现以来，已有20多年的时间 经过测序，基因组中超过一半的基因的功能仍然未知(或不清楚)。其他来源 为了更好地理解特定的生物学作用，需要一系列的信息(超越同源性) 结核分枝杆菌基因在基本的细胞过程和感染中发挥作用。在这个项目中，四条与疾病相关的途径 将研究：对酸胁迫的代谢反应、生物素生物合成、RNA加工和细胞分裂。这个 这些项目将严重依赖的四种实验方法是转座子测序(TnSeq；Essentiality)， RNAseq(转录组学)、基于活性的代谢图谱(ABMP)和CRISPR干扰(CRISPRi)。 TnSeq和RNAseq将应用于靶基因的敲除或敲除菌株，以评估细胞内 对这些扰动的反应，并推断新的基因功能和途径关联。按顺序排列 转座子插入文库(TnSeq)为探索功能和关系提供了一种强大的方法 基因之间通过条件重要性和遗传相互作用。CRISPRi将用于生成 敲除菌株以验证TnSeq预测的表型。转录数据产生了关于联合- 表达，可用于推断功能关系，例如通过调节网络。ABMP提供 通过改变代谢物浓度(例如，可能的底物或 产品)，当纯化蛋白在裂解物中孵育时。 生物信息学和数据共享核心的作用是协助项目进行严格的统计 对这些数据的分析。具体来说，在活动1中，核心将对各种组学进行生物信息学分析 用于识别靶途径中的新基因并定义其功能角色的数据类型。在练习2中，核心 我将应用机器学习算法来集成这些不同的组学数据类型并构建预测模型 这可以用来识别这些途径中的新基因，并预测它们的功能。最后，在活动3下， 生物信息学和数据共享核心将作为数据共享的集中渠道；包括存储 将数据集保存在适当的公共存储库中，并将数据发布在他们开发的MTB专用网站上。

Core C, Abstract Even though it has been over 20 years since the genome of Mycobacterium tuberculosis (Mtb) was first sequenced, the functions of over half the genes in the genome remain unknown (or unclear). Additional sources of information (beyond homology) are needed to generate a better understanding of the specific biological roles Mtb genes play in basic cellular processes and infection. In this program project, four disease-relevant pathways will be studied: metabolic response to acidic stress, biotin biosynthesis, RNA processing, and cell division. The four experimental methods that the Projects will rely heavily on are transposon sequencing (TnSeq; essentiality), RNAseq (transcriptomics), Activity-Based Metabolomic Profiling (ABMP), and CRISPR interference (CRISPRi). TnSeq and RNAseq will be applied to knockout or knockdown strains of target genes to assess the intracellular responses to these perturbations and infer novel gene functions and pathway associations. Sequencing of transposon insertion libraries (TnSeq) provides a powerful method for probing the functions and relationships among genes through conditional essentiality and genetic interactions. CRISPRi will be used to generate knockdown strains to validate phenotypes predicted by TnSeq. Transcriptomic data yields information on co- expression, which can be used to infer functional relationships, e.g. through regulatory networks. ABMP provides direct information on gene functions through changes in metabolite concentrations (e.g. possible substrates or products) when purified protein is incubated in lysate. The role of the Bioinformatics and Data Sharing Core is to assist the Projects with rigorous statistical analyses of these data. Specifically, in Activity 1 the Core will conduct bioinformatic analysis of various 'omics datatypes to identify novel genes in the target pathways and define their functional roles. In Activity 2 the Core will apply Machine Learning algorithms to integrate these diverse 'omics datatypes and build predictive models that can be used to identify new genes in these pathways and predict their functions. Finally, under Activity 3 the Bioinformatics and Data Sharing Core will serve as a centralized conduit for data sharing; including depositing datasets in appropriate public repositories, and posting data on an Mtb-dedicated website they have developed.