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B cells have the unique property to somatically alter their immunoglobulin (IG) genes by V(D)J recombination, somatic hypermutation (SHM) and class-switch recombination (CSR). Aberrant targeting of these mechanisms is implicated in lymphomagenesis, but the mutational processes are poorly understood. By performing whole genome and transcriptome sequencing of 181 germinal center derived B-cell lymphomas (gcBCL) we identified distinct mutational signatures linked to SHM and CSR. We show that not only SHM, but presumably also CSR causes off-target mutations in non-IG genes. Kataegis clusters with high mutational density mainly affected early replicating regions and were enriched for SHM- and CSR-mediated off-target mutations. Moreover, they often co-occurred in loci physically interacting in the nucleus, suggesting that mutation hotspots promote increased mutation targeting of spatially co-localized loci (termed hypermutation by proxy). Only around 1% of somatic small variants were in protein coding sequences, but in about half of the driver genes, a contribution of B-cell specific mutational processes to their mutations was found. The B-cell-specific mutational processes contribute to both lymphoma initiation and intratumoral heterogeneity. Overall, we demonstrate that mutational processes involved in the development of gcBCL are more complex than previously appreciated, and that B cell-specific mutational processes contribute via diverse mechanisms to lymphomagenesis. This study has been supported by the German Ministry of Science and Education (BMBF) in the framework of the ICGC MMML-Seq project (01KU1002A-J) the MMML-MYC-SYS project (036166B) and the project ICGC DE-MINING (01KU1505E), the European Union in the framework of the BLUEPRINT Project (HEALTH-F5-2011-282510) and the KinderKrebsInitiative Buchholz/Holm-Seppensen. This work was supported by the BMBF-funded Heidelberg Center for Human Bioinformatics (HD-HuB) within the German Network for Bioinformatics Infrastructure (de.NBI) (#031A537A, #031A537C). Former grant support of MMML by the Deutsche Krebshilfe (2003–2011) is gratefully acknowledged. We acknowledge COSMIC and use of Cancer Gene Census. Part of the work was performed in association with SFB1074 (particularly subproject B1) funded by DFG. We wish to thank Barbara Hutter, Ivo Buchhalter, Zuguang Gu, and Natalie Jäger for skillful technical assistance. We thank the High-Throughput Sequencing Unit of the Genome and Proteome Core Facility and the Omics IT and Data Management Core Facility of the German Cancer Research Center (DKFZ, Heidelberg) as well as the Institute of Clinical Molecular Biology (IKMB, Christian-Albrechts-University Kiel) for excellent technical support and expertise. DH is a member of the Hartmut Hoffmann-Berling International Graduate School of Molecular and Cellular Biology (HBIGS) and of the MD/PhD-program of the University of Heidelberg. KK and UHT are funded by the Helmholtz International Graduate School for Cancer Research at the German Cancer Research Center. SHB, HK, and SH acknowledge support by LIFE (Leipzig Research Center for Civilization Diseases), Leipzig University. LIFE is funded by the European Union, the European Regional Development Fund (ERDF), the European Social Fund (ESF), and the Free State of Saxony. This work has been carried out with the help of the Interdisciplinary Bank of Biomaterials and Data of the University Hospital of Würzburg and the Julius Maximilian University of Würzburg (idbw). Peer Reviewed "Article signat per 70 autors/es:Daniel Hübschmann, Kortine Kleinheinz, Rabea Wagener, Stephan H. Bernhart, Cristina López, Umut H. Toprak, Stephanie Sungalee, Naveed Ishaque, Helene Kretzmer, Markus Kreuz, Sebastian M. Waszak, Nagarajan Paramasivam, Ole Ammerpohl, Sietse M. Aukema, Renée Beekman, Anke K. Bergmann, Matthias Bieg, Hans Binder, Arndt Borkhardt, Christoph Borst, Benedikt Brors, Philipp Bruns, Enrique Carrillo de Santa Pau, Alexander Claviez, Gero Doose, Andrea Haake, Dennis Karsch, Siegfried Haas, Martin-Leo Hansmann, Jessica I. Hoell, Volker Hovestadt, Bingding Huang, Michael Hummel, Christina Jäger-Schmidt, Jules N. A. Kerssemakers, Jan O. Korbel, Dieter Kube, Chris Lawerenz, Dido Lenze, Joost H. A. Martens, German Ott, Bernhard Radlwimmer, Eva Reisinger, Julia Richter, Daniel Rico, Philip Rosenstiel, Andreas Rosenwald, Markus Schillhabel, Stephan Stilgenbauer, Peter F. Stadler, José I. Martín-Subero, Monika Szczepanowski, Gregor Warsow, Marc A. Weniger, Marc Zapatka, Alfonso Valencia, Hendrik G. Stunnenberg, Peter Lichter, Peter Möller, Markus Loeffler, Roland Eils, Wolfram Klapper, Steve Hoffmann, Lorenz Trümper, ICGC MMML-Seq consortium, ICGC DE-Mining consortium, BLUEPRINT consortium, Ralf Küppers, Matthias Schlesner & Reiner Siebert"

