Integrative Biomedical Informatics, Research Program on Biomedical Informatics (IBI-GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), C/ del Dr. Aiguader 88, Barcelona 08003, Spain.
Structural Bioinformatics Lab, Research Program on Biomedical Informatics (SBI-GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), C/ del Dr. Aiguader 88, Barcelona 08003, Spain.
Bioinformatics. 2024 Jun 3;40(6). doi: 10.1093/bioinformatics/btae379.
Integrative Biomedicl Informatics, Research Program on Biomedical Informatics (IBI - GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF) C/ del Dr. Aiguader 88 Barcelona 08003 Spain.Understanding the genetic basis of complex diseases is one of the main challenges in modern genomics. However, current tools often lack the versatility to efficiently analyze the intricate relationships between genetic variations and disease outcomes. To address this, we introduce Genopyc, a novel Python library designed for comprehensive investigation of how the variants associated to complex diseases affects downstream pathways. Genopyc offers an extensive suite of functions for heterogeneous data mining and visualization, enabling researchers to delve into and integrate biological information from large-scale genomic datasets.
In this work, we present the Genopyc library through application to real-world genome wide association studies variants. Using Genopyc to investigate the functional consequences of variants associated to intervertebral disc degeneration enabled a deeper understanding of the potential dysregulated pathways involved in the disease, which can be explored and visualized by exploiting the functionalities featured in the package. Genopyc emerges as a powerful asset for researchers, facilitating the investigation of complex diseases paving the way for more targeted therapeutic interventions.
Genopyc is available on pip https://pypi.org/project/genopyc/.The source code of Genopyc is available at https://github.com/freh-g/genopyc. A tutorial notebook is available at https://github.com/freh-g/genopyc/blob/main/tutorials/Genopyc_tutorial_notebook.ipynb. Finally, a detailed documentation is available at: https://genopyc.readthedocs.io/en/latest/.
综合生物医学信息学，生物医学信息学研究计划（IBI - GRIB），巴塞罗那 del Mar 医学研究所（IMIM），实验与健康科学系，庞培法布拉大学（UPF）C / del Dr. Aiguader 88 08003 西班牙。理解复杂疾病的遗传基础是现代基因组学的主要挑战之一。然而，目前的工具往往缺乏多功能性，无法有效地分析遗传变异与疾病结果之间错综复杂的关系。为了解决这个问题，我们引入了 Genopyc，这是一个新的 Python 库，用于全面研究与复杂疾病相关的变体如何影响下游途径。Genopyc 提供了广泛的功能，用于异构数据挖掘和可视化，使研究人员能够深入研究并整合来自大规模基因组数据集的生物学信息。
在这项工作中，我们通过应用于真实全基因组关联研究变体来介绍 Genopyc 库。使用 Genopyc 研究与椎间盘退变相关的变体的功能后果，使我们能够更深入地了解该疾病中涉及的潜在失调途径，通过利用该软件包中具有的功能，可以对这些途径进行探索和可视化。Genopyc 是研究人员的有力工具，它可以促进对复杂疾病的研究，为更有针对性的治疗干预铺平道路。
Genopyc 可在 pip 上获得 https://pypi.org/project/genopyc/。Genopyc 的源代码可在 https://github.com/freh-g/genopyc 获得。在 https://github.com/freh-g/genopyc 上提供了一个教程笔记本。最后，在 https://genopyc.readthedocs.io/en/latest/ 上提供了详细的文档。
