Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA.
Genome Biol. 2021 Feb 2;22(1):55. doi: 10.1186/s13059-021-02264-8.
A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level. Here, we introduce GeneWalk ( github.com/churchmanlab/genewalk ) that identifies individual genes and their relevant functions critical for the experimental setting under examination. After the automatic assembly of an experiment-specific gene regulatory network, GeneWalk uses representation learning to quantify the similarity between vector representations of each gene and its GO annotations, yielding annotation significance scores that reflect the experimental context. By performing gene- and condition-specific functional analysis, GeneWalk converts a list of genes into data-driven hypotheses.
高通量功能基因组学实验中的一个瓶颈是从基因命中列表中识别最重要的基因及其相关功能。基因本体论 (GO) 富集方法提供了在基因集水平上的深入了解。在这里，我们介绍了 GeneWalk（github.com/churchmanlab/genewalk），它可以识别对于正在检查的实验设置至关重要的单个基因及其相关功能。在自动组装特定于实验的基因调控网络之后，GeneWalk 使用表示学习来量化每个基因及其 GO 注释的向量表示之间的相似性，从而产生反映实验背景的注释显着性分数。通过执行基因和条件特异性功能分析，GeneWalk 将基因列表转换为数据驱动的假设。
