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1.
Oncotarget. 2018 Aug 28;9(67):32855-32867. doi: 10.18632/oncotarget.26023. eCollection 2018 Aug 28.

Beyond the 3'UTR binding-microRNA-induced protein truncation via DNA binding.

Author information

1
Department of Urology, University Hospital of Cologne, Cologne, Germany.
2
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center of Bioinformatics, Leipzig University, Leipzig, Germany.
3
Department of Natural Sciences, University of Applied Sciences Bonn-Rhein-Sieg, Rheinbach, Germany.
4
Institute of Pathology, University Hospital of Cologne, Cologne, Germany.
5
Institute of Neuropathology, University Hospital of Cologne, Cologne, Germany.

Abstract

Here, we present a miR mechanism which is active in the nucleus and is essential for the production of intron included, C-terminal truncated and biologically active proteins, like e.g. Vim3. We exemplified this mechanism by miRs, miR-15a and miR-498, which are overexpressed in clear cell renal carcinoma or oncocytoma. Both miRs directly interact with DNA in an intronic region, leading to transcriptional stop, and therefore repress the full length version of the pre-mRNA, resulting in intron included truncated proteins (Mxi-2 and Vim3). A computational survey shows that this miR:DNA interactions mechanism may be generally involved in regulating the human transcriptome, with putative interaction sites in intronic regions for over 1000 genes. In this work, an entirely new mechanism is revealed how miRs can repress full length protein translation, resulting in C-terminal truncated proteins.

KEYWORDS:

DNA interaction; Mxi-2; Vim3; miR-15; miR-498

Conflict of interest statement

CONFLICTS OF INTEREST The authors declare no conflicts of interest.

2.
Stem Cells Int. 2018 Aug 19;2018:5692840. doi: 10.1155/2018/5692840. eCollection 2018.

Noncoding RNA Transcripts during Differentiation of Induced Pluripotent Stem Cells into Hepatocytes.

Author information

1
Applied Stem Cell Biology and Cell Technology, Biomedical and Biotechnological Center, Leipzig University, Deutscher Platz 5, 04103 Leipzig, Germany.
2
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstr. 1618, 04107 Leipzig, Germany.
3
Transcriptome Bioinformatics Group at the Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstr. 1618, 04107 Leipzig, Germany.
4
Department of Plastic Surgery and Hand Surgery, University Hospital Rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany.
5
Competence Center for Scalable Data Services and Solutions Dresden/Leipzig, German Centre for Integrative Biodiversity Research (iDiv), and Leipzig Research Center for Civilization Diseases, Universität Leipzig, Ritterstrasse 9-13, 04109 Leipzig, Germany.
6
Max Planck Institute for Mathematics in the Sciences, Insel Strasse 22, 04103 Leipzig, Germany.
7
Fraunhofer Institute for Cell Therapy and Immunology, Perlickstrasse 1, 04103 Leipzig, Germany.
8
Department of Theoretical Chemistry, University of Vienna, Wahringerstrasse 17, 1090 Vienna, Austria.
9
Center for RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, Denmark.
10
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe NM 87501, USA.

Abstract

Recent advances in the stem cell field allow to obtain many human tissues in vitro. However, hepatic differentiation of induced pluripotent stem cells (iPSCs) still remains challenging. Hepatocyte-like cells (HLCs) obtained after differentiation resemble more fetal liver hepatocytes. MicroRNAs (miRNA) play an important role in the differentiation process. Here, we analysed noncoding RNA profiles from the last stages of differentiation and compare them to hepatocytes. Our results show that HLCs maintain an epithelial character and express miRNA which can block hepatocyte maturation by inhibiting the epithelial-mesenchymal transition (EMT). Additionally, we identified differentially expressed small nucleolar RNAs (snoRNAs) and discovered novel noncoding RNA (ncRNA) genes.

3.
Results Probl Cell Differ. 2018;65:197-225. doi: 10.1007/978-3-319-92486-1_11.

Nonprotein-Coding RNAs as Regulators of Development in Tunicates.

Author information

1
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, Germany. cristian@bioinf.uni-leipzig.de.
2
Biology Department, Universidad Nacional de Colombia, Bogotá, Colombia. cristian@bioinf.uni-leipzig.de.
3
Departamento de Zoologia, Instituto Biociências, Universidade de São Paulo, São Paulo, SP, Brazil.
4
Laboratorio de Biología del Desarrollo Evolutiva, Departamento de Ciencias Biológicas, Universidad de los Andes, Bogotá, Colombia.
5
Institute of Biology, Leiden University, Leiden, Netherlands.
6
GiMaRIS, BioScience Park Leiden, Leiden, Netherlands.
7
Naturalis Biodiversity Center, Leiden, Netherlands.
8
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, Germany.
9
Biology Department, Universidad Nacional de Colombia, Bogotá, Colombia.