BackgroundA lack of reproducibility has been repeatedly criticized in computational research. High throughput sequencing (HTS) data analysis is a complex multi-step process. For most of the steps a range of bioinformatic tools is available and for most tools manifold parameters need to be set. Due to this complexity, HTS data analysis is particularly prone to reproducibility and consistency issues. We have defined four criteria that in our opinion ensure a minimal degree of reproducible research for HTS data analysis. A series of workflow management systems is available for assisting complex multi-step data analyses. However, to the best of our knowledge, none of the currently available work flow management systems satisfies all four criteria for reproducible HTS analysis.ResultsHere we present uap, a workflow management system dedicated to robust, consistent, and reproducible HTS data analysis. uap is optimized for the application to omics data, but can be easily extended to other complex analyses. It is available under the GNU GPL v3 license at https://github.com/yigbt/uap.Conclusionsuap is a freely available tool that enables researchers to easily adhere to reproducible research principles for HTS data analyses.

Recent studies revealed trajectories of mutational events in early melanomagenesis, but the accompanying changes in gene expression are far less understood. Therefore, we performed a comprehensive RNA-seq analysis of laser-microdissected melanocytic nevi (n = 23) and primary melanoma samples (n = 57) and characterized the molecular mechanisms of early melanoma development. Using self-organizing maps, unsupervised clustering, and analysis of pseudotime (PT) dynamics to identify evolutionary trajectories, we describe here two transcriptomic types of melanocytic nevi (N1 and N2) and primary melanomas (M1 and M2). N1/M1 lesions are characterized by pigmentation-type and MITF gene signatures, and a high prevalence of NRAS mutations in M1 melanomas. N2/M2 lesions are characterized by inflammatory-type and AXL gene signatures with an equal distribution of wild-type and mutated BRAF and low prevalence of NRAS mutations in M2 melanomas. Interestingly, N1 nevi and M1 melanomas and N2 nevi and M2 melanomas, respectively, cluster together, but there is no clustering in a stage-dependent manner. Transcriptional signatures of M1 melanomas harbor signatures of BRAF/MEK inhibitor resistance and M2 melanomas harbor signatures of anti-PD-1 antibody treatment resistance. Pseudotime dynamics of nevus and melanoma samples are suggestive for a switch-like immune-escape mechanism in melanoma development with downregulation of immune genes paralleled by an increasing expression of a cell cycle signature in late-stage melanomas. Taken together, the transcriptome analysis identifies gene signatures and mechanisms underlying development of melanoma in early and late stages with relevance for diagnostics and therapy.

