Informatics platform for mammalian gene regulation at isoform-level

描述（由申请人提供）：近年来，在哺乳动物细胞中“一个基因产生在一个信号通路中起作用的一种蛋白质”的概念已被证明过于简单化。最近的证据表明，超过50%的人类基因产生多种蛋白质异构体，通过选择性剪接和转录起始和/或终止的选择性使用。值得注意的是，许多这些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，所产生的多种蛋白质同种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，在人类基因组序列草图完成近十年后，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中的基因调控数据的信息学资源应该采用“以基因同种型为中心”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）数据的统计上严格的生物信息学资源，建立一个在异构体水平上理解基因调控的信息学平台。最近，联合收割机看似不同的实验数据的计算方法已经成功地开发了简洁的基因调控模型和转录模块。我们计划扩展这些方法，对目前在不同实验室（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，并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和异构体水平上的蛋白质-DNA相互作用。我们将采用创新的统计建模方法，结合联合收割机最先进的元分类算法，如朴素贝叶斯树，Bagging和LogitBoost，与随机森林特征选择，以良好的分类准确性和减少不稳定性来分类不同类型的目标启动子，以预测基因启动子并从ChIP-seq数据中推断蛋白质-DNA相互作用。计算模型和衍生的信息将被整合到一个新的数据库，这将作为一个在硅片上的转录调控研究的平台。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鉴定人和小鼠之间保守的正向启动子、相应的转录变体和蛋白质同种型，（二）开发新的算法和信息学管道，用于NGS数据集的综合分析，以估计已知和新启动子及其转录物的活性和表达变异，在不同的组织，发展阶段，和疾病状况，和（3）开发一个网络访问的数据库，整合所产生的信息。该项目开发的新的生物信息学方法将有助于在基因异构体水平上加速表型和基因组信息的联系的计算机发现和研究。

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.