Prediction of Poly(A) Sites by Poly(A) Read Mapping.

RNA-seq reads containing part of the poly(A) tail of transcripts (denoted as poly(A) reads) provide the most direct evidence for the position of poly(A) sites in the genome. However, due to reduced coverage of poly(A) tails by reads, poly(A) reads are not routinely identified during RNA-seq mapping. Nevertheless, recent studies for several herpesviruses successfully employed mapping of poly(A) reads to identify herpesvirus poly(A) sites using different strategies and customized programs. To more easily allow such analyses without requiring additional programs, we integrated poly(A) read mapping and prediction of poly(A) sites into our RNA-seq mapping program ContextMap 2. The implemented approach essentially generalizes previously used poly(A) read mapping approaches and combines them with the context-based approach of ContextMap 2 to take into account information provided by other reads aligned to the same location. Poly(A) read mapping using ContextMap 2 was evaluated on real-life data from the ENCODE project and compared against a competing approach based on transcriptome assembly (KLEAT). This showed high positive predictive value for our approach, evidenced also by the presence of poly(A) signals, and considerably lower runtime than KLEAT. Although sensitivity is low for both methods, we show that this is in part due to a high extent of spurious results in the gold standard set derived from RNA-PET data. Sensitivity improves for poly(A) sites of known transcripts or determined with a more specific poly(A) sequencing protocol and increases with read coverage on transcript ends. Finally, we illustrate the usefulness of the approach in a high read coverage scenario by a re-analysis of published data for herpes simplex virus 1. Thus, with current trends towards increasing sequencing depth and read length, poly(A) read mapping will prove to be increasingly useful and can now be performed automatically during RNA-seq mapping with ContextMap 2.

p53-Regulated Networks of Protein, mRNA, miRNA, and lncRNA Expression Revealed by Integrated Pulsed Stable Isotope Labeling With Amino Acids in Cell Culture (pSILAC) and Next Generation Sequencing (NGS) Analyses.

We determined the effect of p53 activation on de novo protein synthesis using quantitative proteomics (pulsed stable isotope labeling with amino acids in cell culture/pSILAC) in the colorectal cancer cell line SW480. This was combined with mRNA and noncoding RNA expression analyses by next generation sequencing (RNA-, miR-Seq). Furthermore, genome-wide DNA binding of p53 was analyzed by chromatin-immunoprecipitation (ChIP-Seq). Thereby, we identified differentially regulated proteins (542 up, 569 down), mRNAs (1258 up, 415 down), miRNAs (111 up, 95 down) and lncRNAs (270 up, 123 down). Changes in protein and mRNA expression levels showed a positive correlation (r = 0.50, p < 0.0001). In total, we detected 133 direct p53 target genes that were differentially expressed and displayed p53 occupancy in the vicinity of their promoter. More transcriptionally induced genes displayed occupied p53 binding sites (4.3% mRNAs, 7.2% miRNAs, 6.3% lncRNAs, 5.9% proteins) than repressed genes (2.4% mRNAs, 3.2% miRNAs, 0.8% lncRNAs, 1.9% proteins), suggesting indirect mechanisms of repression. Around 50% of the down-regulated proteins displayed seed-matching sequences of p53-induced miRNAs in the corresponding 3'-UTRs. Moreover, proteins repressed by p53 significantly overlapped with those previously shown to be repressed by miR-34a. We confirmed up-regulation of the novel direct p53 target genes LINC01021, MDFI, ST14 and miR-486 and showed that ectopic LINC01021 expression inhibits proliferation in SW480 cells. Furthermore, KLF12, HMGB1 and CIT mRNAs were confirmed as direct targets of the p53-induced miR-34a, miR-205 and miR-486-5p, respectively. In line with the loss of p53 function during tumor progression, elevated expression of KLF12, HMGB1 and CIT was detected in advanced stages of cancer. In conclusion, the integration of multiple omics methods allowed the comprehensive identification of direct and indirect effectors of p53 that provide new insights and leads into the mechanisms of p53-mediated tumor suppression.

ContextMap 2: fast and accurate context-based RNA-seq mapping.

