Woodhouse Steven, Piterman Nir, Wintersteiger Christoph M, Göttgens Berthold, Fisher Jasmin
Department of Hematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.
Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK.
BMC Syst Biol. 2018 May 25;12(1):59. doi: 10.1186/s12918-018-0581-y.
Reconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge.
The Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages.
SCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes.
从单细胞基因表达数据重建可执行的机制模型是理解发育和疾病过程的一种强大方法。像人类细胞图谱这样新的宏伟计划很快将导致数据的爆炸式增长，这些数据有可能揭示和理解构成所有人类细胞行为基础的调控网络。然而，为了利用这些数据，需要通用的、用户友好且高效的计算工具，以便没有专业计算机科学知识的生物学家能够轻松使用。
单细胞网络合成工具包（SCNS）是一种通用的计算工具，用于从单细胞基因表达数据重建和分析可执行模型。通过图形用户界面，SCNS获取跨时间进程的单细胞定量PCR或RNA测序数据，并搜索驱动从早期细胞状态向晚期细胞状态转变的逻辑规则。由于所得的重建模型是可执行的，它们可用于预测特定基因扰动对特定谱系生成的影响。
SCNS应该会引起越来越多从事单细胞基因组学研究的人员的广泛关注，并将有助于进一步促进对发育、稳态和疾病过程产生有价值的机制性见解。