Abstract

Tunicates, or urochordates, are a group of small marine organisms that are found widely throughout the seas of the world. As most plausible sister group of the vertebrates, they are of utmost importance for a comprehensive understanding of chordate evolution; hence, they have served as model organisms for many aspects of the developmental biology. Current genomic analysis of tunicates indicates that their genomes evolved with a fast rate not only at the level of nucleotide substitutions but also in terms of genomic organization. The latter involves genome reduction, rearrangements, as well as the loss of some important coding and noncoding RNA (ncRNAs) elements and even entire genomic regions that are otherwise well conserved. These observations are largely based on evidence from comparative genomics resulting from the analysis of well-studied gene families such as the Hox genes and their noncoding elements. In this chapter, the focus lies on the ncRNA complement of tunicates, with a particular emphasis on microRNAs, which have already been studied extensively for other animal clades. MicroRNAs are known as important regulators of key genes in animal development, and they are intimately related to the increase morphological complexity in higher metazoans. Here we review the discovery, evolution, and genome organization of the miRNA repertoire, which has been drastically reduced and restructured in tunicates compared to the chordate ancestor. Known functions of microRNAs as regulators of development in tunicates are a central topic. For instance, we consider the role of miRNAs as regulators of the muscle development and their importance in the regulation of the differential expression during the oral siphon regeneration. Beyond microRNAs, we touch upon the functions of some other ncRNAs such as yellow crescent RNA, moRNAs, RMST lncRNAs, or spliced-leader (SL) RNAs, which have diverse functions associated with the embryonic development, neurogenesis, and mediation of mRNA stability in general.

4.
BMC Res Notes. 2018 Jul 28;11(1):512. doi: 10.1186/s13104-018-3633-x.

The Sierra Platinum Service for generating peak-calls for replicated ChIP-seq experiments.

Author information

1
Image and Signal Processing Group, Department of Computer Science, University of Leipzig, Augustusplatz 10, 04109, Leipzig, Germany. daniel@bioinf.uni-leipzig.de.
2
Natural Language Processing Department, Department of Computer Science, University of Leipzig, Augustusplatz 10, 04109, Leipzig, Germany.
3
Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
4
Image and Signal Processing Group, Department of Computer Science, University of Leipzig, Augustusplatz 10, 04109, Leipzig, Germany.
5
Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
6
Max Planck Institute MIS, Inselstraße 22, 04103, Leipzig, Germany.
7
Fraunhofer Institute for Cell Therapy and Immunology IZI, Perlickstraße 1, 04103, Leipzig, Germany.
8
Institute for Theoretical Chemistry, University of Vienna, Währinger Straße 17, 1090, Vienna, Austria.
9
Center for Non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Copenhagen, Denmark.
10
The Santa Fe Institute, 1399 Hyde Park Road, 87501, Santa Fe, NM, USA.

Abstract

OBJECTIVE:

Sierra Platinum is a fast and robust peak-caller for replicated ChIP-seq experiments with visual quality-control and -steering. The required computing resources are optimized but still may exceed the resources available to researchers at biological research institutes.

RESULTS:

Sierra Platinum Service provides the full functionality of Sierra Platinum: using a web interface, a new instance of the service can be generated. Then experimental data is uploaded and the computation of the peaks is started. Upon completion, the results can be inspected interactively and then downloaded for further analysis, at which point the service terminates.

KEYWORDS:

ChIP-seq; Histone modifications; Peak-caller; Replicate analysis

5.
Genes (Basel). 2018 Jul 26;9(8). pii: E372. doi: 10.3390/genes9080372.

TERribly Difficult: Searching for Telomerase RNAs in Saccharomycetes.

Author information

1
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. maria@tbi.univie.ac.at.
2
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. thiel@tbi.univie.ac.at.
3
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. romanoch@tbi.univie.ac.at.
4
BioInformatics Group, Fakultät CB Hochschule Mittweida, Technikumplatz 17, D-09648 Mittweida, Germany. alexander.holzenleiter@web.de.
5
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany. alexander.holzenleiter@web.de.
6
Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade de Brasília, Campus Universitário⁻Asa Norte, Brasília, DF CEP: 70910-900, Brazil. joaovicers@gmail.com.
7
Departamento de Ciência da Computação, Instituto de Ciências Exatas, Universidade de Brasília, Campus Universitário⁻Asa Norte, Brasília, DF CEP: 70910-900, Brazil. mariaemilia@unb.br.
8
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. michael.wolfinger@univie.ac.at.
9
Center for Anatomy and Cell Biology, Medical University of Vienna, Währingerstraße 13, 1090 Vienna, Austria. michael.wolfinger@univie.ac.at.
10
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. studla@bioinf.uni-leipzig.de.
11
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Competence Center for Scalable Data Services and Solutions, and Leipzig Research Center for Civilization Diseases, Universität Leipzig, D-04107 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
12
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
13
Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA. studla@bioinf.uni-leipzig.de.

Abstract

The telomerase RNA in yeasts is large, usually >1000 nt, and contains functional elements that have been extensively studied experimentally in several disparate species. Nevertheless, they are very difficult to detect by homology-based methods and so far have escaped annotation in the majority of the genomes of Saccharomycotina. This is a consequence of sequences that evolve rapidly at nucleotide level, are subject to large variations in size, and are highly plastic with respect to their secondary structures. Here, we report on a survey that was aimed at closing this gap in RNA annotation. Despite considerable efforts and the combination of a variety of different methods, it was only partially successful. While 27 new telomerase RNAs were identified, we had to restrict our efforts to the subgroup Saccharomycetacea because even this narrow subgroup was diverse enough to require different search models for different phylogenetic subgroups. More distant branches of the Saccharomycotina remain without annotated telomerase RNA.

