志贺菌耐药性与非编码RNA的关系研究

采用RNA-seq技术对志贺菌耐药性与非编码RNA之间的关系进行研究。首先采用梯度剂量的环丙沙星诱导福氏志贺菌2a标准菌株产生耐药性而获得耐药菌株,然后提取标准菌株与耐药菌株的总RNA进行RNA-seq分析。结果显示,在福氏志贺菌的2个样品中共发现100个候选sRNA,并与sRNA的数据库比对得到注释信息,每个候选sRNA都对应着很多个靶标基因,而且靶标基因的表达也有明显差异。预测这些sRNA可能会通过调控耐药基因和一些与细胞增殖或凋亡相关的基因发挥作用,因此还需要挑选合适的候选sRNA并对其用荧光定量PCR实验进行验证。     

Analysis and design of RNA sequencing experiments for identifying isoform regulation

Through alternative splicing, most human genes express multiple isoforms that often differ in function. To infer isoform regulation from high-throughput sequencing of cDNA fragments (RNA-seq), we developed the mixture-of-isoforms (MISO) model, a statistical model that estimates expression of alternatively spliced exons and isoforms and assesses confidence in these estimates. Incorporation of mRNA fragment length distribution in paired-end RNA-seq greatly improved estimation of alternative-splicing levels. MISO also detects differentially regulated exons or isoforms. Application of MISO implicated the RNA splicing factor hnRNP H1 in the regulation of alternative cleavage and polyadenylation, a role that was supported by UV cross-linking-immunoprecipitation sequencing (CLIP-seq) analysis in human cells. Our results provide a probabilistic framework for RNA-seq analysis, give functional insights into pre-mRNA processing and yield guidelines for the optimal design of RNA-seq experiments for studies of gene and isoform expression.     

非编码RNA与基因表达调控

近年来,随着对基因组的深入研究,发现真核生物中存在许多形态和功能各异的非编码RNA分子,这类RNA分子并不表达蛋白质,但它们在基因转录水平、转录后水平及翻译水平起了重要的调控作用。具有调控作用的RNA分子种类非常丰富,如长链非编码RNA(long non-coding RNA,lncRNA)、miRNA、PIWI相互作用RNA(PIWI-interacting RNA,piRNA)、内源性小干扰RNA(endogenous small interfering RNA,endo-siRNA)、竞争性内源RNA(competitive endogenous RNA,ceRNA)等,它们使基因表达过程更为丰富、严谨和有序。本文综述几类典型的非编码RNA对基因表达的调节作用,以助于理解细胞中RNA分子调节网络的功能和机制。     

Allelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies

Background

RNA sequencing (RNA-seq) is the current gold-standard method to quantify gene expression for expression quantitative trait locus (eQTL) studies. However, a potential caveat in these studies is that RNA-seq reads carrying the non-reference allele of variant loci can have lower probability to map correctly to the reference genome, which could bias gene quantifications and cause false positive eQTL associations. In this study, we analyze the effect of this allelic mapping bias in eQTL discovery.

Results

We simulate RNA-seq read mapping over 9.5 M common SNPs and indels, with 15.6% of variants showing biased mapping rate for reference versus non-reference reads. However, removing potentially biased RNA-seq reads from an eQTL dataset of 185 individuals has a very small effect on gene and exon quantifications and eQTL discovery. We detect only a handful of likely false positive eQTLs, and overall eQTL SNPs show no significant enrichment for high mapping bias.

Conclusion

Our results suggest that RNA-seq quantifications are generally robust against allelic mapping bias, and that this does not have a severe effect on eQTL discovery. Nevertheless, we provide our catalog of putatively biased loci to allow better controlling for mapping bias to obtain more accurate results in future RNA-seq studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0467-2) contains supplementary material, which is available to authorized users.     

Detecting microRNA activity from gene expression data

Background  

MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.