Schwartz Ashley V, Sant Karilyn E, George Uduak Z
Computational Science Research Center, College of Sciences, San Diego State University, San Diego, CA 92182, United States.
Division of Environmental Health, School of Public Health, San Diego State University, San Diego, CA 92182, United States.
Bioinform Adv. 2024 May 6;4(1):vbae065. doi: 10.1093/bioadv/vbae065. eCollection 2024.
Understanding the pathways and biological processes underlying differential gene expression is fundamental for characterizing gene expression changes in response to an experimental condition. Zebrafish, with a transcriptome closely mirroring that of humans, are frequently utilized as a model for human development and disease. However, a challenge arises due to the incomplete annotations of zebrafish pathways and biological processes, with more comprehensive annotations existing in humans. This incompleteness may result in biased functional enrichment findings and loss of knowledge. danRerLib, a versatile Python package for zebrafish transcriptomics researchers, overcomes this challenge and provides a suite of tools to be executed in Python including gene ID mapping, orthology mapping for the zebrafish and human taxonomy, and functional enrichment analysis utilizing the latest updated Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. danRerLib enables functional enrichment analysis for GO and KEGG pathways, even when they lack direct zebrafish annotations through the orthology of human-annotated functional annotations. This approach enables researchers to extend their analysis to a wider range of pathways, elucidating additional mechanisms of interest and greater insight into experimental results.
danRerLib, along with comprehensive documentation and tutorials, is freely available. The source code is available at https://github.com/sdsucomptox/danrerlib/ with associated documentation and tutorials at https://sdsucomptox.github.io/danrerlib/. The package has been developed with Python 3.9 and is available for installation on the package management systems PIP (https://pypi.org/project/danrerlib/) and Conda (https://anaconda.org/sdsu_comptox/danrerlib) with additional installation instructions on the documentation website.
了解差异基因表达背后的途径和生物学过程是表征基因表达变化以响应实验条件的基础。斑马鱼的转录组与人类转录组非常相似，常被用作人类发育和疾病的模型。然而，由于斑马鱼途径和生物学过程的注释不完整，而人类有更全面的注释，这就产生了一个挑战。这种不完整性可能导致功能富集结果有偏差以及知识的丢失。danRerLib是一个供斑马鱼转录组学研究人员使用的通用Python包，它克服了这一挑战，并提供了一套可在Python中执行的工具，包括基因ID映射、斑马鱼和人类分类学的直系同源映射，以及利用最新更新的基因本体论（GO）和京都基因与基因组百科全书（KEGG）数据库进行功能富集分析。即使GO和KEGG途径缺乏直接的斑马鱼注释，danRerLib也能通过人类注释的功能注释的直系同源性对其进行功能富集分析。这种方法使研究人员能够将分析扩展到更广泛的途径，阐明更多感兴趣的机制，并更深入地了解实验结果。
danRerLib以及全面的文档和教程都是免费提供的。源代码可在https://github.com/sdsucomptox/danrerlib/获取，相关文档和教程可在https://sdsucomptox.github.io/danrerlib/获取。该包是用Python 3.9开发的，可在包管理系统PIP（https://pypi.org/project/danrerlib/）和Conda（https://anaconda.org/sdsu_comptox/danrerlib）上安装，文档网站上还有额外的安装说明。
