Core C. Bioinformatics and data sharing.

核心 C. 生物信息学和数据共享。

• 批准号: 10426178
• 负责人: SABINE EHRT
• 金额: $ 32.03万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-12 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10426178
• 关键词: Anabolism; Archives; Bioinformatics; Biological; Biological Assay; Biotin; CRISPR interference; Carbon; Cell division; Cell physiology; Collection; Critical Pathways; Data; Data Set; Databases; Deposition; Disease; Essential Genes; Genbank; Gene Library; Genes; Genetic; Genome; Growth; Incubated; Infection; Iron; Knock-out; Libraries; Light; M. tuberculosis genome; Machine Learning; Metabolic; Metabolic Pathway; Methods; Modeling; Mus; Mycobacterium tuberculosis; Ontology; Pathway Analysis; Pathway interactions; Pattern; Phenotype; Play; Proteins; RNA Processing; Regulon; Research Personnel; Role; Source; Statistical Data Interpretation; Statistical Methods; Stress; Training; Tuberculosis; Work; base; chemical genetics; data dissemination; data sharing; design; differential expression; experimental study; gene function; in vivo; independent component analysis; knock-down; machine learning algorithm; metabolic abnormality assessment; metabolomics; mutant; neural network; new technology; novel; predictive modeling; programs; protein function; public repository; response; transcriptome sequencing; transcriptomics; transposon sequencing; vector; web site

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.

核心C，摘要