Applied Bioinformatics is one of the key life sciences’ disciplines of the 21st century. State-of-the-art bioinformatic approaches are proving indispensable in the development of highly innovative and individual therapies, which focus on the disease-specific molecular changes of each individual patient.
TRON’s Bioinformatics department comprises the following groups: Computational Medicine, Data Management and Personalized Integrative Computational Genomics. Our multidisciplinary team is composed of bioinformaticians, biotechnologists, mathematicians and physicists. As a central technology platform, we interact closely with other specialist groups of TRON.
The Computational Medicine and Personalized Integrative Computational Genomics teams develop novel methods for data processing and analysis. In doing so, they ensure the conceptual development and implementation of innovative structures, therapies and diagnostics in the areas of cancer, autoimmunity and infectious diseases:
- TRON’s founding of a Helmholtz Institute for Translational Oncology in Mainz focusing on individualized immunotherapy was successful by virtue of its strong Applied Bioinformatics team and platforms.
- Our highly innovative bioinformatics processing and analysis tools drive the identification of mutations and expression profiles and enable publications in high impact journals.
- Based on our outstanding expertise, we develop sophisticated software solutions for the identification of new targets, which are already applied in clinical research.
- We design bioinformatics solutions for tomorrow’s diagnostics and therapies with strong industrial and academic partners.
The Data Management group provides the infrastructural and technical basis for the work at TRON. Their established powerful databases and laboratory information and management systems enable:
- Smooth operation of high throughput processes, e.g. Next Generation Sequencing (NGS).
- Support of our high-performance computing infrastructure (HPC).
- Monitoring of our high quality standards that allow the development of software for use in GxP-regulated areas, as is also used in the food and pharmaceutical industries.
Available tools developed and published by our group can be retrieved via our Github repository.
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.
Latest Galaxy LIMS code can be found on our bucket:
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.
Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. Here, we present an integrated map of the genome, transcriptome and immunome of an epithelial mouse tumor, the CT26 colon carcinoma cell line. We found that Kras is homozygously mutated at p.G12D, Apc and Tp53 are not mutated, and Cdkn2a is homozygously deleted. Proliferation and stem-cell markers, including Top2a, Birc5 (Survivin), Cldn6 and Mki67, are highly expressed while differentiation and top-crypt markers Muc2, Ms4a8a (MS4A8B) and Epcam are not. Myc, Trp53 (tp53), Mdm2, Hif1a, and Nras are highly expressed while Egfr and Flt1 are not. MHC class I but not MHC class II is expressed. Several known cancer-testis antigens are expressed, including Atad2, Cep55, and Pbk. The highest expressed gene is a mutated form of the mouse tumor-specific antigen gp70. Of the 1,688 non-synonymous somatic point mutations, 154 are both in expressed genes and in peptides predicted to bind MHC and thus potential targets for immunotherapy development. Based on its molecular signature, we predicted that CT26 is refractory to anti-EGFR mAbs and sensitive to MEK and MET inhibitors, as have been previously reported. CT26 cells share molecular features with aggressive, undifferentiated, refractory human colorectal carcinoma cells. As CT26 is one of the most extensively used syngeneic mouse tumor models, our data provide a map for the rationale design of mode-of-action studies for pre-clinical evaluation of targeted- and immunotherapies.