BACKGROUND: Mapping of short sequencing reads is a crucial step in the analysis of RNA sequencing (RNA-seq) data. ContextMap is an RNA-seq mapping algorithm that uses a context-based approach to identify the best alignment for each read and allows parallel mapping against several reference genomes. RESULTS: In this article, we present ContextMap 2, a new and improved version of ContextMap. Its key novel features are: (i) a plug-in structure that allows easily integrating novel short read alignment programs with improved accuracy and runtime; (ii) context-based identification of insertions and deletions (indels); (iii) mapping of reads spanning an arbitrary number of exons and indels. ContextMap 2 using Bowtie, Bowtie 2 or BWA was evaluated on both simulated and real-life data from the recently published RGASP study. CONCLUSIONS: We show that ContextMap 2 generally combines similar or higher recall compared to other state-of-the-art approaches with significantly higher precision in read placement and junction and indel prediction. Furthermore, runtime was significantly lower than for the best competing approaches. ContextMap 2 is freely available at http://www.bio.ifi.lmu.de/ContextMap .

Widespread disruption of host transcription termination in HSV-1 infection.

Herpes simplex virus 1 (HSV-1) is an important human pathogen and a paradigm for virus-induced host shut-off. Here we show that global changes in transcription and RNA processing and their impact on translation can be analysed in a single experimental setting by applying 4sU-tagging of newly transcribed RNA and ribosome profiling to lytic HSV-1 infection. Unexpectedly, we find that HSV-1 triggers the disruption of transcription termination of cellular, but not viral, genes. This results in extensive transcription for tens of thousands of nucleotides beyond poly(A) sites and into downstream genes, leading to novel intergenic splicing between exons of neighbouring cellular genes. As a consequence, hundreds of cellular genes seem to be transcriptionally induced but are not translated. In contrast to previous reports, we show that HSV-1 does not inhibit co-transcriptional splicing. Our approach thus substantially advances our understanding of HSV-1 biology and establishes HSV-1 as a model system for studying transcription termination.

Mining RNA-seq data for infections and contaminations.

RNA sequencing (RNA-seq) provides novel opportunities for transcriptomic studies at nucleotide resolution, including transcriptomics of viruses or microbes infecting a cell. However, standard approaches for mapping the resulting sequencing reads generally ignore alternative sources of expression other than the host cell and are little equipped to address the problems arising from redundancies and gaps among sequenced microbe and virus genomes. We show that screening of sequencing reads for contaminations and infections can be performed easily using ContextMap, our recently developed mapping software. Based on mapping-derived statistics, mapping confidence, similarities and misidentifications (e.g. due to missing genome sequences) of species/strains can be assessed. Performance of our approach is evaluated on three real-life sequencing data sets and compared to state-of-the-art metagenomics tools. In particular, ContextMap vastly outperformed GASiC and GRAMMy in terms of runtime. In contrast to MEGAN4, it was capable of providing individual read mappings to species and resolving non-unique mappings, thus allowing the identification of misalignments caused by sequence similarities between genomes and missing genome sequences. Our study illustrates the importance and potentials of routinely mining RNA-seq experiments for infections or contaminations by microbes and viruses. By using ContextMap, gene expression of infecting agents can be analyzed and novel insights in infection processes and tumorigenesis can be obtained.

Clonal expansion analysis of transposon insertions by high-throughput sequencing identifies candidate cancer genes in a PiggyBac mutagenesis screen.

Somatic transposon mutagenesis in mice is an efficient strategy to investigate the genetic mechanisms of tumorigenesis. The identification of tumor driving transposon insertions traditionally requires the generation of large tumor cohorts to obtain information about common insertion sites. Tumor driving insertions are also characterized by their clonal expansion in tumor tissue, a phenomenon that is facilitated by the slow and evolving transformation process of transposon mutagenesis. We describe here an improved approach for the detection of tumor driving insertions that assesses the clonal expansion of insertions by quantifying the relative proportion of sequence reads obtained in individual tumors. To this end, we have developed a protocol for insertion site sequencing that utilizes acoustic shearing of tumor DNA and Illumina sequencing. We analyzed various solid tumors generated by PiggyBac mutagenesis and for each tumor >106 reads corresponding to >104 insertion sites were obtained. In each tumor, 9 to 25 insertions stood out by their enriched sequence read frequencies when compared to frequencies obtained from tail DNA controls. These enriched insertions are potential clonally expanded tumor driving insertions, and thus identify candidate cancer genes. The candidate cancer genes of our study comprised many established cancer genes, but also novel candidate genes such as Mastermind-like1 (Mamld1) and Diacylglycerolkinase delta (Dgkd). We show that clonal expansion analysis by high-throughput sequencing is a robust approach for the identification of candidate cancer genes in insertional mutagenesis screens on the level of individual tumors.

Real-time transcriptional profiling of cellular and viral gene expression during lytic cytomegalovirus infection.

