Integrative analyses of RNA editing,alternative splicing,and expression of young genes in human brain transcriptome by deep RNA sequencing

Next-generation RNA sequencing has been successfully used for identification of transcript assembly,evaluation of gene expression levels,and detection of post-transcriptional modifications.Despite these large-scale studies,additional comprehensive RNA-seq data from different subregions of the human brain are required to fully evaluate the evolutionary patterns experienced by the human brain transcriptome.Here,we provide a total of 6.5 billion RNA-seq reads fromdifferent subregions of the human brain.A significant correlation was observed between the levels of alternative splicing and RNA editing,which might be explained by a competition between the molecularmachineries responsible for the splicing and editing of RNA.Younghuman protein-coding genesdemonstrate biased expression to the neocortical and non-neocortical regions during evolution on the lineage leading to humans.Wealso found that a significantly greater number of young human protein-coding genes are expressed in the putamen,a tissue that was also observed to have the highest level of RNA-editing activity.The putamen,which previously received little attention,plays an important role in cognitive ability,and our data suggest a potential contribution of the putamen to human evolution.

Application of Computational Biology to Decode Brain Transcriptomes

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.

Genomics in Neurological Disorders

Neurological disorders comprise a variety of complex diseases in the central nervous system, which can be roughly classified as neurodegenerative diseases and psychiatric disorders. The basic and translational research of neurological disorders has been hindered by the difficulty in accessing the pathological center （i.e., the brain） in live patients. The rapid advancement of sequencing and array technologies has made it possible to investigate the disease mechanism and biomarkers from a systems perspective. In this review, recent progresses in the discovery of novel risk genes, treatment targets and peripheral biomarkers employing genomic technologies will be dis- cussed. Our major focus will be on two of the most heavily investigated neurological disorders, namely Alzheimer＇s disease and autism spectrum disorder.

Decoding nervous system by single-cell RNA sequencing

Background： Mammalian brain are composed of a large number of specialized cell types with diverse molecular composition, functions and differentiation potentials. The application of recently developed single-cell RNA sequencing （scRNA-seq） technology in this filed has provided us new insights about this sophisticated system, deepened our understanding of the cell type diversity and led to the discovery of novel cell types. Results： Here we review recent progresses of applying this technology on studying brain cell heterogeneity, adult neurogenesis as well as brain tumors, then we discuss some current limitations and future directions of using scRNA- seq on the investagation of nervous system. Conclusions： We believe the application of single-celi RNA sequencing in neuroscience will accelerate the progress of big brain projects.