Molecular and Computational Transcriptomics in Prostate Cancer

RNA-seq is a powerful new technology that allows detection of new RNA molecules, including potentially new biomarkers. For various analysis applications of such data, new or better solutions are needed. Because there is a lack of diagnostic / prognostic biomarkers for prostate cancer, one of the most common types of cancer in men, prostate cancer is an ideal model system for the development of new analysis methods. We developed new computational methods, to improve detection of fusion genes and small ncRNAs, and demonstrate how these methods can be used to find prostate cancer specific transcripts. All proposed methods were ultimately integrated into the Galaxy RNA workbench that has been made available to other researchers, free of charge

IReport: A generalised Galaxy solution for integrated experimental reporting

Background: Galaxy offers a number of visualisation options with components, such as Trackster, Circster and Galaxy Charts, but currently lacks the ability to easily combine outputs from different tools into a single view or report. A number of tools produce HTML reports as output in order to combine the various output files from a single tool; however, this requires programming and knowledge of HTML, and the reports must be custom-made for each new tool.Findings: We have developed a generic and flexible reporting tool for Galaxy, iReport, that allows users to create interactive HTML reports directly from the Galaxy UI, with the ability to combine an arbitrary number of outputs from any number of different tools. Content can be organised into different tabs, and interactivity can be added to components. To demonstrate the capability of iReport we provide two publically available examples, the first is an iReport explaining about iReports, created for, and using content from the recent Galaxy Community Conference 2014. The second is a genetic report based on a trio analysis to determine candidate pathogenic variants which uses our previously developed Galaxy toolset for whole-genome NGS analysis, CGtag. These reports may be adapted for outputs from any sequencing platform and any results, such as omics data, non-high throughput results and clinical variables.Conclusions: iReport provides a secure, collaborative, and flexible web-based reporting system that is compatible with Galaxy (and non-Galaxy) generated content. We demonstrate its value with a real-life example of reporting genetic trio-analysis

The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis. Availability: The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench

The RNA workbench: Best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis

Cellular, mitochondrial and molecular alterations associate with early left ventricular diastolic dysfunction in a porcine model of diabetic metabolic derangement

The prevalence of diabetic metabolic derangement (DMetD) has increased dramatically over the last decades. Although there is increasing evidence that DMetD is associated with cardiac dysfunction, the early DMetD-induced myocardial alterations remain incompletely understood. Here, we studied early DMetD-related cardiac changes in a clinically relevant large animal model. DMetD was established in adult male Göttingen miniswine by streptozotocin injections and a high-fat, high-sugar diet, while control animals remained on normal pig chow. Five months later left ventricular (LV) function was assessed by echocardiography and hemodynamic measurements, followed by comprehensive biochemical, molecular and histological analyses. Robust DMetD developed, evidenced by hyperglycemia, hypercholesterolemia and hypertriglyceridemia. DMetD resulted in altered LV nitroso-redox balance

Guide-free Cas9 from pathogenic Campylobacter jejuni bacteria causes severe damage to DNA

CRISPR-Cas9 systems are enriched in human pathogenic bacteria and have been linked to cytotoxicity by an unknown mechanism. Here, we show that upon infection of human cells, Campylobacter jejuni secretes its Cas9 (CjeCas9) nuclease into their cytoplasm. Next, a native nuclear localization signal enables CjeCas9 nuclear entry, where it catalyzes metal-dependent nonspecific DNA cleavage leading to cell death. Compared to CjeCas9, native Cas9 of Streptococcus pyogenes (SpyCas9) is more suitable for guide-dependent editing. However, in human cells, native SpyCas9 may still cause some DNA damage, most likely because of its ssDNA cleavage activity. This side effect can be completely prevented by saturation of SpyCas9 with an appropriate guide RNA, which is only partially effective for CjeCas9. We conclude that CjeCas9 plays an active role in attacking human cells rather than in viral defense. Moreover, these unique catalytic features may therefore make CjeCas9 less suitable for genome editing applications

The EGFRvIII transcriptome in glioblastoma, a meta-omics analysis.