During viral infections cellular gene expression is subject to rapid alterations induced by both viral and antiviral mechanisms. In this study, we applied metabolic labeling of newly transcribed RNA with 4-thiouridine (4sU-tagging) to dissect the real-time kinetics of cellular and viral transcriptional activity during lytic murine cytomegalovirus (MCMV) infection. Microarray profiling on newly transcribed RNA obtained at different times during the first six hours of MCMV infection revealed discrete functional clusters of cellular genes regulated with distinct kinetics at surprising temporal resolution. Immediately upon virus entry, a cluster of NF-κB- and interferon-regulated genes was induced. Rapid viral counter-regulation of this coincided with a very transient DNA-damage response, followed by a delayed ER-stress response. Rapid counter-regulation of all three clusters indicated the involvement of novel viral regulators targeting these pathways. In addition, down-regulation of two clusters involved in cell-differentiation (rapid repression) and cell-cycle (delayed repression) was observed. Promoter analysis revealed all five clusters to be associated with distinct transcription factors, of which NF-κB and c-Myc were validated to precisely match the respective transcriptional changes observed in newly transcribed RNA. 4sU-tagging also allowed us to study the real-time kinetics of viral gene expression in the absence of any interfering virion-associated-RNA. Both qRT-PCR and next-generation sequencing demonstrated a sharp peak of viral gene expression during the first two hours of infection including transcription of immediate-early, early and even well characterized late genes. Interestingly, this was subject to rapid gene silencing by 5-6 hours post infection. Despite the rapid increase in viral DNA load during viral DNA replication, transcriptional activity of some viral genes remained remarkably constant until late-stage infection, or was subject to further continuous decline. In summary, this study pioneers real-time transcriptional analysis during a lytic herpesvirus infection and highlights numerous novel regulatory aspects of virus-host-cell interaction.

Ultrashort and progressive 4sU-tagging reveals key characteristics of RNA processing at nucleotide resolution.

RNA synthesis and decay rates determine the steady-state levels of cellular RNAs. Metabolic tagging of newly transcribed RNA by 4-thiouridine (4sU) can reveal the relative contributions of RNA synthesis and decay rates. The kinetics of RNA processing, however, had so far remained unresolved. Here, we show that ultrashort 4sU-tagging not only provides snapshot pictures of eukaryotic gene expression but, when combined with progressive 4sU-tagging and RNA-seq, reveals global RNA processing kinetics at nucleotide resolution. Using this method, we identified classes of rapidly and slowly spliced/degraded introns. Interestingly, each class of splicing kinetics was characterized by a distinct association with intron length, gene length, and splice site strength. For a large group of introns, we also observed long lasting retention in the primary transcript, but efficient secondary splicing or degradation at later time points. Finally, we show that processing of most, but not all small nucleolar (sno)RNA-containing introns is remarkably inefficient with the majority of introns being spliced and degraded rather than processed into mature snoRNAs. In summary, our study yields unparalleled insights into the kinetics of RNA processing and provides the tools to study molecular mechanisms of RNA processing and their contribution to the regulation of gene expression.

A context-based approach to identify the most likely mapping for RNA-seq experiments.

BACKGROUND: Sequencing of mRNA (RNA-seq) by next generation sequencing technologies is widely used for analyzing the transcriptomic state of a cell. Here, one of the main challenges is the mapping of a sequenced read to its transcriptomic origin. As a simple alignment to the genome will fail to identify reads crossing splice junctions and a transcriptome alignment will miss novel splice sites, several approaches have been developed for this purpose. Most of these approaches have two drawbacks. First, each read is assigned to a location independent on whether the corresponding gene is expressed or not, i.e. information from other reads is not taken into account. Second, in case of multiple possible mappings, the mapping with the fewest mismatches is usually chosen which may lead to wrong assignments due to sequencing errors. RESULTS: To address these problems, we developed ContextMap which efficiently uses information on the context of a read, i.e. reads mapping to the same expressed region. The context information is used to resolve possible ambiguities and, thus, a much larger degree of ambiguities can be allowed in the initial stage in order to detect all possible candidate positions. Although ContextMap can be used as a stand-alone version using either a genome or transcriptome as input, the version presented in this article is focused on refining initial mappings provided by other mapping algorithms. Evaluation results on simulated sequencing reads showed that the application of ContextMap to either TopHat or MapSplice mappings improved the mapping accuracy of both initial mappings considerably. CONCLUSIONS: In this article, we show that the context of reads mapping to nearby locations provides valuable information for identifying the best unique mapping for a read. Using our method, mappings provided by other state-of-the-art methods can be refined and alignment accuracy can be further improved. AVAILABILITY: http://www.bio.ifi.lmu.de/ContextMap.