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Contains fulltext : 201167.pdf (publisher's version ) (Open Access)

Background: Bidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single-cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single-cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single-cell technologies are not yet established, in the context of BPs. Results: We performed integrative analyses of novel human single-cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles. Conclusions: Our results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

Motivation: Protocols to generate strand-specific transcriptomes with next-generation sequencing platforms have been used by the scientific community roughly since 2008. Strand-specific reads allow for detection of antisense events and a higher resolution of expression profiles enabling extension of current transcript annotations. However, applications making use of this strandedness information are still scarce. Results: Here we present a tool (Janus), which focuses on the identification of transcriptional active regions in antisense orientation to known and novel transcribed elements of the genome. Janus can compare the antisense events of multiple samples and assigns scores to identify mutual expression of either transcript in a sense/antisense pair, which could hint to regulatory mechanisms. Janus is able to make use of single-nucleotide variant (SNV) and methylation data, if available, and reports the sense to antisense ratio of regions in the vicinity of the identified genetic and epigenetic variation. Janus interrogates positions of heterozygous SNVs to identify strand-specific allelic imbalance. Availability: Janus is written in C/C++ and freely available at http://www.ikmb.uni-kiel.de/janus/janus.html under terms of GNU General Public License, for both, Linux and Windows 64×. Although the binaries will work without additional downloads, the software depends on bamtools (https://github.com/pezmaster31/bamtools) for compilation. A detailed tutorial section is included in the first section of the supplemental material and included as brief readme.txt in the tutorial archive. Contact: m.barann@mucosa.de or p.rosenstiel@mucosa.de Supplementary information: Supplementary data are available at Bioinformatics online

Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA-and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing-alternative splice sites, introns, and cleavage sites-which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.

Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA- and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing—alternative splice sites, introns and cleavage sites—which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.

The acquisition of distinct cell fates is central to the development of multicellular organisms and is largely mediated by gene expression patterns specific to individual cells and tissues. A spatially and temporally resolved analysis of gene expression facilitates the elucidation of transcriptional networks linked to cellular identity and function. We present an approach that allows cell type-specific transcriptional profiling of distinct target cells, which are rare and difficult to access, with unprecedented sensitivity and resolution. We combined laser-assisted microdissection (LAM), linear amplification starting from <1 ng of total RNA, and RNA-sequencing (RNA-Seq). As a model we used the central cell of the Arabidopsis thaliana female gametophyte, one of the female gametes harbored in the reproductive organs of the flower. We estimated the number of expressed genes to be more than twice the number reported previously in a study using LAM and ATH1 microarrays, and identified several classes of genes that were systematically underrepresented in the transcriptome measured with the ATH1 microarray. Among them are many genes that are likely to be important for developmental processes and specific cellular functions. In addition, we identified several intergenic regions, which are likely to be transcribed, and describe a considerable fraction of reads mapping to introns and regions flanking annotated loci, which may represent alternative transcript isoforms. Finally, we performed a de novo assembly of the transcriptome and show that the method is suitable for studying individual cell types of organisms lacking reference sequence information, demonstrating that this approach can be applied to most eukaryotic organisms.

Motivation: Protocols to generate strand-specific transcriptomes with next-generation sequencing platforms have been used by the scientific community roughly since 2008. Strand-specific reads allow for detection of antisense events and a higher resolution of expression profiles enabling extension of current transcript annotations. However, applications making use of this strandedness information are still scarce. Results: Here we present a tool (Janus), which focuses on the identification of transcriptional active regions in antisense orientation to known and novel transcribed elements of the genome. Janus can compare the antisense events of multiple samples and assigns scores to identify mutual expression of either transcript in a sense/antisense pair, which could hint to regulatory mechanisms. Janus is able to make use of single-nucleotide variant (SNV) and methylation data, if available, and reports the sense to antisense ratio of regions in the vicinity of the identified genetic and epigenetic variation. Janus interrogates positions of heterozygous SNVs to identify strand-specific allelic imbalance. Availability: Janus is written in C/C++ and freely available at http://www.ikmb.uni-kiel.de/janus/janus.html under terms of GNU General Public License, for both, Linux and Windows 64×. Although the binaries will work without additional downloads, the software depends on bamtools (https://github.com/pezmaster31/bamtools) for compilation. A detailed tutorial section is included in the first section of the supplemental material and included as brief readme.txt in the tutorial archive. Contact: m.barann@mucosa.de or p.rosenstiel@mucosa.de Supplementary information: Supplementary data are available at Bioinformatics online