背景(考古学)
基因
计算生物学
控制(管理)
生物
不变(物理)
计算机科学
遗传学
人工智能
数学
数学物理
古生物学
作者
Stephen R. Stürzenbaum,Peter Kille
标识
DOI:10.1016/s1096-4959(01)00440-7
摘要
The measurement of transcript levels constitutes the foundation of today's molecular genetics. Independent of the techniques used, quantifications are generally normalised using invariant control genes to account for sample handling, loading and experimental variation. All of the widely used control genes are evaluated, dissecting different methodological approaches and issues regarding the experimental context (e.g. development and tissue type). Furthermore, the major sources of error are highlighted when applying these techniques. Finally, different approaches undertaken to assess the invariance of control genes are critically analysed to generate a procedure that will help to discern the best control for novel experiments.
科研通智能强力驱动
Strongly Powered by AbleSci AI