Application of Computational Biology to Decode Brain Transcriptomes
- Author:
Li JIE
1
;
Wang GUANG-ZHONG
Author Information
1. CAS Key Laboratory of Computational Biology
- Keywords:
Brain transcriptome atlas;
Computational analysis;
Spatiotemporal pattern;
Coexpression analysis;
Single-cell analysis
- From:
Genomics, Proteomics & Bioinformatics
2019;17(4):367-380
- CountryChina
- Language:Chinese
-
Abstract:
The rapid development of high-throughput sequencing technologies has generated massive valuable brain transcriptome atlases, providing great opportunities for systematically investigating gene expression characteristics across various brain regions throughout a series of developmental stages. Recent studies have revealed that the transcriptional architecture is the key to interpreting the molecular mechanisms of brain complexity. However, our knowledge of brain transcriptional characteristics remains very limited. With the immense efforts to generate high-quality brain transcriptome atlases, new computational approaches to analyze these highdimensional multivariate data are greatly needed. In this review, we summarize some public resources for brain transcriptome atlases and discuss the general computational pipelines that are commonly used in this field, which would aid in making new discoveries in brain development and disorders.
