Youri Hoogstrate

A comprehensive repertoire of tRNA-derived fragments in prostate cancer.

Prostate cancer (PCa) is the most common cancer among men in developed countries. Although its genetic background is thoroughly investigated, rather little is known about the role of small non-coding RNAs (sncRNA) in this disease. tRNA-derived fragments (tRFs) represent a new class of sncRNAs, which are present in a broad range of species and have been reported to play a role in several cellular processes. Here, we analyzed the expression of tRFs in fresh frozen patient samples derived from normal adjacent prostate and different stages of PCa by RNA-sequencing. We identified 598 unique tRFs, many of which are deregulated in cancer samples when compared to normal adjacent tissue. Most of the identified tRFs are derived from the 5’- and 3’-ends of mature cytosolic tRNAs, but we also found tRFs produced from other parts of tRNAs, including pre-tRNA trailers and leaders, as well as tRFs from mitochondrial tRNAs. The 5’-derived tRFs comprise the most abundant class of tRFs in general and represent the major class among upregulated tRFs. The 3’-derived tRFs types are dominant among downregulated tRFs in PCa. We validated the expression of three tRFs using qPCR. The ratio of tRFs derived from tRNALysCTT and tRNAPheGAA emerged as a good indicator of progression-free survival and a candidate prognostic marker. This study provides a systematic catalogue of tRFs and their dysregulation in PCa and can serve as the basis for further research on the biomarker potential and functional roles of tRFs in this disease.

FlaiMapper: computational annotation of small ncRNA-derived fragments using RNA-seq high-throughput data

MOTIVATION Recent discoveries show that most types of small non-coding RNAs (sncRNAs) such as miRNAs, snoRNAs and tRNAs get further processed into putatively active smaller RNA species. Their roles, genetic profiles and underlying processing mechanisms are only partially understood. To find their quantities and characteristics, a proper annotation is essential. Here, we present FlaiMapper, a method that extracts and annotates the locations of sncRNA-derived RNAs (sncdRNAs). These sncdRNAs are often detected in sequencing data and observed as fragments of their precursor sncRNA. Using small RNA-seq read alignments, FlaiMapper is able to annotate fragments primarily by peak detection on the start and end position densities followed by filtering and a reconstruction process. RESULTS To assess performance of FlaiMapper, we used independent publicly available small RNA-seq data. We were able to detect fragments representing putative sncdRNAs from nearly all types of sncRNA, including 97.8% of the annotated miRNAs in miRBase that have supporting reads. Comparison of FlaiMapper-predicted boundaries of miRNAs with miRBase entries demonstrated that 89% of the start and 54% of the end positions are identical. Additional benchmarking showed that FlaiMapper is superior in performance compared with existing software. Further analysis indicated a variety of characteristics in the fragments, including sequence motifs and relations with RNA interacting factors. These characteristics set a good basis for further research on sncdRNAs. AVAILABILITY AND IMPLEMENTATION The platform independent GPL licensed Python 2.7 code is available at: https://github.com/yhoogstrate/flaimapper.

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

Abstract 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.

FuMa: reporting overlap in RNA-seq detected fusion genes

UNLABELLED A new generation of tools that identify fusion genes in RNA-seq data is limited in either sensitivity and or specificity. To allow further downstream analysis and to estimate performance, predicted fusion genes from different tools have to be compared. However, the transcriptomic context complicates genomic location-based matching. FusionMatcher (FuMa) is a program that reports identical fusion genes based on gene-name annotations. FuMa automatically compares and summarizes all combinations of two or more datasets in a single run, without additional programming necessary. FuMa uses one gene annotation, avoiding mismatches caused by tool-specific gene annotations. FuMa matches 10% more fusion genes compared with exact gene matching due to overlapping genes and accepts intermediate output files that allow a stepwise analysis of corresponding tools. AVAILABILITY AND IMPLEMENTATION The code is available at: https://github.com/ErasmusMC-Bioinformatics/fuma and available for Galaxy in the tool sheds and directly accessible at https://bioinf-galaxian.erasmusmc.nl/galaxy/ CONTACT y.hoogstrate@erasmusmc.nl or a.stubbs@erasmusmc.nl SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

iReport: a generalised Galaxy solution for integrated experimental reporting

BackgroundGalaxy 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.FindingsWe 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.ConclusionsiReport 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.

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.

Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data

The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and integrate the data efficiently. For this reason, we set up an ELIXIR implementation study, together with the Translational research IT (TraIT) programme, to design a data ecosystem that is able to link raw and interpreted data. In this project, the data from the TraIT Cell Line Use Case (TraIT-CLUC) are used as a test case for this system. Within this ecosystem, we use the European Genome-phenome Archive (EGA) to store raw molecular profiling data; tranSMART to collect interpreted molecular profiling data and clinical data for corresponding samples; and Galaxy to store, run and manage the computational workflows. We can integrate these data by linking their repositories systematically. To showcase our design, we have structured the TraIT-CLUC data, which contain a variety of molecular profiling data types, for storage in both tranSMART and EGA. The metadata provided allows referencing between tranSMART and EGA, fulfilling the cycle of data submission and discovery; we have also designed a data flow from EGA to Galaxy, enabling reanalysis of the raw data in Galaxy. In this way, users can select patient cohorts in tranSMART, trace them back to the raw data and perform (re)analysis in Galaxy. Our conclusion is that the majority of metadata does not necessarily need to be stored (redundantly) in both databases, but that instead FAIR persistent identifiers should be available for well-defined data ontology levels: study, data access committee, physical sample, data sample and raw data file. This approach will pave the way for the stable linkage and reuse of data.

Integration of EGA secure data access into Galaxy

High-throughput molecular profiling techniques are routinely generating vast amounts of data for translational medicine studies. Secure access controlled systems are needed to manage, store, transfer and distribute these data due to its personally identifiable nature. The European Genome-phenome Archive (EGA) was created to facilitate access and management to long-term archival of bio-molecular data. Each data provider is responsible for ensuring a Data Access Committee is in place to grant access to data stored in the EGA. Moreover, the transfer of data during upload and download is encrypted. ELIXIR, a European research infrastructure for life-science data, initiated a project (2016 Human Data Implementation Study) to understand and document the ELIXIR requirements for secure management of controlled-access data. As part of this project, a full ecosystem was designed to connect archived raw experimental molecular profiling data with interpreted data and the computational workflows, using the CTMM Translational Research IT (CTMM-TraIT) infrastructure http://www.ctmm-trait.nl as an example. Here we present the first outcomes of this project, a framework to enable the download of EGA data to a Galaxy server in a secure way. Galaxy provides an intuitive user interface for molecular biologists and bioinformaticians to run and design data analysis workflows. More specifically, we developed a tool -- ega_download_streamer - that can download data securely from EGA into a Galaxy server, which can subsequently be further processed. This tool will allow a user within the browser to run an entire analysis containing sensitive data from EGA, and to make this analysis available for other researchers in a reproducible manner, as shown with a proof of concept study. The tool ega_download_streamer is available in the Galaxy tool shed: https://toolshed.g2.bx.psu.edu/view/yhoogstrate/ega_download_streamer.

Consensus molecular subtype classification of colorectal adenomas: CMS 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.

Abstract A45: GAS5-encoded intronic snoRNAs produce specific sdRNAs overexpressed in aggressive prostate cancer

Small non-coding RNAs, such as miRNAs, are implicated in carcinogenesis. To investigate changes in the entire small RNA transcriptome in prostate cancer (PCa) we analyzed 11 clinical sample pools representing different stages of PCa by deep sequencing. We found that most C/D-box small nucleolar RNAs (snoRNAs) are specifically processed to smaller snoRNA-derived RNAs (sdRNAs) highly expressed in PCa. In particular, SNORD78 produces sdRNAs strongly up-regulated in PCa. Together with 9 other snoRNAs, SNORD78 is encoded in the introns of the Growth Arrest Specific 5 gene (GAS5). Examination of SNORD78 and the positioned in a neighboring intron SNORD44 showed that both snoRNAs produce predominantly one sdRNA fragment each, specifically originating from the 39-arm (SNORD78) or the 59-arm (SNORD44) of the precursor sequence. Inspection of the secondary structures of SNORD44 and SNORD78 revealed that they have a degenerated C9/D9 box and can fold in a tight hairpin similarly to miRNAs. In contrast, SNORD74 and SNORD81 that also encoded in introns of GAS5, contain canonical C9/D9 boxes and each produce three equally expressed sdRNAs. Quantitative real time PCR analysis in an independent patient cohort of 106 fresh-frozen clinical samples confirmed the significant up-regulation of all four snoRNAs and their derivative sdRNAs in PCa samples compared to normal tissue. Interestingly, the increased expression of snoRNAs and sdRNAs was not associated with elevated levels of GAS5 transcript. Based on these results, we conclude that (i) separate regulatory mechanisms control the posttranscriptional processing of the spliced GAS5 transcript and the encoded in its introns snoRNAs; (ii) SNORD44, SNORD78, SNORD74 and SNORD81 function as precursors of different sdRNAs; (iii) SNORD44, SNORD78, SNORD74 and SNORD81 and their derivative sdRNAs are significantly up-regulated in PCa and carry biomarker potential for this disease. Citation Format: Elena S. Martens-Uzunova, Anton Kalsbeek, Youri Hoogstrate, Adam Baker, Soren Jensby Nielsen, Tapio Visakorpi, Chris Bangma, Guido Jenster. GAS5-encoded intronic snoRNAs produce specific sdRNAs overexpressed in aggressive prostate cancer. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr A45.