Applied Bioinformatics is one of the key scientific disciplines of the 21st century. State-of-the-art bioinformatics approaches are proving indispensable in the development of innovative therapies, which focus on the disease-specific molecular changes in each individual patient. TRON’s Bioinformatics department comprises a multidisciplinary team of bioinformaticians, biotechnologists, mathematicians, physicists and scientists from other related fields. The Computational Medicine (CompMed) and Personalized Integrative Computational Genomics (PICG) teams develop novel methods and algorithms for processing and analysis of sequencing data in the context of cancer, autoimmunity and infectious diseases, both for internal research projects and with external partners, as service or for application in GxP-regulated areas.
The Data Management team, with expertise in maintaining high performance cluster (HPC) infrastructures and database systems, builds database infrastructures and ensures smooth NGS operations and traceability via in-house developed laboratory information and management (LIMS). Through our cooperation with the Center for Data Processing of the Johannes Gutenberg-University Mainz we have access to one of the most powerful HPC CLUSTERS in the world.
The Single Cell Genomics (SCG) team are specialists in building standardized pipelines for reproducible research on the single-cell level, working with other specialist research groups to elaborate precise and advanced immunological insights. As with CompMed and PICG’s activities, SCG’s research and development is applied to internal studies and is available to collaboration partners.
computational validation of fusion gene detection tools without relying on simulated reads
Fusion genes, resulting from larger chromosomal rearrangements, can play an important role in the development of cancer. Investigating such events is hence not only essential in understanding cancer biology but may help identify therapeutic targets. Unfortunately, the performance of existing fusion detection tools cannot be evaluated due to the lack of known fusion events. In the past, simulated reads that form such fusion events during alignment have been used to assess the performance of the tools. However, read simulation cannot represent the biological complexity of RNA-seq data.
In this article, we present a method to introduce artificial fusion events into the chromosomal sequences of the human reference genome. Using a dedicated set of fusion detection tools on MCF7 samples, we compared our approach with read simulation data and show that only our tool, ArtiFuse, incorporates the biological variety of sequencing data.
The ArtiFuse approach can be used to benchmark the performance of published fusion detection tools and helps to build up a repertoire of high-quality tools for upcoming analyses.
Human cancer cell lines are an important resource for research and drug development. However, the available annotations of cell lines are sparse, incomplete, and distributed in multiple repositories. Re-analyzing publicly available raw RNA-Seq data, we determined the human leukocyte antigen (HLA) type and abundance, identified expressed viruses and calculated gene expression of 1,082 cancer cell lines. Using the determined HLA types, public databases of cell line mutations, and existing HLA binding prediction algorithms, we predicted antigenic mutations in each cell line. We integrated the results into a comprehensive knowledgebase. Using the Django web framework, we provide an interactive user interface with advanced search capabilities to find and explore cell lines and an application-programming interface to extract cell line information. The portal is available at http://celllines.tron-mainz.de.
We have developed a laboratory information management system (LIMS) for a next-generation sequencing (NGS) laboratory within the existing Galaxy platform. The system provides lab technicians standard and customizable sample information forms, barcoded submission forms, tracking of input sample quality, multiplex-capable automatic flow cell design and automatically generated sample sheets to aid physical flow cell preparation. In addition, the platform provides the researcher with a user-friendly interface to create a request, submit accompanying samples, upload sample quality measurements and access to the sequencing results. As the LIMS is within the Galaxy platform, the researcher has access to all Galaxy analysis tools and workflows. The system reports requests and associated information to a message queuing system, such that information can be posted and stored in external systems, such as a wiki. Through an API, raw sequencing results can be automatically pre-processed and uploaded to the appropriate request folder. Developed for the Illumina HiSeq 2500 instrument, many features are directly applicable to other instruments.
Next generation sequencing (NGS) has enabled high throughput discovery of somatic mutations. Detection depends on experimental design, lab platforms, parameters and analysis algorithms. However, NGS-based somatic mutation detection is prone to erroneous calls, with reported validation rates near 54% and congruence between algorithms less than 50%. Here, we developed an algorithm to assign a single statistic, a false discovery rate (FDR), to each somatic mutation identified by NGS. This FDR confidence value accurately discriminates true mutations from erroneous calls. Using sequencing data generated from triplicate exome profiling of C57BL/6 mice and B16-F10 melanoma cells, we used the existing algorithms GATK, SAMtools and SomaticSNiPer to identify somatic mutations. For each identified mutation, our algorithm assigned an FDR. We selected 139 mutations for validation, including 50 somatic mutations assigned a low FDR (high confidence) and 44 mutations assigned a high FDR (low confidence). All of the high confidence somatic mutations validated (50 of 50), none of the 44 low confidence somatic mutations validated, and 15 of 45 mutations with an intermediate FDR validated. Furthermore, the assignment of a single FDR to individual mutations enables statistical comparisons of lab and computation methodologies, including ROC curves and AUC metrics. Using the HiSeq 2500, single end 50 nt reads from replicates generate the highest confidence somatic mutation call set.
Peptide microarrays offer an enormous potential as a screening tool for peptidomics experiments and have recently seen an increased field of application ranging from immunological studies to systems biology. By allowing the parallel analysis of thousands of peptides in a single run, they are suitable for high-throughput settings. Since data characteristics of peptide microarrays differ from DNA oligonucleotide microarrays, computational methods need to be tailored to these specifications to allow a robust and automated data analysis. While follow-up experiments can ensure the specificity of results, sensitivity cannot be recovered in later steps. Providing sensitivity is thus a primary goal of data analysis procedures. To this end, we created rapmad (Robust Alignment of Peptide MicroArray Data), a novel computational tool implemented in R. We evaluated rapmad in antibody reactivity experiments for several thousand peptide spots and compared it to two existing algorithms for the analysis of peptide microarrays. rapmad displays competitive and superior behavior to existing software solutions. Particularly, it shows substantially improved sensitivity for low intensity settings without sacrificing specificity. It thereby contributes to increasing the effectiveness of high throughput screening experiments. rapmad allows the robust and sensitive, automated analysis of high-throughput peptide array data.