Differential meta-analysis of RNA-seq data from multiple studies

High-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. We 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. To conclude, the 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 metaRNASeq is available on the R Forge

Distinct and stage-specific contributions of TET1 and TET2 to stepwise cytosine oxidation in the transition from naive to primed pluripotency

Cytosine DNA bases can be methylated by DNA methyltransferases and subsequently oxidized by TET proteins. The resulting 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) are considered demethylation intermediates as well as stable epigenetic marks. To dissect the contributions of these cytosine modifying enzymes, we generated combinations of Tet knockout (KO) embryonic stem cells (ESCs) and systematically measured protein and DNA modification levels at the transition from naive to primed pluripotency. Whereas the increase of genomic 5-methylcytosine (5mC) levels during exit from pluripotency correlated with an upregulation of the de novo DNA methyltransferases DNMT3A and DNMT3B, the subsequent oxidation steps turned out to be far more complex. The strong increase of oxidized cytosine bases (5hmC, 5fC, and 5caC) was accompanied by a drop in TET2 levels, yet the analysis of KO cells suggested that TET2 is responsible for most 5fC formation. The comparison of modified cytosine and enzyme levels in Tet KO cells revealed distinct and differentiation-dependent contributions of TET1 and TET2 to 5hmC and 5fC formation arguing against a processive mechanism of 5mC oxidation. The apparent independent steps of 5hmC and 5fC formation suggest yet to be identified mechanisms regulating TET activity that may constitute another layer of epigenetic regulation

Correlation analysis of the transcriptome of growing leaves with mature leaf parameters in a maize RIL population

Background: To sustain the global requirements for food and renewable resources, unraveling the molecular networks underlying plant growth is becoming pivotal. Although several approaches to identify genes and networks involved in final organ size have been proven successful, our understanding remains fragmentary. Results: Here, we assessed variation in 103 lines of the Zea mays B73xH99 RIL population for a set of final leaf size and whole shoot traits at the seedling stage, complemented with measurements capturing growth dynamics, and cellular measurements. Most traits correlated well with the size of the division zone, implying that the molecular basis of final leaf size is already defined in dividing cells of growing leaves. Therefore, we searched for association between the transcriptional variation in dividing cells of the growing leaf and final leaf size and seedling biomass, allowing us to identify genes and processes correlated with the specific traits. A number of these genes have a known function in leaf development. Additionally, we illustrated that two independent mechanisms contribute to final leaf size, maximal growth rate and the duration of growth. Conclusions: Untangling complex traits such as leaf size by applying in-depth phenotyping allows us to define the relative contributions of the components and their mutual associations, facilitating dissection of the biological processes and regulatory networks underneath

OsDCL1a activation impairs phytoalexin biosynthesis and compromises disease resistance in rice

MicroRNAs (miRNAs) are small non-coding RNAs that act as post-transcriptional regulators of gene expression via sequence-specific cleavage or translational repression of target transcripts. They are transcribed as long single-stranded RNA precursors with unique stem-loop structures that are processed by a DICER-Like (DCL) ribonuclease, typically DCL1, to produce mature miRNAs. Although a plethora of miRNAs have been found to be regulated by pathogen infection in plants, the biological function of most miRNAs remains largely unknown. Here, the contribution of OsDCL1 to rice immunity was investigated. OsDCL1a activation enhances susceptibility to infection by fungal pathogens in rice. Activation of OsDCL1a represses the pathogen-inducible host defence response and negatively regulates diterpenoid phytoalexin production. These findings provide a basis to understand the molecular mechanisms through which OsDCL1a mediates rice immunity

MuSERA: Multiple sample enriched region assessment

Enriched region (ER) identification is a fundamental step in several next-generation sequencing (NGS) experiment types. Yet, although NGS experimental protocols recommend producing replicate samples for each evaluated condition and their consistency is usually assessed, typically pipelines for ER identification do not consider available NGS replicates. This may alter genome-wide descriptions of ERs, hinder significance of subsequent analyses on detected ERs and eventually preclude biological discoveries that evidence in replicate could support. MuSERA is a broadly useful stand-alone tool for both interactive and batch analysis of combined evidence from ERs in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser.We review MuSERA rationale and implementation, and illustrate how sets of significant ERs are expanded by applying MuSERA on replicates for several types of NGS data, including ChIP-seq of transcription factors or histone marks and DNase-seq hypersensitive sites. We show that MuSERA can determine a new, enhanced set of ERs for each sample by locally combining evidence on replicates, and prove how the easy-to-use interactive graphical displays and quantitative evaluations that MuSERA provides effectively support thorough inspection of obtained results and evaluation of their biological content, facilitating their understanding and biological interpretations. MuSERA is freely available at http://www.bioinformatics.deib.polimi.it/MuSERA/