Informatics Platform for Mammalian Gene Regulation at Isoform-level

异构体水平的哺乳动物基因调控信息学平台

基本信息

批准号：
10273985
负责人：
RAMANA V DAVULURI
金额：
$ 34.36万
依托单位：
STATE UNIVERSITY NEW YORK STONY BROOK
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-11-01 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10273985
关键词：
Address Adopted Algorithmic Analysis Algorithms Alleles Alternative Splicing Architecture Bioinformatics Biological Assay Bipolar Disorder Brain Neoplasms CRISPR/Cas technology Cancer Patient Cell Line Cells Classification Cluster Analysis Computer software Computing Methodologies DNA-Binding Proteins Data Data Set Derivation procedure Development Diagnosis Disease Epigenetic Process Exons Gene Expression Gene Expression Regulation Genes Genetic Genetic Transcription Genome Genomics Glioblastoma Goals Human Human Genome Informatics Knowledge Label Luciferases Malignant Neoplasms Mammalian Cell Methods Molecular Mus Mutation Neurodegenerative Disorders Normal tissue morphology Parkinson Disease Pattern Phenotype Protein Isoforms Quantitative Trait Loci RNA Splicing Regulation Regulator Genes Reporter Research Sampling Schizophrenia Signal Pathway Site Specimen Statistical Data Interpretation Statistical Methods Stratification Tissue Sample Transcript Update Variant autism spectrum disorder base bioinformatics tool cancer subtypes chromatin immunoprecipitation classification algorithm data integration data mining diagnostic biomarker discrete data feature selection gene function gene product genetic signature genome editing genome-wide analysis high dimensionality improved in silico innovation molecular diagnostics multiple omics neuropsychiatric disorder novel open source outcome forecast platform-independent prediction algorithm programs promoter protein function public health relevance therapeutic target tool transcriptome transcriptome sequencing tumor user friendly software user-friendly

Address Adopted Algorithmic Analysis Algorithms Alleles Alternative Splicing Architecture Bioinformatics Biological Assay Bipolar Disorder Brain Neoplasms CRISPR/Cas technology Cancer Patient Cell Line Cells Classification Cluster Analysis Computer software Computing Methodologies DNA-Binding Proteins Data Data Set Derivation procedure Development Diagnosis Disease Epigenetic Process Exons Gene Expression Gene Expression Regulation Genes Genetic Genetic Transcription Genome Genomics Glioblastoma Goals Human Human Genome Informatics Knowledge Label Luciferases Malignant Neoplasms Mammalian Cell Methods Molecular Mus Mutation Neurodegenerative Disorders Normal tissue morphology Parkinson Disease Pattern Phenotype Protein Isoforms Quantitative Trait Loci RNA Splicing Regulation Regulator Genes Reporter Research Sampling Schizophrenia Signal Pathway Site Specimen Statistical Data Interpretation Statistical Methods Stratification Tissue Sample Transcript Update Variant autism spectrum disorder base bioinformatics tool cancer subtypes chromatin immunoprecipitation classification algorithm data integration data mining diagnostic biomarker discrete data feature selection gene function gene product genetic signature genome editing genome-wide analysis high dimensionality improved in silico innovation molecular diagnostics multiple omics neuropsychiatric disorder novel open source outcome forecast platform-independent prediction algorithm programs promoter protein function public health relevance therapeutic target tool transcriptome transcriptome sequencing tumor user friendly software user-friendly