Background Anti-CD3 immunotherapy was initially approved for clinical use for renal transplantation rejection prevention. Subsequently, new generations of anti-CD3 antibodies have entered clinical trials for a broader spectrum of therapeutic applications, including cancer and autoimmune diseases. Despite their extensive use, little is known about the exact mechanism of these molecules, except that they are able to activate T cells, inducing an overall immunoregulatory and tolerogenic behavior. To better understand the effects of anti-CD3 antibodies on human T cells, PBMCs were stimulated, and then, we performed RNA-seq assays of enriched T cells to assess changes in their gene expression profiles. In this study, three different anti-CD3 antibodies were used for the stimulation: two recombinant antibody fragments, namely, a humanized and a chimeric FvFc molecule, and the prototype mouse mAb OKT3. Results Gene Ontology categories and individual immunoregulatory markers were compared, suggesting a similarity in modulated gene sets, mainly those for immunoregulatory and inflammatory terms. Upregulation of interleukin receptors, such as IL2RA, IL1R, IL12RB2, IL18R1, IL21R and IL23R, and of inhibitory molecules, such as FOXP3, CTLA4, TNFRSF18, LAG3 and PDCD1, were also observed, suggesting an inhibitory and exhausted phenotype. Conclusions We used a deep transcriptome sequencing method for comparing three anti-CD3 antibodies in terms of Gene Ontology enrichment and immunological marker expression. The present data showed that both recombinant antibodies induced a compatible expression profile, suggesting that they might be candidates for a closer evaluation with respect to their therapeutic value. Moreover, the proposed methodology is amenable to be more generally applied for molecular comparison of cell receptor dependent antibody therapy. Electronic supplementary material The online version of this article (10.1186/s12864-019-5967-8) contains supplementary material, which is available to authorized users.

Motivation Alternative splicing is a biological process of fundamental importance in most eukaryotes. It plays a pivotal role in cell differentiation and gene regulation and has been associated with a number of different diseases. The widespread availability of RNA-Sequencing capacities allows an ever closer investigation of differentially expressed isoforms. However, most tools for differential alternative splicing (DAS) analysis do not take split reads, i.e. the most direct evidence for a splice event, into account. Here, we present DIEGO, a compositional data analysis method able to detect DAS between two sets of RNA-Seq samples based on split reads. Results The python tool DIEGO works without isoform annotations and is fast enough to analyze large experiments while being robust and accurate. We provide python and perl parsers for common formats. Availability and implementation The software is available at: www.bioinf.uni-leipzig.de/Software/DIEGO. Supplementary information Supplementary data are available at Bioinformatics online.

Long non-coding RNAs (lncRNAs) form a substantial component of the transcriptome and are involved in a wide variety of regulatory mechanisms. Compared to protein-coding genes, they are often expressed at low levels and are restricted to a narrow range of cell types or developmental stages. As a consequence, the diversity of their isoforms is still far from being recorded and catalogued in its entirety, and the debate is ongoing about what fraction of non-coding RNAs truly conveys biological function rather than being “junk”. Here, using a collection of more than 100 transcriptomes from related B cell lymphoma, we show that lncRNA loci produce a very defined set of splice variants. While some of them are so rare that they become recognizable only in the superposition of dozens or hundreds of transcriptome datasets and not infrequently include introns or exons that have not been included in available genome annotation data, there is still a very limited number of processing products for any given locus. The combined depth of our sequencing data is large enough to effectively exhaust the isoform diversity: the overwhelming majority of splice junctions that are observed at all are represented by multiple junction-spanning reads. We conclude that the human transcriptome produces virtually no background of RNAs that are processed at effectively random positions, but is—under normal circumstances—confined to a well defined set of splice variants.

The unprecedented outbreak of Ebola in West Africa resulted in over 28,000 cases and 11,000 deaths, underlining the need for a better understanding of the biology of this highly pathogenic virus to develop specific counter strategies. Two filoviruses, the Ebola and Marburg viruses, result in a severe and often fatal infection in humans. However, bats are natural hosts and survive filovirus infections without obvious symptoms. The molecular basis of this striking difference in the response to filovirus infections is not well understood. We report a systematic overview of differentially expressed genes, activity motifs and pathways in human and bat cells infected with the Ebola and Marburg viruses, and we demonstrate that the replication of filoviruses is more rapid in human cells than in bat cells. We also found that the most strongly regulated genes upon filovirus infection are chemokine ligands and transcription factors. We observed a strong induction of the JAK/STAT pathway, of several genes encoding inhibitors of MAP kinases (DUSP genes) and of PPP1R15A, which is involved in ER stress-induced cell death. We used comparative transcriptomics to provide a data resource that can be used to identify cellular responses that might allow bats to survive filovirus infections.

