但凡是做过基因表达数据分析的（芯片、RNA-seq，scRNA-seq），肯定是跑过基因集功能注释和通路富集的，因为它是研究未知基因集的利器。
但跑过之后老板肯定会给反馈，通常得到的注释都是没有太多意义的，偶尔能随缘得到一些满意的注释，所以常见的注释数据库是有显而易见的缺点的。
而往往我们是在验证时才使用注释，这种拿不准确数据来验证新的数据的方法确实值得思考。
那么GO和KEGG常见注释库到底有些什么缺点呢？
那就不得不去了解GO、KEGG是怎么来的
The Gene Ontology Consortium (GOC) uses two further evidence codes to describe experimental support for an annotation:
IMP (Inferred by mutant phenotype),
and IPI (Inferred by physical interaction).
The consortium uses other evidence codes to describe inferences used in annotations that are not supported by direct experimental evidence, but these will not be considered in this discussion (http://www.geneontology.org/GO.evidence.shtml).
First, each KO record is re-examined and associated with protein sequence data used in experiments of functional characterization.
Second, the GENES database now includes viruses, plasmids, and the addendum category for functionally characterized proteins that are not represented in complete genomes.
Third, new automatic annotation servers, BlastKOALA and GhostKOALA, are made available utilizing the non-redundant pangenome data set generated from the GENES database.
我的答案：
显然生物体内的所有基因表达是一个动态的网络
像GO这种静态的树状结构是会丢失大部分信息，树结构和网络结构有天壤之别。
像KEGG这种虽然是网状结构，但是也只是一个小的局部静态网络，必然会丢失一些全局的、动态的信息。
也就是对基因的划分不能静态，实际上我们也很难真正研究一个基因的功能，因为牵一发而动全身，这就是为什么仅仅敲除一个基因会带来如此大的连锁效应！
看文章：Gene Ontology annotations: what they mean and where they come from
KEGG as a reference resource for gene and protein annotation