BACKGROUND: EGFR is among the genes most frequently altered in glioblastoma, with exons 2-7 deletions (EGFRvIII) being amongst its most common genomic mutations. There are conflicting reports about its prognostic role and it remains unclear whether and how it differs in signalling compared with wildtype EGFR. METHODS: To better understand the oncogenic role of EGFRvIII, we leveraged four large datasets into one large glioblastoma transcriptome dataset (n=741) alongside 81 whole-genome samples from two datasets. RESULTS: The EGFRvIII/EGFR expression ratios differ strongly between tumours and ranges from 1% to 95%. Interestingly, the slope of relative EGFRvIII expression is near-linear, which argues against a more positive selection pressure than EGFR wildtype. An absence of selection pressure is also suggested by the similar survival between EGFRvIII positive and negative glioblastoma patients. EGFRvIII levels are inversely correlated with pan-EGFR (all wildtype and mutant variants) expression, which indicates that EGFRvIII has a higher potency in downstream pathway activation. EGFRvIII-positive glioblastomas have a lower CDK4 or MDM2 amplification incidence than EGFRvIII-negative (p=0.007), which may point towards crosstalk between these pathways. EGFRvIII-expressing tumours have an upregulation of 'classical' subtype genes compared to those with EGFR-amplification only (p=3.873e-6). Genomic breakpoints of the EGFRvIII deletions have a preference towards the 3' end of the large intron-1. These preferred breakpoints preserve a cryptic exon resulting in a novel EGFRvIII variant and preserve an intronic enhancer. CONCLUSIONS: These data provide deeper insights into the complex EGFRvIII biology and provide new insights for targeting EGFRvIII mutated tumours

Non-IDH1-R132H IDH1/2 mutations are associated with increased DNA methylation and improved survival in astrocytomas, compared to IDH1-R132H mutations

Somatic mutations in the isocitrate dehydrogenase genes IDH1 and IDH2 occur at high frequency in several tumour types. Even though these mutations are confined to distinct hotspots, we show that gliomas are the only tumour type with an exceptionally high percentage of IDH1(R132H) mutations. Patients harbouring IDH1(R132H) mutated tumours have lower levels of genome-wide DNA-methylation, and an associated increased gene expression, compared to tumours with other IDH1/2 mutations ("non-R132H IDH1/2 mutations"). This reduced methylation is seen in multiple tumour types and thus appears independent of the site of origin. For 1p/19q non-codeleted glioma (astrocytoma) patients, we show that this difference is clinically relevant: in samples of the randomised phase III CATNON trial, patients harbouring tumours with IDH mutations other than IDH1(R132H) have a better outcome (hazard ratio 0.41, 95% CI [0.24, 0.71], p = 0.0013). Such non-R132H IDH1/2-mutated tumours also had a significantly lower proportion of tumours assigned to prognostically poor DNA-methylation classes (p < 0.001). IDH mutation-type was independent in a multivariable model containing known clinical and molecular prognostic factors. To confirm these observations, we validated the prognostic effect of IDH mutation type on a large independent dataset. The observation that non-R132H IDH1/2-mutated astrocytomas have a more favourable prognosis than their IDH1(R132H) mutated counterpart indicates that not all IDH-mutations are identical. This difference is clinically relevant and should be taken into account for patient prognostication.MTG6Molecular tumour pathology - and tumour genetic

Consensus molecular subtype classification of colorectal adenomas

Consensus molecular subtyping is an RNA expression-based classification system for colorectal cancer (CRC). Genomic alterations accumulate during CRC pathogenesis, including the premalignant adenoma stage, leading to changes in RNA expression. Only a minority of adenomas progress to malignancies, a transition that is associated with specific DNA copy number aberrations or microsatellite instability (MSI). We aimed to investigate whether colorectal adenomas can already be stratified into consensus molecular subtype (CMS) classes, and whether specific CMS classes are related to the presence of specific DNA copy number aberrations associated with progression to malignancy. RNA sequencing was performed on 62 adenomas and 59 CRCs. MSI status was determined with polymerase chain reaction-based methodology. DNA copy number was assessed by low-coverage DNA sequencing (n = 30) or array-comparative genomic hybridisation (n = 32). Adenomas were classified into CMS classes together with CRCs from the study cohort and from The Cancer Genome Atlas (n = 556), by use of the established CMS classifier. As a result, 54 of 62 (87%) adenomas were classified according to the CMS. The CMS3 ‘metabolic subtype’, which was least common among CRCs, was most prevalent among adenomas (n = 45; 73%). One of the two adenomas showing MSI was classified as CMS1 (2%), the ‘MSI immune’ subtype. Eight adenomas (13%) were classified as the ‘canonical’ CMS2. No adenomas were classified as the ‘mesenchymal’ CMS4, consistent with the fact that adenomas lack invasion-associated stroma. The distribution of the CMS classes among adenomas was confirmed in an independent series. CMS3 was enriched with adenomas at low risk of progressing to CRC, whereas relatively more high-risk adenomas were observed in CMS2. We conclude that adenomas can be stratified into the CMS classes. Considering that CMS1 and CMS2 expression signatures may mark adenomas at increased risk of progression, the distribution of the CMS classes among adenomas is consistent with the proportion of adenomas expected to progress to CRC

Community-Driven Data Analysis Training for Biology

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable