limma powers differential expression analyses for RNA-sequencing and microarray studies

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.     

生物信息学基因表达差异分析

基因的差异化表达由多种因素共同导致,并且与许多疾病的发生和发展有密切联系,对差异化表达的基因进行生物信息学以及生物统计学的分析对于研究细胞调节机制和疾病机理有着重要意义。目前,对差异化表达的基因有以下几种主流的研究方法：DNA微阵列（DNA microarray）,抑制性消减杂交（SSH）,基因表达连续性分析（SAGE）,代表性差异分析（RDA）,以及mRNA差异显示PCR（mRNA DDRT-PCR）。目前许多基因差异化表达数据是建立在时段（time series）基础上,因此对基于时间变化的基因差异化表达分析变得尤为重要。本文将对差异化表达基因的几种主流方法进行详细阐述,并介绍一种基于傅里叶函数的时段基因差异化表达分析。     

Robust method for detecting differential gene expression in twin studies

MOTIVATION: A steadily increasing number of experiments with microarrays stimulate the further development of the statistical methods of the analysis of gene expression data. One of the central problems in this area is detecting differential gene expression under two or more conditions. Unfortunately, up to now it has not been studied how the correlations between related individuals, such as twins influence the estimates of differential gene expression. RESULTS: In this paper, we discuss this problem and propose a new method that is robust with respect to correlations of gene expression data for twins.     

Targeted sequence capture as a powerful tool for evolutionary analysis1

Next-generation sequencing technologies (NGS) have revolutionized biological research by significantly increasing data generation while simultaneously decreasing the time to data output. For many ecologists and evolutionary biologists, the research opportunities afforded by NGS are substantial; even for taxa lacking genomic resources, large-scale genome-level questions can now be addressed, opening up many new avenues of research. While rapid and massive sequencing afforded by NGS increases the scope and scale of many research objectives, whole genome sequencing is often unwarranted and unnecessarily complex for specific research questions. Recently developed targeted sequence enrichment, coupled with NGS, represents a beneficial strategy for enhancing data generation to answer questions in ecology and evolutionary biology. This marriage of technologies offers researchers a simple method to isolate and analyze a few to hundreds, or even thousands, of genes or genomic regions from few to many samples in a relatively efficient and effective manner. These strategies can be applied to questions at both the infra- and interspecific levels, including those involving parentage, gene flow, divergence, phylogenetics, reticulate evolution, and many more. Here we provide a brief overview of targeted sequence enrichment, and emphasize the power of this technology to increase our ability to address a wide range of questions of interest to ecologists and evolutionary biologists, particularly for those working with taxa for which few genomic resources are available.     

FunSys: Software for functional analysis of prokaryotic transcriptome and proteome

The vast amount of data produced by next-generation sequencing (NGS) has necessitated the development of computational tools to assist in understanding the myriad functions performed by the biological macromolecules involved in heredity. In this work, we developed the FunSys programme, a stand-alone tool with an user friendly interface that enables us to evaluate and correlate differential expression patterns from RNA sequencing and proteomics datasets. The FunSys generates charts and reports based on the results of the analysis of differential expression to aid the interpretation of the results. AVAILABILITY: The database is available for free at https://sourceforge.net/projects/funsysufpa/     

Comparative analysis of gene expression between Babesia bovis blood stages and kinetes allowed by improved genome annotation

Throughout their life cycle, Babesia parasites alternate between a mammalian host, where they cause babesiosis, and the tick vector. Transition between hosts results in distinct environmental signals that influence patterns of gene expression, consistent with the morphological and functional changes operating in the parasites during their life stages. In addition, comparing differential patterns of gene expression among mammalian and tick parasite stages can provide clues for developing improved methods of control. Hereby, we upgraded the genome assembly of Babesia bovis, a bovine hemoparasite, closing a 139 kbp gap, and used RNA-Seq datasets derived from mammalian blood and tick kinete stages to update the genome annotation. Of the originally annotated genes, 1,254 required structural changes, and 326 new genes were identified, leading to a different predicted proteome compared to the original annotation. Next, the RNA-Seq data was used to identify B. bovis genes that were differentially expressed in the vertebrate and arthropod hosts. In blood stages, 28% of the genes were upregulated up to 300 fold, whereas 26% of the genes in kinetes, a tick stage, were upregulated up to >19,000 fold. We thus discovered differentially expressed genes that may play key biological roles and serve as suitable targets for the development of vaccines to control bovine babesiosis.     

Power estimation of the t test for detecting differential gene expression

There exist now a number of statistical methods for detecting differential gene expression in experiments with microarray data. In trials under two conditions, a version of the two-sample t statistic is usually used. However, the problem of estimating the power for these tests has so far been insufficiently studied. In this paper, we propose a method to calculate the power of the robust t test for detecting differential gene expression in experiments with twins. We discuss also the results of the implementation of this method to simulated data.     

DNA barcoding and minibarcoding as a powerful tool for feather mite studies

Feather mites (Astigmata: Analgoidea and Pterolichoidea) are among the most abundant and commonly occurring bird ectosymbionts. Basic questions on the ecology and evolution of feather mites remain unanswered because feather mite species identification is often only possible for adult males, and it is laborious even for specialized taxonomists, thus precluding large‐scale identifications. Here, we tested DNA barcoding as a useful molecular tool to identify feather mites from passerine birds. Three hundred and sixty‐one specimens of 72 species of feather mites from 68 species of European passerine birds from Russia and Spain were barcoded. The accuracy of barcoding and minibarcoding was tested. Moreover, threshold choice (a controversial issue in barcoding studies) was also explored in a new way, by calculating through simulations the effect of sampling effort (in species number and species composition) on threshold calculations. We found one 200‐bp minibarcode region that showed the same accuracy as the full‐length barcode (602 bp) and was surrounded by conserved regions potentially useful for group‐specific degenerate primers. Species identification accuracy was perfect (100%) but decreased when singletons or species of the Proctophyllodes pinnatus group were included. In fact, barcoding confirmed previous taxonomic issues within the P. pinnatus group. Following an integrative taxonomy approach, we compared our barcode study with previous taxonomic knowledge on feather mites, discovering three new putative cryptic species and validating three previous morphologically different (but still undescribed) new species.     