KEYWORDS:

homology search; non-coding RNA; secondary structure; synteny; telomerase RNA; yeast

6.
Sci Rep. 2018 Jul 24;8(1):11168. doi: 10.1038/s41598-018-29400-y.

Trichoplax genomes reveal profound admixture and suggest stable wild populations without bisexual reproduction.

Author information

1
University of Veterinary Medicine Hannover, Foundation, ITZ Ecology and Evolution, Bünteweg 17d, D-30559, Hannover, Germany. kai.kamm@ecolevol.de.
2
University of Veterinary Medicine Hannover, Foundation, ITZ Ecology and Evolution, Bünteweg 17d, D-30559, Hannover, Germany.
3
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107, Leipzig, Germany.
4
Sackler Institute for Comparative Genomics and Division of Invertebrate Zoology, American Museum of Natural History, New York, New York, USA.
5
University of Veterinary Medicine Hannover, Foundation, ITZ Ecology and Evolution, Bünteweg 17d, D-30559, Hannover, Germany. bernd.schierwater@ecolevol.de.
6
Sackler Institute for Comparative Genomics and Division of Invertebrate Zoology, American Museum of Natural History, New York, New York, USA. bernd.schierwater@ecolevol.de.
7
Yale University, Molecular, Cellular and Developmental Biology, New Haven, CT, 06520, USA. bernd.schierwater@ecolevol.de.

Abstract

The phylum Placozoa officially consists of only a single described species, Trichoplax adhaerens, although several lineages can be separated by molecular markers, geographical distributions and environmental demands. The placozoan 16S haplotype H2 (Trichoplax sp. H2) is the most robust and cosmopolitan lineage of placozoans found to date. In this study, its genome was found to be distinct but highly related to the Trichoplax adhaerens reference genome, for remarkably unique reasons. The pattern of variation and allele distribution between the two lineages suggests that both originate from a single interbreeding event in the wild, dating back at least several decades ago, and both seem not to have engaged in sexual reproduction since. We conclude that populations of certain placozoan haplotypes remain stable for long periods without bisexual reproduction. Furthermore, allelic variation within and between the two Trichoplax lineages indicates that successful bisexual reproduction between related placozoan lineages might serve to either counter accumulated negative somatic mutations or to cope with changing environmental conditions. On the other hand, enrichment of neutral or beneficial somatic mutations by vegetative reproduction, combined with rare sexual reproduction, could instantaneously boost genetic variation, generating novel ecotypes and eventually species.

7.
Algorithms Mol Biol. 2018 Jul 16;13:12. doi: 10.1186/s13015-018-0130-7. eCollection 2018.

Split-inducing indels in phylogenomic analysis.

Donath A1, Stadler PF2,3,4,5,6,7,8.

Author information

1
1Center for Molecular Biodiversity Research (zmb), Zoological Research Museum Alexander Koenig (ZFMK), Adenauerallee 160, 53113 Bonn, Germany.
2
2Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany.
3
3Competence Center for Scalable Data Services and Solutions Dresden/Leipzig, German Centre for Integrative Biodiversity Research (iDiv), and Leipzig Research Center for Civilization Diseases, Universität Leipzig, Härtelstraße 16-18, 04107 Leipzig, Germany.
4
4Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103 Leipzig, Germany.
5
5Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, 04103 Leipzig, Germany.
6
6Department of Theoretical Chemistry, University of Vienna, Währinger Straße 17, 1090 Vienna, Austria.
7
Center for non-coding RNA in Technology and Health, Grønegårdsvej 3, 1870 Frederiksberg C, Denmark.
8
8Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM87501 USA.

Abstract

Background:

Most phylogenetic studies using molecular data treat gaps in multiple sequence alignments as missing data or even completely exclude alignment columns that contain gaps.

Results:

Here we show that gap patterns in large-scale, genome-wide alignments are themselves phylogenetically informative and can be used to infer reliable phylogenies provided the gap data are properly filtered to reduce noise introduced by the alignment method. We introduce here the notion of split-inducing indels (splids) that define an approximate bipartition of the taxon set. We show both in simulated data and in case studies on real-life data that splids can be efficiently extracted from phylogenomic data sets.

Conclusions:

Suitably processed gap patterns extracted from genome-wide alignment provide a surprisingly clear phylogenetic signal and an allow the inference of accurate phylogenetic trees.

KEYWORDS:

Genome-wide multiple sequence alignments; In/del; Phylogenomics; Splits

8.
IEEE/ACM Trans Comput Biol Bioinform. 2017 Dec 11. doi: 10.1109/TCBB.2017.2781724. [Epub ahead of print]

Chemical Transformation Motifs --- Modelling Pathways as Integer Hyperflows.

Abstract

We present an elaborate framework for formally modelling pathways in chemical reaction networks on a mechanistic level. Networks are modelled mathematically as directed multi-hypergraphs, with vertices corresponding to molecules and hyperedges to reactions. Pathways are modelled as integer hyperflows and we expand the network model by detailed routing constraints. In contrast to the more traditional approaches like Flux Balance Analysis or Elementary Mode analysis we insist on integer-valued flows. While this choice makes it necessary to solve possibly hard integer linear programs, it has the advantage that more detailed mechanistic questions can be formulated. It is thus possible to query networks for general transformation motifs, and to automatically enumerate optimal and near-optimal pathways. Similarities and differences between our work and traditional approaches in metabolic network analysis are discussed in detail. To demonstrate the applicability of the mathematical framework to real-life problems we first explore the design space of possible non-oxidative glycolysis pathways and show that recent manually designed pathways can be further optimised. We then use a model of sugar chemistry to investigate pathways in the autocatalytic formose process. A graph transformation-based approach is used to automatically generate the reaction networks of interest.

