How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%–40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.     

Gene amplification and expression by RNA viruses and potential for further application to plant gene transfer

The great majority of plant viruses encapsidate messenger-sense ssRNA and have no natural DNA phase in their life cycle. Despite their RNA nature, essentially any desired change can be introduced into such genomes by using recombinant DNA techniques with suitably constructed, expressible viral cDNA clones. For some viruses such as brome mosaic virus, these methods have been used to define the sequences controlling RNA-directed genomic RNA replication and the expression of internal genes via subgenomic mRNAs. The results suggest a surprising degree of genetic flexibility, which appears to be reflected in the varied gene complements and genetic organizations of presumably related plant and animal RNA viruses sharing conserved replication genes. Foreign genes inserted in such RNA virus genomes can be amplified and expressed to a high level in transfected plant cells. In addition to the potential use of such viruses as episomal expression vectors, it should be possible to couple the viral pathways of RNA-dependent RNA synthesis to amplify and to further regulate the expression of genes transformed into plant chromosomes.     

Optimum experimental design for Free‐Air Carbon dioxide Enrichment (FACE) studies

This article presents the logical reasoning underlying the optimal design of an experiment. We used Free‐Air Carbon dioxide Enrichment (FACE) experiments to illustrate this trade‐off as such experiments are particularly costly. On a theoretical basis, two‐way nested designs and split‐plot designs have similar power in testing carbon dioxide (CO₂) main effects. If researchers have the choice of adding two replicate rings or two control plots to their experiment, our results show that both options provide a substantial gain in statistical power, with a slightly greater gain in the former case and at reduced financial cost in the latter. The former option, however, provides an insurance against possible ring failure. On an empirical basis, we analysed a preliminary FACE photosynthesis dataset collected at Duke University. The experiment was designed as a split‐plot design to test the effects of growth environment (GROWTH) and measurement CO₂ concentration (MEAS) on photosynthetic rates of loblolly pine. Although a significant effect of MEAS was observed, we failed to detect a significant main effect of GROWTH. Power analysis was used to understand why the GROWTH main effect was not significant. The minimum detectable difference between treatment means that we calculated for GROWTH in this experiment was 4.04 μmol CO₂ m^?2 s^?1 for a statistical power of 0.90, whereas the observed difference was 0.16 μmol CO₂ m^?2 s^?1. Our recommendations for the design of FACE experiments are: (i) consider a second treatment factor with many levels within each ring in order to obtain a split‐plot design that provides a powerful test of interaction between treatment factors; (ii) add control plots, unless financial constrictions disallow for necessary personnel; (3) pool the data of FACE experiments conducted in comparable ecosystems (e.g. forests or grasslands), with two rings per treatment level at each site.     

RNA干扰技术在抗丙型病毒性肝炎中的应用

RNA干扰技术可以特异性敲低或关闭特定基因的表达,已经在生命科学的各基础研究领域中得到广泛应用,并且在抗病毒和肿瘤治疗等药物开发研究中具有良好的应用前景。近年来,RNA干扰技术被逐渐用于抗丙型病毒性肝炎的研究,不仅可以特异性地阻断丙型肝炎病毒的复制和表达,而且还可以特异性地阻断与丙型肝炎病毒结合的蛋白的复制和表达。我们简要综述了近年来RNA干扰技术在此方面的应用。     

The real cost of sequencing: higher than you think!

Advances in sequencing technology have led to a sharp decrease in the cost of ''data generation''. But is this sufficient to ensure cost-effective and efficient ''knowledge generation''?     

固始鸡1日龄和36周龄下丘脑高丰度差异性MiRNA的鉴定及功能预测分析

为鉴定鸡下丘脑发育相关特异性表达miRNA,基于固始鸡1日龄和36周龄下丘脑小RNA的Solexa测序数据,共鉴定到266种2个发育阶段共表达的miRNA,其中157种miRNA的表达水平被显著下调,22种被显著上调.聚类分析显示,鸡下丘脑高丰度差异性miRNA主要集中于let-7、mir-181、mir-30、mir-99、mir-1和mir-17等基因家族.另外,预测了10种高丰度差异性miRNA的靶基因,并进行了相应的GO分析和KEGG通路分析.结果显示,预测靶基因在发育过程、代谢过程、细胞过程和生物学过程调节等4个生物学过程以及细胞周期、粘着斑、TGF-beta信号通路和MAPK信号通路等通路中显著富集.研究结果为进一步揭示miRNA调控鸡下丘脑发育的分子机制提供了有益线索.     

新一代高通量RNA测序数据的处理与分析

随着新一代高通量DNA测序技术的快速发展,RNA测序(RNA-seq)已成为基因表达和转录组分析新的重要手段．RNA-seq技术产生的海量数据为生物信息学带来了新的机遇和挑战．有效地对测序数据进行针对性的生物信息学处理和分析,成为RNA-seq技术能否在科学探索中发挥重大作用的关键．以新一代Illumina/Solexa测序平台所产生的数据为例,在扼要介绍高通量RNA-seq测序流程的基础上,对RNA-seq数据处理和分析的方法和现有软件做一个较为全面的综述,并对其中有待进一步研究的问题进行展望．     

AllelicImbalance: an R/ bioconductor package for detecting,managing, and visualizing allele expression imbalance data from RNA sequencing

Background

One aspect in which RNA sequencing is more valuable than microarray-based methods is the ability to examine the allelic imbalance of the expression of a gene. This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms. Allelic imbalance analysis is subject to technical biases, due to differences in the sequences of the measured alleles. Flexible bioinformatics tools are needed to ease the workflow while retaining as much RNA sequencing information as possible throughout the analysis to detect and address the possible biases.

Results

We present AllelicImblance, a software program that is designed to detect, manage, and visualize allelic imbalances comprehensively. The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing. The visualization features can reveal notable, non-trivial allelic imbalance behavior over specific regions, such as exons.

Conclusions

The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0620-2) contains supplementary material, which is available to authorized users.     

单细胞RNA测序数据分析方法研究进展

单细胞RNA测序(Single cell RNA sequencing,scRNA-Seq)已经广泛应用于细胞分化、肿瘤微环境及多种疾病病因学研究。目前,由于scRNA-Seq具有低捕获率、高噪声、高变异性等特点,通过优化数据分析方法提高测序结果准确性已经成为测序领域的研究热点。对近年来数据分析过程中利用的数学方法进行了总结,讨论了数据分析的优势及存在的问题,以期为新算法的开发和应用提供参考,逐步提高测序结果的可靠性。