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：
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
项目类别：
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

项目摘要

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（由适用提供）：近年来，哺乳动物细胞中的“一种基因产生一种在一个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，通过替代剪接和转录起始和/或终止，超过50％的人基因产生了多种蛋白质同工型。值得注意的是，许多这些基因的破坏在癌症和几种神经精神疾病中实施。对于大多数人类基因，产生的多种蛋白质同工型在功能上不同，并且可以参与不同的信号通路。然而，自人类基因组绘制序列完成以来几乎十年后，我们仍然假设“基因”是细胞中的基本功能单元。我们认为同工型级基因产物 - “转录物变体”和“蛋白质同工型”是基本功能哺乳动物细胞中的单元，因此，哺乳动物细胞中管理和分析基因调节数据的信息资源应采用“以基因同工型为中心”而不是“以基因为中心”的方法。我们建议通过开发用于处理下一代测序（NGS）数据的统计严格的生物信息学资源来构建一个信息的平台，以了解同工型级别的基因调节。最近，结合了看似不同的实验数据的计算方法已成功地开发了简洁的基因调节模型和转录模块。我们计划将这些方法扩展到当前跨不同实验室（包括我们在Wistar的不同实验室）生成的多个高通量数据集的集成分析，以预测哺乳动物基因的不同转录同工型和在同工型水平上的蛋白质-DNA相互作用。我们将采用创新的统计建模方法，结合了最先进的元分类算法，例如NaâVeVe Ve Bayes Tree，Bagging和LogitBoost，以及随机的森林功能选择，以良好的分类准确性和降低的目标启动子来对不同类型的目标启动子进行分类，以预测基因启动子并从Chip-Seq-seq数据中推断蛋白质DNA相互作用。计算模型和派生信息将集成到新的数据库中，该数据库将作为转录调节研究的计算机平台。（1）（1）开发统计上严格的新颖算法和生物信息学管道，以识别直系同源的启动子，相应的转录物变体和蛋白质同工型，这些变体和蛋白质同工型是人类和小鼠之间的（2）开发新型算法和信息启动的启动者，以及对NGS进行启动的启动，以估算NGS的促进型，以估算NGS的分析，以估算NGS的分析，以估算NGS的分析，以估算NGS的分析，以估算NGS的分析，以估算NGS的估算值和图表。在各种时机，发育阶段和疾病状况中的变体以及（3）开发一个可访问的数据库，以整合生成的信息。该项目开发的新型生物信息学方法将有助于在基因 - 异型型水平上加速表型和基因组信息的链接，从而有助于硅化和研究。