9.
J Math Biol. 2018 Jun 27. doi: 10.1007/s00285-018-1260-8. [Epub ahead of print]

Reconstructing gene trees from Fitch's xenology relation.

Geiß M1,2, Anders J1,2, Stadler PF1,2,3,4,5,6,7,8, Wieseke N9, Hellmuth M10,11.

Author information

1
Bioinformatics Group, Department of Computer Science, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
2
Interdisciplinary Center of Bioinformatics, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
3
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Competence Center for Scalable Data Services and Solutions, Leipzig University, Leipzig, Germany.
4
Leipzig Research Center for Civilization Diseases, Leipzig University, Leipzig, Germany.
5
Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology - IZI, Perlickstraße 1, 04103, Leipzig, Germany.
6
Max-Planck-Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany.
7
Inst. f. Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Wien, Austria.
8
Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, 87501, USA.
9
Swarm Intelligence and Complex Systems Group, Department of Computer Science, Leipzig University, Augustusplatz 10, 04109, Leipzig, Germany.
10
Institute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Straße 47, 17487, Greifswald, Germany. mhellmuth@mailbox.org.
11
Center for Bioinformatics, Saarland University, Building E 2.1, P.O. Box 151150, 66041, Saarbrücken, Germany. mhellmuth@mailbox.org.

Abstract

Two genes are xenologs in the sense of Fitch if they are separated by at least one horizontal gene transfer event. Horizonal gene transfer is asymmetric in the sense that the transferred copy is distinguished from the one that remains within the ancestral lineage. Hence xenology is more precisely thought of as a non-symmetric relation: y is xenologous to x if y has been horizontally transferred at least once since it diverged from the least common ancestor of x and y. We show that xenology relations are characterized by a small set of forbidden induced subgraphs on three vertices. Furthermore, each xenology relation can be derived from a unique least-resolved edge-labeled phylogenetic tree. We provide a linear-time algorithm for the recognition of xenology relations and for the construction of its least-resolved edge-labeled phylogenetic tree. The fact that being a xenology relation is a heritable graph property, finally has far-reaching consequences on approximation problems associated with xenology relations.

KEYWORDS:

Di-cograph; Fitch xenology; Fixed parameter tractable; Forbidden induced subgraphs; Heritable graph property; Informative triple sets; Least-resolved tree; Phylogenetic tree; Recognition algorithm; Rooted triples

10.
Bull Math Biol. 2018 Aug;80(8):2154-2176. doi: 10.1007/s11538-018-0451-1. Epub 2018 Jun 12.

Cover-Encodings of Fitness Landscapes.

Author information

1
IFISC (CSIC-UIB), Campus Universitat de les Illes Balears, 07122, Palma de Mallorca, Spain.
2
Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103, Leipzig, Germany.
3
Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, 04103, Leipzig, Germany. studla@bioinf.uni-leipzig.de.
4
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University Leipzig, 04107, Leipzig, Germany. studla@bioinf.uni-leipzig.de.
5
Santa Fe Institute, Santa Fe, NM, 87501, USA. studla@bioinf.uni-leipzig.de.

Abstract

The traditional way of tackling discrete optimization problems is by using local search on suitably defined cost or fitness landscapes. Such approaches are however limited by the slowing down that occurs when the local minima that are a feature of the typically rugged landscapes encountered arrest the progress of the search process. Another way of tackling optimization problems is by the use of heuristic approximations to estimate a global cost minimum. Here, we present a combination of these two approaches by using cover-encoding maps which map processes from a larger search space to subsets of the original search space. The key idea is to construct cover-encoding maps with the help of suitable heuristics that single out near-optimal solutions and result in landscapes on the larger search space that no longer exhibit trapping local minima. We present cover-encoding maps for the problems of the traveling salesman, number partitioning, maximum matching and maximum clique; the practical feasibility of our method is demonstrated by simulations of adaptive walks on the corresponding encoded landscapes which find the global minima for these problems.

KEYWORDS:

Adaptive walk; Coarse-graining; Combinatorial optimization; Genotype–phenotype map; Oracle function

11.
Theory Biosci. 2018 Jun 21. doi: 10.1007/s12064-018-0264-7. [Epub ahead of print]

Phylogenetics beyond biology.

Retzlaff N1,2, Stadler PF3,4,5,6,7,8,9.

Author information

1
Max-Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany.
2
Interdisciplinary Center of Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany.
3
Max-Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany. peter.stadler@bioinf.uni-leipzig.de.
4
Interdisciplinary Center of Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany. peter.stadler@bioinf.uni-leipzig.de.
5
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center of Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany. peter.stadler@bioinf.uni-leipzig.de.
6
Fraunhofer Institut für Zelltherapie und Immunologie - IZI, Perlickstraße 1, 04103, Leipzig, Germany. peter.stadler@bioinf.uni-leipzig.de.
7
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Vienna, Austria. peter.stadler@bioinf.uni-leipzig.de.
8
Center for Non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark. peter.stadler@bioinf.uni-leipzig.de.
9
Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM, 87501, USA. peter.stadler@bioinf.uni-leipzig.de.

