Bioinformatics, Data Analytics and Predictive Modeling

生物信息学、数据分析和预测建模核心的总体目标是支持我们的用户 信息学的需要，并为成瘾和神经科学领域创造创新的信息学资源， 推进我们对细胞间信号传导、药物成瘾和药物暴露过程的理解。高- 通量肽组学、蛋白质组学、代谢组学、转录组学和基因组学研究推进了我们 了解与健康和疾病相关的分子过程，并包括测序， 肽、蛋白质、蛋白质组装体、代谢物、转录物和基因的鉴定和分析。的 这些信息的整合加强了对分子途径的表征， 暴露于滥用药物。挑战来自于检查蛋白质和肽的复杂混合物， 从分析大量的数据，从肽，蛋白质和转录异构体，和基因。这些 当研究神经肽时，由于其复杂的转录后和后 翻译加工研究神经递质、蛋白质、蛋白质复合物和代谢物 类似的挑战。我们的核心坚定的使命是开发和促进使用强大的和敏感的 分析工具，以促进与药物暴露和细胞相关的分子过程的理解 信号我们已经成为注释，预测和表征的首要资源， 神经肽和蛋白形式，同时通过公共知识库和开源发现分享我们的发现 工具.我们的使命是通过（a）开发所需的生物信息学资源;（B）与 采样和分离核心以及分子特征分析和表征核心， 生物信息学事项，包括实验设计;高级分析、基准测试和交叉验证; 和可视化的结果。针对目前生物信息学的局限性，提出了四个目标：（1） 肽和蛋白质的严格鉴定;（2）连接肽的精确检测和定量 细胞-细胞信号传导;（3）增强的驱动分子和与之相关的分子关系的表征 细胞信号传导和药物暴露;以及（4）蛋白质形式的精确表征和定量， 与细胞间信号传导和dug暴露相关的蛋白质复合物。建议的努力中心的意义 开发生物信息学资源，提高肽，蛋白质， 通过整合，蛋白质形式、蛋白质复合物、代谢物和转录物的鉴定和表征 多组学信息。开发的方法将被应用于增强我们对成瘾的理解- 相关的通路，并与神经科学界分享。

The overarching goal of the Bioinformatics, Data Analytics and Predictive Modeling Core is to support our users' informatics needs and to create innovative informatics resources for the addiction and neuroscience fields to advance our understanding of the cell-cell signaling, drug addiction and drug exposure processes. High- throughput peptidomics, proteomics, metabolomics, transcriptomics, and genomics studies advance our understanding of the molecular processes associated with health and disease, and encompass sequencing, identification, and profiling of peptides, proteins, protein assemblies, metabolites, transcripts, and genes. The integration of this information strengthens the characterization of molecular pathways underlying the effects of exposure to drugs of abuse. Challenges arise from examining complex mixtures of proteins and peptides, and from analyzing massive volumes of data from peptides, protein and transcript isoforms, and genes. These challenges are magnified when studying neuropeptides because of their complex post-transcriptional and post- translational processing. Studying neurotransmitters, proteins, protein complexes, and metabolites present comparable challenges. Our Core's steadfast mission is to develop and facilitate the use of robust and sensitive analytical tools to advance the understanding of molecular processes associated with drug exposure and cell signaling. We have become a premier resource for the annotation, prediction, and characterization of neuropeptides and proteoforms while sharing our findings through public repositories and open source discovery tools. Our mission is accomplished by (a) developing needed bioinformatics resources; and (b) collaborating with the Sampling and Separation Core and the Molecular Profiling and Characterization Core in assisting users on bioinformatics matters, including experimental design; advanced analysis, benchmarking, and cross-validation; and visualization of the findings. Addressing present bioinformatic limitations, four Aims are proposed: (1) rigorous identification of peptides and proteins; (2) precise detection and quantification of the peptides linked to cell-cell signaling; (3) enhanced characterization of driver molecules and molecular relationships associated with cell signaling and drug exposure; and (4) precise characterization and quantification of the proteoforms and protein complexes linked to cell-cell signaling and dug exposure. The significance of the proposed efforts centers on the development of bioinformatics resources that will improve the accuracy and precision of peptide, protein, proteoform, protein complex, metabolite, and transcript identification and characterization through the integration of multi-omic information. The developed approaches will be applied to augment our understanding of addiction- associated pathways and shared with the neuroscience community.