During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.

An R package metaMA is available on the CRAN.

BackgroundHigh-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question.ResultsWe demonstrate how p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies.ConclusionsThe p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package is available on the CRAN (http://cran.r-project.org/web/packages/metaRNASeq).

Purpose: Whole-genome sequencing has revealed MYD88 L265P and CXCR4 mutations (CXCR4 mut ) as the most prevalent somatic mutations in Waldenstr€ om macroglobulinemia. CXCR4 mutation has proved to be of critical importance in Waldenstr€ om macroglobulinemia, in part due to its role as a mechanism of resistance to several agents. We have therefore sought to unravel the different aspects of CXCR4 mutations in Waldenstr€ om macroglobulinemia.Experimental Design: We have scanned the two coding exons of CXCR4 in Waldenstr€ om macroglobulinemia using deep nextgeneration sequencing and Sanger sequencing in 98 patients with Waldenstr€ om macroglobulinemia and correlated with SNP array landscape and mutational spectrum of eight candidate genes involved in TLR, RAS, and BCR pathway in an integrative study.Results: We found all mutations to be heterozygous, somatic, and located in the C-terminal domain of CXCR4 in 25% of the Waldenstr€ om macroglobulinemia. CXCR4 mutations led to a truncated receptor protein associated with a higher expression of CXCR4. CXCR4 mutations pertain to the same clone as to MYD88 L265P mutations but were mutually exclusive to CD79A/CD79B mutations (BCR pathway). We identified a genomic signature in CXCR4 mut Waldenstr€ om macroglobulinemia traducing a more complex genome. CXCR4 mutations were also associated with gain of chromosome 4, gain of Xq, and deletion 6q.Conclusions: Our study panned out new CXCR4 mutations in Waldenstr€ om macroglobulinemia and identified a specific signature associated to CXCR4 mut , characterized with complex genomic aberrations among MYD88L265P Waldenstr€ om macroglobulinemia. Our results suggest the existence of various genomic subgroups in Waldenstr€ om macroglobulinemia.