Circular and apparently trans-spliced RNAs have recently been reported as abundant types of transcripts in mammalian transcriptome data. Both types of non-colinear RNAs are also abundant in RNA-seq of different tissue from both the African and the Indonesian coelacanth. We observe more than 8,000 lincRNAs with normal gene structure and several thousands of circularized and trans-spliced products, showing that such atypical RNAs form a substantial contribution to the transcriptome. Surprisingly, the majority of the circularizing and trans-connecting splice junctions are unique to atypical forms, that is, are not used in normal isoforms.

Based on novel correlation analyses in RNA-seq data from normal tissues and from Burkitt lymphoma (BL) cell lines, Hart et al. (1) suggest that MINCR is not a MYC-induced long noncoding (lnc) RNA. We thank the authors for providing additional analyses, but we do not think these data prove that MINCR is not a MYC-induced lncRNA.

The unprecedented outbreak of Ebola in West Africa resulted in over 28,000 cases and 11,000 deaths, underlining the need for a better understanding of the biology of this highly pathogenic virus to develop specific counter strategies. Two filoviruses, the Ebola and Marburg viruses, result in a severe and often fatal infection in humans. However, bats are natural hosts and survive filovirus infections without obvious symptoms. The molecular basis of this striking difference in the response to filovirus infections is not well understood. We report a systematic overview of differentially expressed genes, activity motifs and pathways in human and bat cells infected with the Ebola and Marburg viruses, and we demonstrate that the replication of filoviruses is more rapid in human cells than in bat cells. We also found that the most strongly regulated genes upon filovirus infection are chemokine ligands and transcription factors. We observed a strong induction of the JAK/STAT pathway, of several genes encoding inhibitors of MAP kinases (DUSP genes) and of PPP1R15A, which is involved in ER stress-induced cell death. We used comparative transcriptomics to provide a data resource that can be used to identify cellular responses that might allow bats to survive filovirus infections.

Many fields of science and industry depend on efficient production of active protein using heterologous expression in Escherichia coli. The solubility of proteins upon expression is dependent on their amino acid sequence. Prediction of solubility from sequence is therefore highly valuable. We present a novel machine-learning-based model called PROSO II which makes use of new classification methods and growth in experimental data to improve coverage and accuracy of solubility predictions. The classification algorithm is organized as a two-layered structure in which the output of a primary Parzen window model for sequence similarity and a logistic regression classifier of amino acid k-mer composition serve as input for a second-level logistic regression classifier. Compared with previously published research our model is trained on five times more data than used by any other method before (82 000 proteins). When tested on a separate holdout set not used at any point of method development our server attained the best results in comparison with other currently available methods: accuracy 75.4%, Matthew's correlation coefficient 0.39, sensitivity 0.731, specificity 0.759, gain (soluble) 2.263. In summary, due to utilization of cutting edge machine learning technologies combined with the largest currently available experimental data set the PROSO II server constitutes a substantial improvement in protein solubility predictions. PROSO II is available at http://mips.helmholtz-muenchen.de/prosoII.

Although Burkitt lymphomas and follicular lymphomas both have features of germinal center B cells, they are biologically and clinically quite distinct. Here we performed whole-genome bisulfite, genome and transcriptome sequencing in 13 IG-MYC translocation-positive Burkitt lymphoma, nine BCL2 translocation-positive follicular lymphoma and four normal germinal center B cell samples. Comparison of Burkitt and follicular lymphoma samples showed differential methylation of intragenic regions that strongly correlated with expression of associated genes, for example, genes active in germinal center dark-zone and light-zone B cells. Integrative pathway analyses of regions differentially methylated in Burkitt and follicular lymphomas implicated DNA methylation as cooperating with somatic mutation of sphingosine phosphate signaling, as well as the TCF3-ID3 and SWI/SNF complexes, in a large fraction of Burkitt lymphomas. Taken together, our results demonstrate a tight connection between somatic mutation, DNA methylation and transcriptional control in key B cell pathways deregulated differentially in Burkitt lymphoma and other germinal center B cell lymphomas.