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项目摘要

SUMMARY With each successive discovery in genetics, the true dynamic complexity of the human genome has become increasingly apparent, requiring relatively consistent updates to the technical definition of the word “gene”. It is now understood that the notion of “one gene makes one protein that functions in one signaling pathway” in human cells is overly simplistic, because majority of the human genes produce multiple functional products (transcript variants and protein isoforms), through alternative transcription and/or alternative splicing. Therefore, our central hypothesis is that the isoform-level gene products – “transcript variants” and “protein isoforms” are the basic functional units in a mammalian cell, and accordingly, the informatics platforms for managing and analyzing gene regulation data both in normal and disease cells should adopt “gene isoform centric” rather than “gene centric” approaches. Towards the goal of broadly impacting gene regulation and functional studies at gene isoform-level, we have been developing novel algorithms for analyses of genome- wide transcriptome (RNA-seq and exon-array) and protein-DNA binding (ChIP-seq) data, and for extending the gene-level orthology mapping to exon- and transcript-level mapping between the orthologous human and mouse genes. By applying these novel algorithms on public datasets, we have observed significant expression differences between different sample groups (e.g., developmental stages, cancer subtypes, normal vs cancer) for numerous genes at the isoform-level but not at the overall gene-level, and experimentally validated the `significant' isoforms using RT-qPCR in independent bio-specimens. While the application of these algorithms has led to the development of new methods for diagnosis of glioblastoma or a sub-type thereof, the isoform- level transcriptome analyses results also led to some challenging questions – for example – How are the alternative promoters of a gene show switch-like opposing patterns of activity (while one promoter is up- the other is down-regulated in one condition vs the other), and how are different splice-variants of a gene show opposing expression patterns in cancer versus normal tissue samples? We currently lack informatics methods to address these challenging questions. Therefore, we propose to develop novel statistical methods (1) for integrative cluster analysis of isoform-level gene expression information from exon-array and RNA-seq platforms, (2) for identification of differential transcript/isoform usage in heterogeneous cancer samples, and (3) for identification of alternative transcription/splicing quantitative trait locus (sQTL) in tumor adjusted by somatic genetic and epigenetic changes. And, (4) the novel predictions from these algorithms will be experimentally validated by performing Chromatin immunoprecipitation (ChIP), dual-luciferase reporter assay and CRISPR/Cas9 genome editing in U87 and A172 cells. The novel bioinformatics methods developed by this project will help in silico discovery and research for accelerating the linkage of phenotypic and genomic information, at gene-isoform level.

概括随着遗传学中的每一个成功发现，人类基因组的真正动态复杂性已成为越来越明显，需要对“基因”一词的技术定义进行相对一致的更新。这是现在了解“一个基因使一种在一个信号通路中起作用的蛋白质的概念” 人类细胞过于简单，因为大多数人基因产生多种功能产物（转录本变体和蛋白质同工型），通过替代转录和/或替代剪接。因此，我们的中心假设是同工型级基因产物 - “转录物变体”和“蛋白质同工型”是哺乳动物细胞中的基本功能单元，因此，信息的平台在正常和疾病细胞中管理和分析基因调控数据都应采用“基因同工型以中心”而不是“以基因为中心”的方法。目的是广泛影响基因调节和在基因同工型级的功能研究，我们一直在开发新的算法，用于分析基因组 - 广泛的转录组（RNA-SEQ和EXON-ARRAY）和蛋白-DNA结合（CHIP-SEQ）数据，用于扩展基因级矫正映射到直系同源的人和成绩单级映射小鼠基因。通过在公共数据集上应用这些新型算法，我们观察到了重要的表达不同样本组之间的差异（例如，发育阶段，癌症亚型，正常与癌症）之间的差异对于同工型级的许多基因，但在整个基因级别上不进行，并在实验上验证了使用RT-QPCR在独立的生物特异性中使用RT-QPCR的“显着”同工型。在应用这些算法的同时导致开发用于诊断胶质母细胞瘤或亚型的新方法，同工型 - 等级转录组分析结果也导致了一些挑战问题，例如 - 基因显示类似开关的相对活动模式的替代启动子（而一个启动子是向上的 - 另一个条件在另一个条件下被下调），而基因显示的不同剪接变化是如何的癌症与正常组织样本中的相对表达模式？我们目前缺乏信息性方法解决这些挑战问题。因此，我们建议开发新的统计方法（1）来自外显子阵列和RNA-Seq的同工级基因表达信息的综合聚类分析平台，（2）用于鉴定异质癌样本中的差异转录本/同工型使用，并（3）用于鉴定肿瘤中的替代转录/剪接定量性状基因座（SQTL）体细胞遗传和表观遗传变化。（4）这些算法的新颖预测将是通过执行染色质免疫沉淀（CHIP），双雷酸酶报告基因测定法测定实验验证以及U87和A172细胞中的CRISPR/CAS9基因组编辑。新颖的生物信息学方法由此开发项目将有助于计算机发现和研究，以加速表型和基因组的连锁信息，基因 - 异型水平。