Abstract

Evolutionary processes have been described not only in biology but also for a wide range of human cultural activities including languages and law. In contrast to the evolution of DNA or protein sequences, the detailed mechanisms giving rise to the observed evolution-like processes are not or only partially known. The absence of a mechanistic model of evolution implies that it remains unknown how the distances between different taxa have to be quantified. Considering distortions of metric distances, we first show that poor choices of the distance measure can lead to incorrect phylogenetic trees. Based on the well-known fact that phylogenetic inference requires additive metrics, we then show that the correct phylogeny can be computed from a distance matrix [Formula: see text] if there is a monotonic, subadditive function [Formula: see text] such that [Formula: see text] is additive. The required metric-preserving transformation [Formula: see text] can be computed as the solution of an optimization problem. This result shows that the problem of phylogeny reconstruction is well defined even if a detailed mechanistic model of the evolutionary process remains elusive.

KEYWORDS:

Additive metric; Cultural evolution; Metric-preserving functions; Phylogenetic tree

12.
Nature. 2018 Jul;559(7714):E10. doi: 10.1038/s41586-018-0167-2.

Author Correction: The landscape of genomic alterations across childhood cancers.

Author information

1
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
2
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
3
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany.
5
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
6
The Finsen Laboratory, Rigshospitalet, Biotech Research and Innovation Centre (BRIC), Copenhagen University, Copenhagen, Denmark.
7
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
9
Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
10
Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University and BioQuant Center, 69120, Heidelberg, Germany.
11
Klinikum Stuttgart - Olgahospital, Zentrum für Kinder-, Jugend- und Frauenmedizin, Pädiatrie, Stuttgart, Germany.
12
Department of Pediatric Oncology, Hematology & Clinical Immunology, University Children's Hospital, Heinrich Heine University, Düsseldorf, Germany.
13
Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
14
Institute for Experimental Cancer Research in Pediatrics, University Hospital Frankfurt, Frankfurt am Main, Germany.
15
Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, Wuerzburg University, Würzburg, Germany.
16
Department of Pediatric Surgery, Research Laboratories, Dr. von Hauner Children's Hospital, Ludwig Maximilians University Munich, Munich, Germany.
17
Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel and University of Basel, Basel, Switzerland.
18
Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
19
Division of Pediatric Hematology and Oncology, University Medical Center Aachen, Aachen, Germany.
20
Department of Human Genetics, University Hospital Essen, Essen, Germany.
21
Division of Pediatric Hematology and Oncology, Department of Pediatrics, University Medical Center Freiburg, Freiburg, Germany.
22
Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany.
23
Institute of Human Genetics, University of Ulm & University Hospital of Ulm, Ulm, Germany.
24
Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
25
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
26
Innovative Therapies for Children with Cancer Consortium and Department of Clinical Research, Gustave Roussy, Université Paris-Saclay, Villejuif, France.
27
Pediatric Hematology and Oncology, University Hospital Münster, Muenster, Germany.
28
Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
29
Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
30
Center for Individualized Pediatric Oncology (ZIPO) and Brain Tumors, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany.
31
Division of Pediatric Hematology and Oncology, University Medical Center Göttingen, Göttingen, Germany.
32
Pediatric Oncology & Hematology, Pediatrics III, University Hospital of Essen, Essen, Germany.
33
Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
34
Swabian Children's Cancer Center, Children's Hospital, Klinikum Augsburg, Augsburg, Germany.
35
Genomics and Proteomics Core Facility, High Throughput Sequencing Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Hospital for Children and Adolescents, University Hospital Frankfurt, Frankfurt, Germany.
37
University Hospital Cologne, Klinik und Poliklinik für Kinder- und Jugendmedizin, Cologne, Germany.
38
Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands.
39
Division of Neurosurgery, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
40
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
41
Division of Oncology, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, USA.
42
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
43
Institute of Computer Science, Freie Universität Berlin, Berlin, Germany.
44
Institute of Medical Genetics and Human Genetics, Charité University Hospital, Berlin, Germany.
45
Bioinformatics and Omics Data Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.
46
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany. s.pfister@dkfz.de.
47
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. s.pfister@dkfz.de.
48
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. s.pfister@dkfz.de.
49
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany. s.pfister@dkfz.de.

Abstract

In this Article, author Benedikt Brors was erroneously associated with affiliation number '8' (Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA); the author's two other affiliations (affiliations '3' and '7', both at the German Cancer Research Center (DKFZ)) were correct. This has been corrected online.

13.
Noncoding RNA. 2017 Jul 5;3(3). pii: E23. doi: 10.3390/ncrna3030023.

Rare Splice Variants in Long Non-Coding RNAs.

Sen R1, Doose G2, Stadler PF3,4,5,6,7,8,9.

Author information

1
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. rituparno@bioinf.uni-leipzig.de.
2
ecSeq Bioinformatics, Brandvorwerkstraße 43, D-04275 Leipzig, Germany. gero.doose@ecseq.com.
3
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
4
ecSeq Bioinformatics, Brandvorwerkstraße 43, D-04275 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
5
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Competence Center for Scalable Data Services and Solutions, Leipzig Research Center for Civilization Diseases, and Leipzig Research Center for Civilization Diseases (LIFE), University Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
6
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
7
Fraunhofer Institute for Cell Therapy and Immunology, Perlickstrasse 1, D-04103 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
8
Center for RNA in Technology and Health, University Copenhagen, Grønnegårdsvej 3, 1870 Frederiksberg C, Denmark. studla@bioinf.uni-leipzig.de.
9
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA. studla@bioinf.uni-leipzig.de.

