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

• 批准号: 8658144
• 负责人: RAMANA V DAVULURI
• 金额: $ 33.72万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-02 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8658144
• 关键词: Adopted; Algorithms; Alternative Splicing; Arts; Autistic Disorder; Binding Sites; Bioinformatics; Bipolar Disorder; Cells; ChIP-seq; Classification; Complex; Computational algorithm; Computer Simulation; Computer software; DNA; DNA Polymerase II; DNA-Protein Interaction; Data; Data Set; Databases; Development; Disease; Embryonic Development; Fertility; Gene Expression; Gene Expression Regulation; Gene Proteins; Generations; Genes; Genetic Transcription; Genome; Genomics; Goals; Homologous Gene; Human; Human Genome; Informatics; Laboratories; Life; Malignant Neoplasms; Mammalian Cell; Methodology; Methods; Modeling; Molecular; Mus; Nature; Oncogenic; Parkinson Disease; Pathway interactions; Pattern Recognition; Process; Protein Isoforms; Protein Kinase; Proteins; Regulation; Regulator Genes; Research; Research Personnel; Resource Informatics; Resources; Schizophrenia; Secure; Signal Pathway; Solutions; Staging; Statistical Models; TP53 gene; TP73 gene; Technology; Tissues; Transcript; Transcription Initiation; Transcription factor genes; Transcriptional Regulation; Trees; Tumor Suppressor Genes; Tumor Suppressor Proteins; Variant; Work; base; cancer initiation; cost; epigenomics; forest; genome wide association study; improved; innovation; neuropsychiatry; next generation sequencing; novel; prevent; promoter; protein function; public health relevance; tool; transcription factor; transcriptome sequencing; tumor progression; user friendly software; user-friendly; web-accessible

DESCRIPTION (provided by applicant): In recent years, the notion of "one gene makes one protein that functions in one signaling pathway" in mammalian cells has been shown to be overly simplistic. Recent evidence suggests that more than 50% of the human genes produce multiple protein isoforms, through alternative splicing and alternative usage of transcription initiation and/or termination. Notably, the disruption of many of these genes is implicated in cancer and several neuropsychiatric disorders. For majority of human genes the resulting multiple protein isoforms are functionally different and can participate in different signaling pathways. However, nearly after a decade since the completion of the human genome draft sequence, we still assume "gene" as the basic functional unit in a cell. We argue that the isoform-level gene products - "transcript variants" and "protein isoforms" are the basic functional units in a mammalian cell, and accordingly, the informatics resources for managing and analyzing gene regulation data in mammalian cells should adopt "gene isoform centric" rather than "gene centric" approaches. We propose to build an informatics platform for understanding gene regulation at isoform-level by developing statistically rigorous bioinformatics resources for processing Next-Generation Sequencing (NGS) data. Recently, computational approaches that combine seemingly disparate experimental data have been successful in developing concise gene regulation models and transcriptional modules. We plan to extend these methodologies to perform integrative analysis of multiple high-throughput data sets currently generated across different laboratories, including ours at Wistar, into computational models to predict different transcriptional isoforms of mammalian genes and protein-DNA interactions at isoform level. We will apply innovative statistical modeling approaches that combine state-of-the-art meta-classification algorithms, such as Na¿ve Bayes Tree, Bagging and LogitBoost, with Random Forest feature selection to classify different types of target promoters with good classification accuracy and reduced instability, in order to predict gene promoters and infer the protein-DNA interactions from ChIP-seq data. The computational models and the derived information will be integrated into a novel database, which will serve as an in silico platform for transcriptional regulation studies. This will be completed by pursuing the following aims, (1) Develop statistically rigorous novel algorithms and bioinformatics pipelines to identify the orthologous promoters, corresponding transcript variants and protein isoforms that are conserved between human and mouse, (2) develop novel algorithms and informatics pipelines for integrative analysis of NGS datasets to estimate the activity and expression of both known and novel promoters and their transcript variants, in various tissues, developmental stages, and disease conditions, and (3) develop a web-accessible database for integrating the information generated. 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.

描述（由申请人提供）：近年来，在哺乳动物细胞中“一个基因产生一种在一种信号通路中起作用的蛋白质”的概念已被证明过于简单化。最近的证据表明，超过 50% 的人类基因通过选择性剪接和转录起始和/或终止的选择性使用产生多种蛋白质亚型。值得注意的是，其中许多基因的破坏与癌症和几种神经精神疾病有关。对于大多数人类基因而言，产生的多种蛋白质亚型在功能上是不同的，并且可以参与不同的信号传导途径。然而，人类基因组草图序列完成近十年后，我们仍然认为“基因”是细胞中的基本功能单位。我们认为，亚型水平的基因产物——“转录变体”和“蛋白质亚型”是基本的功能。 因此，用于管理和分析哺乳动物细胞基因调控数据的信息学资源应采用“以基因亚型为中心”而不是“以基因为中心”的方法。我们建议通过开发统计严格的生物信息学资源来处理下一代测序（NGS）数据，建立一个信息学平台，以了解亚型水平的基因调控。最近，结合看似不同的实验数据的计算方法已成功开发出简洁的基因调控模型和转录模块。我们计划扩展这些方法，对当前在不同实验室（包括我们在 Wistar 的实验室）生成的多个高通量数据集进行综合分析，将其转化为计算模型，以预测哺乳动物基因的不同转录异构体以及异构体水平上的蛋白质-DNA 相互作用。我们将应用创新的统计建模方法，将最先进的元分类算法（例如朴素贝叶斯树、Bagging 和 LogitBoost）与随机森林特征选择相结合，以良好的分类精度和较低的不稳定性对不同类型的目标启动子进行分类，以便预测基因启动子并从 ChIP-seq 数据推断蛋白质-DNA 相互作用。计算模型和衍生信息将被整合到一个新的数据库中，该数据库将作为转录调控研究的计算机平台。这将通过追求以下目标来完成：(1) 开发统计上严格的新算法和生物信息学管道，以识别人类和小鼠之间保守的直向同源启动子、相应的转录本变体和蛋白质亚型，(2) 开发新的算法和信息学管道，用于 NGS 数据集的综合分析，以估计已知和新型启动子及其转录本的活性和表达 变体，在不同的组织、发育阶段和疾病状况下，以及（3）开发一个可通过网络访问的数据库来整合生成的信息。该项目开发的新型生物信息学方法将有助于计算机发现和研究，以加速基因异构体水平上表型和基因组信息的联系。