New insights from existing sequence data: generating breakthroughs without a pipette

mirnaQC: a webserver for comparative quality control of miRNA-seq data

Although miRNA-seq is extensively used in many different fields, its quality control is frequently restricted to a PhredScore-based filter. Other important quality related aspects like microRNA yield, the fraction of putative degradation products (such as rRNA fragments) or the percentage of adapter-dimers are hard to assess using absolute thresholds. Here we present mirnaQC, a webserver that relies on 34 quality parameters to assist in miRNA-seq quality control. To improve their interpretability, quality attributes are ranked using a reference distribution obtained from over 36 000 publicly available miRNA-seq datasets. Accepted input formats include FASTQ and SRA accessions. The results page contains several sections that deal with putative technical artefacts related to library preparation, sequencing, contamination or yield. Different visualisations, including PCA and heatmaps, are available to help users identify underlying issues. Finally, we show the usefulness of this approach by analysing two publicly available datasets and discussing the different quality issues that can be detected using mirnaQC.

An interactive environment for agile analysis and visualization of ChIP-sequencing data

To empower experimentalists with a means for fast and comprehensive chromatin immunoprecipitation sequencing (ChIP-seq) data analyses, we introduce an integrated computational environment, EaSeq. The software combines the exploratory power of genome browsers with an extensive set of interactive and user-friendly tools for genome-wide abstraction and visualization. It enables experimentalists to easily extract information and generate hypotheses from their own data and public genome-wide datasets. For demonstration purposes, we performed meta-analyses of public Polycomb ChIP-seq data and established a new screening approach to analyze more than 900 datasets from mouse embryonic stem cells for factors potentially associated with Polycomb recruitment. EaSeq, which is freely available and works on a standard personal computer, can substantially increase the throughput of many analysis workflows, facilitate transparency and reproducibility by automatically documenting and organizing analyses, and enable a broader group of scientists to gain insights from ChIP-seq data.

Long-range RNA pairings contribute to mutually exclusive splicing

Mutually exclusive splicing is an important means of increasing the protein repertoire, by which the Down's syndrome cell adhesion molecule (Dscam) gene potentially generates 38,016 different isoforms in Drosophila melanogaster. However, the regulatory mechanisms remain obscure due to the complexity of the Dscam exon cluster. Here, we reveal a molecular model for the regulation of the mutually exclusive splicing of the serpent pre-mRNA based on competition between upstream and downstream RNA pairings. Such dual RNA pairings confer fine tuning of the inclusion of alternative exons. Moreover, we demonstrate that the splicing outcome of alternative exons is mediated in relative pairing strength-correlated mode. Combined comparative genomics analysis and experimental evidence revealed similar bidirectional structural architectures in exon clusters 4 and 9 of the Dscam gene. Our findings provide a novel mechanistic framework for the regulation of mutually exclusive splicing and may offer potentially applicable insights into long-range RNA-RNA interactions in gene regulatory networks.

Determining exon connectivity in complex mRNAs by nanopore sequencing

Short-read high-throughput RNA sequencing, though powerful, is limited in its ability to directly measure exon connectivity in mRNAs that contain multiple alternative exons located farther apart than the maximum read length. Here, we use the Oxford Nanopore MinION sequencer to identify 7,899 ‘full-length’ isoforms expressed from four Drosophila genes, Dscam1, MRP, Mhc, and Rdl. These results demonstrate that nanopore sequencing can be used to deconvolute individual isoforms and that it has the potential to be a powerful method for comprehensive transcriptome characterization.

Downregulation of reversion-inducing cysteine-rich protein with Kazal motifs in malignant melanoma: inverse correlation with membrane-type 1-matrix metalloproteinase and tissue inhibitor of metalloproteinase 2

The invasive phenotype of many tumors is associated with an imbalance between the matrix metalloproteinases (MMPs) and their inhibitors, tissue inhibitors of metalloproteinases (TIMPs), and the membrane-anchored reversion-inducing cysteine-rich protein with Kazal motifs (RECK). RECK inhibits MMP-2, MMP-9, and MT1-MMP, and has been linked to patient survival and better prognosis in several types of tumors. However, despite the wide implication of these MMPs in melanoma establishment and progression, the role of RECK in this type of tumor is still unknown. Here, we analyzed the expression of RECK, TIMP1, TIMP2, TIMP3, MT1MMP, MMP2, and MMP9 in two publicly available melanoma microarray datasets and in a panel of human melanoma cell lines. We found that RECK is downregulated in malignant melanoma, accompanied by upregulation of MT1MMP and TIMP2. In both datasets, we observed that the group of samples displaying higher RECK levels show lower median expression levels of MT1MMP and TIMP2 and higher levels of TIMP3. When tested in a sample-wise manner, these correlations were statistically significant. Inverse correlations between RECK, MT1MMP, and TIMP2 were verified in a panel of human melanoma cell lines and in a further reduced model that includes a pair of matched primary tumor-derived and metastasis-derived cell lines. Taken together, our data indicate a consistent correlation between RECK, MT1MMP, and TIMP2 across different models of clinical samples and cell lines and suggest evidence of the potential use of this subset of genes as a gene signature for diagnosing melanoma.

Unlocking Triticeae genomics to sustainably feed the future

The tribe Triticeae includes the major crops wheat and barley. Within the last few years, the whole genomes of four Triticeae species-barley, wheat, Tausch's goatgrass (Aegilops tauschii) and wild einkorn wheat (Triticum urartu)-have been sequenced. The availability of these genomic resources for Triticeae plants and innovative analytical applications using next-generation sequencing technologies are helping to revitalize our approaches in genetic work and to accelerate improvement of the Triticeae crops. Comparative genomics and integration of genomic resources from Triticeae plants and the model grass Brachypodium distachyon are aiding the discovery of new genes and functional analyses of genes in Triticeae crops. Innovative approaches and tools such as analysis of next-generation populations, evolutionary genomics and systems approaches with mathematical modeling are new strategies that will help us discover alleles for adaptive traits to future agronomic environments. In this review, we provide an update on genomic tools for use with Triticeae plants and Brachypodium and describe emerging approaches toward crop improvements in Triticeae.

Counting on co-transcriptional splicing

Splicing is the removal of intron sequences from pre-mRNA by the spliceosome. Researchers working in multiple model organisms – notably yeast, insects and mammalian cells – have shown that pre-mRNA can be spliced during the process of transcription (i.e. co-transcriptionally), as well as after transcription termination (i.e. post-transcriptionally). Co-transcriptional splicing does not assume that transcription and splicing machineries are mechanistically coupled, yet it raises this possibility. Early studies were based on a limited number of genes, which were often chosen because of their experimental accessibility. Since 2010, eight studies have used global datasets as counting tools, in order to quantify co-transcriptional intron removal. The consensus view, based on four organisms, is that the majority of splicing events take place co-transcriptionally in most cells and tissues. Here, we discuss the nature of the various global datasets and how bioinformatic analyses were conducted. Considering the broad differences in experimental approach and analysis, the level of agreement on the prevalence of co-transcriptional splicing is remarkable.