Abstract

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.

KEYWORDS:

GENCODE; lncRNA; lncRNA isoforms; splice junctions

14.
Genome Res. 2018 May;28(5):699-713. doi: 10.1101/gr.229757.117. Epub 2018 Apr 11.

In vitro iCLIP-based modeling uncovers how the splicing factor U2AF2 relies on regulation by cofactors.

Author information

1
Institute of Molecular Biology (IMB) gGmbH, 55128 Mainz, Germany.
2
Institute of Structural Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany.
3
Biomolecular NMR and Center for Integrated Protein Science Munich at Department of Chemistry, Technical University of Munich, 85747 Garching, Germany.
4
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany.
5
Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany.
6
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, 79104 Freiburg, Germany.
7
Buchmann Institute for Molecular Life Sciences (BMLS), Goethe University Frankfurt, 60438 Frankfurt a.M., Germany.
#
Contributed equally

Abstract

Alternative splicing generates distinct mRNA isoforms and is crucial for proteome diversity in eukaryotes. The RNA-binding protein (RBP) U2AF2 is central to splicing decisions, as it recognizes 3' splice sites and recruits the spliceosome. We establish "in vitro iCLIP" experiments, in which recombinant RBPs are incubated with long transcripts, to study how U2AF2 recognizes RNA sequences and how this is modulated by trans-acting RBPs. We measure U2AF2 affinities at hundreds of binding sites and compare in vitro and in vivo binding landscapes by mathematical modeling. We find that trans-acting RBPs extensively regulate U2AF2 binding in vivo, including enhanced recruitment to 3' splice sites and clearance of introns. Using machine learning, we identify and experimentally validate novel trans-acting RBPs (including FUBP1, CELF6, and PCBP1) that modulate U2AF2 binding and affect splicing outcomes. Our study offers a blueprint for the high-throughput characterization of in vitro mRNP assembly and in vivo splicing regulation.

PMID:
29643205
PMCID:
PMC5932610
[Available on 2018-11-01]
DOI:
10.1101/gr.229757.117
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15.
BMC Evol Biol. 2018 Apr 11;18(1):51. doi: 10.1186/s12862-018-1147-8.

Tracing the evolution of the heterotrimeric G protein α subunit in Metazoa.

Lokits AD1,2, Indrischek H3,4, Meiler J2,5, Hamm HE6, Stadler PF7,8,9,10,11,12.

Author information

1
Neuroscience Program, Vanderbilt University, Nashville, TN, USA.
2
Center for Structural Biology, Vanderbilt University, Nashville, TN, USA.
3
Bioinformatics Group, Department of Computer Science, Leipzig University, Leipzig, Germany.
4
Computational EvoDevo Group, Bioinformatics Department, Leipzig University, Leipzig, Germany.
5
Chemistry Department, Vanderbilt University, Nashville, TN, USA.
6
Pharmacology Department, Vanderbilt University Medical Center, Nashville, TN, USA. heidi.hamm@vanderbilt.edu.
7
Bioinformatics Group, Department of Computer Science, Leipzig University, Leipzig, Germany. peter.stadler@bioinf.uni-leipzig.de.
8
Center for non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg C, Denmark. peter.stadler@bioinf.uni-leipzig.de.
9
Institute for Theoretical Chemistry, University of Vienna, Wien, Austria. peter.stadler@bioinf.uni-leipzig.de.
10
IZBI-Interdisciplinary Center for Bioinformatics and LIFE-Leipzig Research Center for Civilization Diseases and Competence Center for Scalable Data Services and Solutions, University Leipzig, Leipzig, Germany. peter.stadler@bioinf.uni-leipzig.de.
11
Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. peter.stadler@bioinf.uni-leipzig.de.
12
Santa Fe Institute, Santa Fe, NM, USA. peter.stadler@bioinf.uni-leipzig.de.

Abstract

BACKGROUND:

Heterotrimeric G proteins are fundamental signaling proteins composed of three subunits, Gα and a Gβγ dimer. The role of Gα as a molecular switch is critical for transmitting and amplifying intracellular signaling cascades initiated by an activated G protein Coupled Receptor (GPCR). Despite their biochemical and therapeutic importance, the study of G protein evolution has been limited to the scope of a few model organisms. Furthermore, of the five primary Gα subfamilies, the underlying gene structure of only two families has been thoroughly investigated outside of Mammalia evolution. Therefore our understanding of Gα emergence and evolution across phylogeny remains incomplete.

RESULTS:

We have computationally identified the presence and absence of every Gα gene (GNA-) across all major branches of Deuterostomia and evaluated the conservation of the underlying exon-intron structures across these phylogenetic groups. We provide evidence of mutually exclusive exon inclusion through alternative splicing in specific lineages. Variations of splice site conservation and isoforms were found for several paralogs which coincide with conserved, putative motifs of DNA-/RNA-binding proteins. In addition to our curated gene annotations, within Primates, we identified 15 retrotranspositions, many of which have undergone pseudogenization. Most importantly, we find numerous deviations from previous findings regarding the presence and absence of individual GNA- genes, nuanced differences in phyla-specific gene copy numbers, novel paralog duplications and subsequent intron gain and loss events.