The methylotrophic yeast Komagatella pastoris is a relevant bioengineering platform for protein synthesis. Even though non-coding RNAs are well known to be key players in the control of gene expression no comprehensive annotation of non-coding RNAs has been reported for this species. We combine here published RNA-seq data with a wide array of homology based annotation tools and de novo gene predictions to compile the non-coding RNAs in K. pastoris.

Numerous high-throughput sequencing studies have focused on detecting conventionally spliced mRNAs in RNA-seq data. However, non-standard RNAs arising through gene fusion, circularization or trans-splicing are often neglected. We introduce a novel, unbiased algorithm to detect splice junctions from single-end cDNA sequences. In contrast to other methods, our approach accommodates multi-junction structures. Our method compares favorably with competing tools for conventionally spliced mRNAs and, with a gain of up to 40% of recall, systematically outperforms them on reads with multiple splits, trans-splicing and circular products. The algorithm is integrated into our mapping tool segemehl (http://www.bioinf.uni-leipzig.de/Software/segemehl/).

RNA prediction has long been struggling with long-range base pairs since prediction accuracy decreases with base pair span. We analyze here the empirical distribution of base pair spans in large collection of experimentally known RNA structures. Surprisingly, we find that long-range base pairs are overrepresented in these data. In particular, there is no evidence that long-range base pairs are systematically overpredicted relative to short-range interactions in thermodynamic predictions. This casts doubt on a recent suggestion that kinetic effects are the cause of length-dependent decrease of predictability. Instead of a modification of the energy model we advocate a modification of the expected accuracy model for RNA secondary structures. We demonstrate that the inclusion of a span-dependent penalty leads to improved maximum expected accuracy structure predictions compared to both the standard MEA model and a modified folding algorithm with an energy penalty function. The prevalence of long-range base pairs provide further evidence that RNA structures in general do not have the so-called polymer zeta property. This has consequences for the asymptotic performance for a large class of sparsified RNA folding algorithms.

Prokaryotic transcripts constitute almost always uninterrupted intervals when mapped back to the genome. Split reads, i.e., RNA-seq reads consisting of parts that only map to discontiguous loci, are thus disregarded in most analysis pipelines. There are, however, some well-known exceptions, in particular, tRNA splicing and circularized small RNAs in Archaea as well as self-splicing introns. Here, we reanalyze a series of published RNA-seq data sets, screening them specifically for non-contiguously mapping reads. We recover most of the known cases together with several novel archaeal ncRNAs associated with circularized products. In Eubacteria, only a handful of interesting candidates were obtained beyond a few previously described group I and group II introns. Most of the atypically mapping reads do not appear to correspond to well-defined, specifically processed products. Whether this diffuse background is, at least in part, an incidental by-product of prokaryotic RNA processing or whether it consists entirely of technical artifacts of reverse transcription or amplification remains unknown.

Motivation: Today many non-coding RNAs are known to play an active role in various important biological processes. Since RNA's functionality is correlated with specific structural motifs that are often conserved in phylogenetically related molecules, computational prediction of RNA structure should ideally be based on a set of homologous primary structures. But many available RNA secondary structure prediction programs that use sequence alignments do not consider pseudoknots or their estimations consist on a single structure without information on uncertainty. Results: In this article we present a method that takes advantage of the evolutionary history of a group of aligned RNA sequences for sampling consensus secondary structures, including pseudoknots, according to their approximate posterior probability. We investigate the benefit of using evolutionary history and demonstrate the competitiveness of our method compared with similar methods based on RNase P RNA sequences and simulated data. Availability: PhyloQFold, a C + + implementation of our method, is freely available from http://evol.bio.lmu.de/_statgen/software/phyloqfold/ Contact: gero@bioinf.uni-leipzig.de, metzler@bio.lmu.de Supplementary information: Supplementary data are available at Bioinformatics online.