Massively parallel signature sequencing (MPSS) as a tool for in-depth quantitative gene expression profiling in all organisms.

Massively parallel signature sequencing (MPSS) is one of the newest tools available for conducting in-depth expression profiling. MPSS is an open-ended platform that analyses the level of expression of virtually all genes in a sample by counting the number of individual mRNA molecules produced from each gene. There is no requirement that genes be identified and characterised prior to conducting an experiment. MPSS has a routine sensitivity at a level of a few molecules of mRNA per cell, and the datasets are in a digital format that simplifies the management and analysis of the data. Therefore, of the various microarray and non-microarray technologies currently available, MPSS provides many advantages for generating the type of complete datasets that will help to facilitate hypothesis-driven experiments in the era of digital biology.     

Magnetic resonance spectroscopy: a powerful tool for drug metabolism studies.

Studies on the metabolism and disposition of drugs using nuclear magnetic resonance spectroscopy (MRS) as the analytical technique are reviewed. An overview of the main studies classed in terms of the observed magnetic nucleus (1H, 2H, 7Li, 13C, 19F, 31P, 77Se) is followed by some typical examples of the way in which 19F and 31P MRS can be profitably employed to gain more understanding about the metabolism and disposition of the anticancer fluoropyrimidines (5-fluorouracil (FU) and its prodrugs) and ifosfamide (IF). The results of three recent studies carried out in our laboratory are developed. They concern the direct quantitative monitoring of the hepatic metabolism of FU in the isolated perfused mouse liver, the elucidation of the origin of the cardiotoxicity of FU and the metabolism of IF from an analysis of biofluids of patients. Finally, the advantages and limitations of MRS for investigations on drug metabolism are discussed.     

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.     

Cancer outlier differential gene expression detection

We study statistical methods to detect cancer genes that are over- or down-expressed in some but not all samples in a disease group. This has proven useful in cancer studies where oncogenes are activated only in a small subset of samples. We propose the outlier robust t-statistic (ORT), which is intuitively motivated from the t-statistic, the most commonly used differential gene expression detection method. Using real and simulation studies, we compare the ORT to the recently proposed cancer outlier profile analysis (Tomlins and others, 2005) and the outlier sum statistic of Tibshirani and Hastie (2006). The proposed method often has more detection power and smaller false discovery rates. Supplementary information can be found at http://www.biostat.umn.edu/~baolin/research/ort.html.     

Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq

Background

Massively parallel cDNA sequencing (RNA-seq) experiments are gradually superseding microarrays in quantitative gene expression profiling. However, many biologists are uncertain about the choice of differentially expressed gene (DEG) analysis methods and the validity of cost-saving sample pooling strategies for their RNA-seq experiments. Hence, we performed experimental validation of DEGs identified by Cuffdiff2, edgeR, DESeq2 and Two-stage Poisson Model (TSPM) in a RNA-seq experiment involving mice amygdalae micro-punches, using high-throughput qPCR on independent biological replicate samples. Moreover, we sequenced RNA-pools and compared their results with sequencing corresponding individual RNA samples.

Results

False-positivity rate of Cuffdiff2 and false-negativity rates of DESeq2 and TSPM were high. Among the four investigated DEG analysis methods, sensitivity and specificity of edgeR was relatively high. We documented the pooling bias and that the DEGs identified in pooled samples suffered low positive predictive values.

Conclusions

Our results highlighted the need for combined use of more sensitive DEG analysis methods and high-throughput validation of identified DEGs in future RNA-seq experiments. They indicated limited utility of sample pooling strategies for RNA-seq in similar setups and supported increasing the number of biological replicate samples.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1767-y) contains supplementary material, which is available to authorized users.     

Long-term monitoring studies as a powerful tool in marine ecosystem research

The EU-funded research project WISER (“Water bodies in Europe: Integrative Systems to assess Ecological status and Recovery”) developed new assessment methods required by the EU Water Framework Directive (WFD) for lakes, coastal and transitional waters. WISER also addressed the recovery of biotic assemblages from degradation. The results are summarised in five key messages, supported by papers in this special issue and by WISER results published elsewhere: (1) Response to stress differs between organism groups, water types and stressors; a conceptual model is proposed summarising how the individual organism groups respond to different types of degradation in rivers, lakes, transitional and coastal waters. (2) The sources of uncertainty differ between BQEs and water types, leading to methodological suggestions on how to design WFD sampling programmes. (3) Results from about 300 current assessment methods indicate geographical variations in metrics but assessments are comparable at an aggregated level (“ecological status”). (4) Scale and time matter; restoration requires action at (sub)-basin levels and recovery may require decades. (5) Long-term trends require consideration; the effects of both degradation and restoration at the water body or river basin scales is increasingly superimposed by multiple stressors acting at large scales, in particular by climate change.     

A unified approach for simultaneous gene clustering and differential expression identification

Although both clustering and identification of differentially expressed genes are equally essential in most microarray studies, the two tasks are often conducted without regard to each other. This is clearly not the most efficient way of extracting information. The main aim of this article is to develop a coherent statistical method that can simultaneously cluster and detect differentially expressed genes. Through information sharing between the two tasks, the proposed approach gives more sensible clustering among genes and is more sensitive in identifying differentially expressed genes. The improvement over existing methods is illustrated in both our simulation results and a case study.