CONCLUSIONS:

Our curated annotations allow us to draw more accurate inferences regarding the emergence of all Gα family members across Metazoa and to present a new, updated theory of Gα evolution. Leveraging this, our results are critical for gaining new insights into the co-evolution of the Gα subunit and its many protein binding partners, especially therapeutically relevant G protein - GPCR signaling pathways which radiated in Vertebrata evolution.

KEYWORDS:

Evolution; G protein coupled receptors; Genome annotation; Heterotrimeric G protein; Orthology; Paralog; Whole genome duplication

16.
J Cheminform. 2018 Apr 5;10(1):19. doi: 10.1186/s13321-018-0273-z.

Finding the K best synthesis plans.

Author information

1
Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark.
2
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Vienna, Austria.
3
Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, Härtelstraße 16-18, 04107, Leipzig, Germany.
4
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany.
5
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Vienna, Austria. studla@bioinf.uni-leipzig.de.
6
Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, Härtelstraße 16-18, 04107, Leipzig, Germany. studla@bioinf.uni-leipzig.de.
7
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, 04103, Leipzig, Germany. studla@bioinf.uni-leipzig.de.
8
Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, 04103, Leipzig, Germany. studla@bioinf.uni-leipzig.de.
9
Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark. studla@bioinf.uni-leipzig.de.
10
Santa Fe Institute, 1399 Hyde Park Rd, 87501, Santa Fe, USA. studla@bioinf.uni-leipzig.de.

Abstract

In synthesis planning, the goal is to synthesize a target molecule from available starting materials, possibly optimizing costs such as price or environmental impact of the process. Current algorithmic approaches to synthesis planning are usually based on selecting a bond set and finding a single good plan among those induced by it. We demonstrate that synthesis planning can be phrased as a combinatorial optimization problem on hypergraphs by modeling individual synthesis plans as directed hyperpaths embedded in a hypergraph of reactions (HoR) representing the chemistry of interest. As a consequence, a polynomial time algorithm to find the K shortest hyperpaths can be used to compute the K best synthesis plans for a given target molecule. Having K good plans to choose from has many benefits: it makes the synthesis planning process much more robust when in later stages adding further chemical detail, it allows one to combine several notions of cost, and it provides a way to deal with imprecise yield estimates. A bond set gives rise to a HoR in a natural way. However, our modeling is not restricted to bond set based approaches-any set of known reactions and starting materials can be used to define a HoR. We also discuss classical quality measures for synthesis plans, such as overall yield and convergency, and demonstrate that convergency has a built-in inconsistency which could render its use in synthesis planning questionable. Decalin is used as an illustrative example of the use and implications of our results.

KEYWORDS:

Algorithm; Bond set; Convergency; Decalin; Hypergraph; Hyperpath; Synthesis planning

17.
18.
Nature. 2018 Mar 15;555(7696):321-327. doi: 10.1038/nature25480. Epub 2018 Feb 28.

The landscape of genomic alterations across childhood cancers.

Author information

1
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
2
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
3
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany.
5
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
6
The Finsen Laboratory, Rigshospitalet, Biotech Research and Innovation Centre (BRIC), Copenhagen University, Copenhagen, Denmark.
7
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
9
Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
10
Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University and BioQuant Center, 69120, Heidelberg, Germany.
11
Klinikum Stuttgart - Olgahospital, Zentrum für Kinder-, Jugend- und Frauenmedizin, Pädiatrie, Stuttgart, Germany.
12
Department of Pediatric Oncology, Hematology & Clinical Immunology, University Children's Hospital, Heinrich Heine University, Düsseldorf, Germany.
13
Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
14
Institute for Experimental Cancer Research in Pediatrics, University Hospital Frankfurt, Frankfurt am Main, Germany.
15
Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany.
16
Department of Pediatric Surgery, Research Laboratories, Dr von Hauner Children's Hospital, Ludwig Maximilians University Munich, Munich, Germany.
17
Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel and University of Basel, Basel, Switzerland.
18
Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
19
Division of Pediatric Hematology and Oncology, University Medical Center Aachen, Aachen, Germany.
20
Department of Human Genetics, University Hospital Essen, Essen, Germany.
21
Division of Pediatric Hematology and Oncology, Department of Pediatrics, University Medical Center Freiburg, Freiburg, Germany.
22
Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany.
23
Institute of Human Genetics, University of Ulm & University Hospital of Ulm, Ulm, Germany.
24
Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
25
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
26
Innovative Therapies for Children with Cancer Consortium and Department of Clinical Research, Gustave Roussy, Université Paris-Saclay, Villejuif, France.
27
Pediatric Hematology and Oncology, University Hospital Münster, Münster, Germany.
28
Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
29
Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
30
Center for Individualized Pediatric Oncology (ZIPO) and Brain Tumors, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany.
31
Division of Pediatric Hematology and Oncology, University Medical Center Göttingen, Göttingen, Germany.
32
Pediatric Oncology & Hematology, Pediatrics III, University Hospital of Essen, Essen, Germany.
33
Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
34
Swabian Children's Cancer Center, Children's Hospital, Klinikum Augsburg, Augsburg, Germany.
35
Genomics and Proteomics Core Facility, High Throughput Sequencing Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Hospital for Children and Adolescents, University Hospital Frankfurt, Frankfurt, Germany.
37
University Hospital Cologne, Klinik und Poliklinik für Kinder- und Jugendmedizin, Cologne, Germany.
38
Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands.
39
Division of Neurosurgery, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
40
Department of Computational Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
41
Division of Oncology, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, USA.
42
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
43
Institute of Computer Science, Freie Universität Berlin, Berlin, Germany.
44
Institute of Medical Genetics and Human Genetics, Charité University Hospital, Berlin, Germany.
45
Bioinformatics and Omics Data Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Abstract

Pan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7-8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.

Comment in

PMID:
29489754
DOI:
10.1038/nature25480
[Indexed for MEDLINE]
Icon for Nature Publishing Group
19.
Algorithms Mol Biol. 2018 Feb 6;13:2. doi: 10.1186/s13015-018-0121-8. eCollection 2018.

Time-consistent reconciliation maps and forbidden time travel.

Author information

1
1Institute of Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Strasse 47, 17487 Greifswald, Germany.
2
2Department of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark.
3
5Bioinformatics Group, Department of Computer Science, University of Leipzig, Häartelstraße 16-18, 04107 Leipzig, Germany.
4
6Interdisciplinary Center for Bioinformatics, Universität Leipzig, Häartelstraße 16-18, 04107 Leipzig, Germany.
5
7Max-Planck-Institute for Mathematics in the Sciences, Inselstraße 22, 04103 Leipzig, Germany.
6
Fraunhofer Institut for Cell Therapy and Immunology, Perlickstraße 1, 04103 Leipzig, Germany.
7
9Inst. f. Theoretical Chemistry, University of Vienna, Wäahringerstraße 17, 1090 Wien, Austria.
8
10Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501 USA.
9
Center for non-coding RNA in Technology and Health, Grønegåardsvej 3, 1870 Frederiksberg C, Denmark.
10
3Parallel Computing and Complex Systems Group, Department of Computer Science, Leipzig University, Augustusplatz 10, 04109 Leipzig, Germany.
11
4Center for Bioinformatics, Saarland University, Building E 2.1, P.O. Box 151150, 66041 Saarbrücken, Germany.

Abstract

Background:

In the absence of horizontal gene transfer it is possible to reconstruct the history of gene families from empirically determined orthology relations, which are equivalent to event-labeled gene trees. Knowledge of the event labels considerably simplifies the problem of reconciling a gene tree T with a species trees S, relative to the reconciliation problem without prior knowledge of the event types. It is well-known that optimal reconciliations in the unlabeled case may violate time-consistency and thus are not biologically feasible. Here we investigate the mathematical structure of the event labeled reconciliation problem with horizontal transfer.

Results:

We investigate the issue of time-consistency for the event-labeled version of the reconciliation problem, provide a convenient axiomatic framework, and derive a complete characterization of time-consistent reconciliations. This characterization depends on certain weak conditions on the event-labeled gene trees that reflect conditions under which evolutionary events are observable at least in principle. We give an [Formula: see text]-time algorithm to decide whether a time-consistent reconciliation map exists. It does not require the construction of explicit timing maps, but relies entirely on the comparably easy task of checking whether a small auxiliary graph is acyclic. The algorithms are implemented in C++ using the boost graph library and are freely available at https://github.com/Nojgaard/tc-recon.

Significance:

The combinatorial characterization of time consistency and thus biologically feasible reconciliation is an important step towards the inference of gene family histories with horizontal transfer from orthology data, i.e., without presupposed gene and species trees. The fast algorithm to decide time consistency is useful in a broader context because it constitutes an attractive component for all tools that address tree reconciliation problems.

KEYWORDS:

History of gene families; Horizontal gene transfer; Reconciliation map; Time-consistency; Tree reconciliation

20.
J Exp Zool B Mol Dev Evol. 2018 Jan;330(1):5-14. doi: 10.1002/jez.b.22785. Epub 2018 Jan 22.

Toward a mechanistic explanation of phenotypic evolution: The need for a theory of theory integration.

Laubichler MD1,2,3, Prohaska SJ3,4,5, Stadler PF3,5,6,7,8,9,10.

Author information

1
School of Life Sciences, Arizona State University, Tempe, Arizona.
2
Marine Biological Laboratory, Woods Hole, Massachusetts.
3
Santa Fe Institute, Santa Fe, New Mexico.
4
Computational EvoDevo Group, Department of Computer Science, Leipzig, Germany.
5
Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, Germany.
6
Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
7
Max-Planck Institute for Mathematics in the Sciences, Leipzig, Germany.
8
Fraunhofer Institut für Zelltherapie und Immunologie-IZI, Leipzig, Germany.
9
Department of Theoretical Chemistry, University of Vienna, Wien, Austria.
10
Center for Non-Coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.

Abstract

Reconciling different underlying ontologies and explanatory contexts has been one of the main challenges and impediments for theory integration in biology. Here, we analyze the challenge of developing an inclusive and integrative theory of phenotypic evolution as an example for the broader challenge of developing a theory of theory integration within the life sciences and suggest a number of necessary formal steps toward the resolution of often incompatible (hidden) assumptions. Theory integration in biology requires a better formal understanding of the structure of biological theories The strategy for integrating theories crucially depends on the relationships of the underlying ontologies.

KEYWORDS:

ontology; phenotypic evolution; theory integration

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