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1.
Blood Cancer J. 2018 Aug 10;8(8):77. doi: 10.1038/s41408-018-0114-3.

RBFOX2 and alternative splicing in B-cell lymphoma.

Author information

1
Department of Human and Animal Cell Lines, Leibniz-Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany. hqu@dsmz.de.
2
Department of Human and Animal Cell Lines, Leibniz-Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
3
Transcriptome Bioinformatics Group - Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany.
4
Computational Biology, Leibniz Institute on Aging - Fritz Lipmann Institute and Friedrich Schiller University Jena, Jena, Germany.
5
Institute of Human Genetics, Ulm University and Ulm University Medical Center, Ulm, Germany.

Publication type

Publication type

2.
Oncogene. 2018 Jul 11. doi: 10.1038/s41388-018-0385-y. [Epub ahead of print]

RNA-seq analysis identifies different transcriptomic types and developmental trajectories of primary melanomas.

Author information

1
Department of Dermatology, Venereology and Allergology, University of Leipzig, Philipp-Rosenthal-Str. 23-25, 04103, Leipzig, Germany. Manfred.kunz@medizin.uni-leipzig.de.
2
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany.
3
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103, Leipzig, Germany.
4
Bioinformatics Group, Faculty for Mathematics and Computer Science, University of Leipzig, Härtelstrasse 16-18, 04107, Leipzig, Germany.
5
Department of Dermatology, Venereology and Allergology, University of Leipzig, Philipp-Rosenthal-Str. 23-25, 04103, Leipzig, Germany.
6
Department of Dermatology and Allergy, University of Bonn, Sigmund-Freud-Strasse 25, 53127, Bonn, Germany.
7
Department of Dermatology, University of Magdeburg, Leipziger Strasse 44, 39120, Magdeburg, Germany.
8
Department of Dermatology, Venereology and Allergology, University of Köln, 50937 Köln, Germany.
9
Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, and Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, 68167 Mannheim, Germany.
10
Department of Dermatology, Fachklinik Hornheide, Dorbaumstrasse 300, 48157, Münster, Germany.
11
Unit for RNA Biology, Department of Clinical Chemistry and Clinical Pharmacology, University of Bonn, 53127 Bonn, Germany.
12
Department of Dermatology, University Hospital Essen, West German Cancer Center, University Duisburg-Essen and the German Cancer Consortium (DKTK), University of Duisburg-Essen, 45122 Essen, Germany.
13
Department of Physiological Chemistry, University of Würzburg, Biozentrum, Am Hubland, 97074, Würzburg, Germany.
14
Comprehensive Cancer Center Mainfranken, University Clinic Würzburg, 97080, Würzburg, Germany.
15
Institute of Biochemistry, Emil-Fischer Zentrum, University of Erlangen, Fahrstraße 17, 91054, Erlangen, Germany.
16
Hagler Institute for Advanced Study and Department of Biology, Texas A&M University, College Station, TX, 77843-3572, USA.

Abstract

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.

3.
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.

4.
Exp Mol Med. 2018 Mar 2;50(3):e453. doi: 10.1038/emm.2017.290.

Whither systems medicine?

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK.
2
Institute of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
3
Fachbereich Informatik und Informationswissenschaft, Universität Konstanz, Konstanz, Germany.
4
Institute of Pathology, Charité Universitätsmedizin, Berlin, Germany.
5
Integrative Research Institute for the Life Sciences, Institute for Theoretical Biology, Humboldt Universität, Berlin, Germany.
6
Biologische Heilmittel Heel GmbH, Baden-Baden, Germany.
7
Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, USA.
8
Perinatal Epidemiology Unit, Hannover Medical School, Hannover, Germany.
9
Center for Information Services and High Performance Computing, Technische Universität Dresden, Dresden, Germany.
10
Institute for Pathology, Neuropathology, Hannover Medical School, Hannover, Germany.
11
Institute for Neuropathology, University Clinic of Freiburg, Freiburg, Germany.
12
Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
13
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
14
Center for Mathematics, Technische Universität München, Garching, Germany.
15
Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
16
Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
17
BioQuant Center, University of Heidelberg, Heidelberg, Germany.
18
Maastricht Center for Systems Biology, University of Maastricht, Maastricht, The Netherlands.
19
Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany.
20
Division of Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.
21
Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
22
Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Frankfurt am Main, Germany.
23
Center for Bioinformatics, University of Tübingen, Tübingen, Germany.
24
Quantitative Biology Center, University of Tübingen, Tübingen, Germany.
25
Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany.
26
Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.
27
Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany.
28
Biomax Informatics AG, Planegg (Munich), Germany.
29
Department of Computational Biology and Applied Algorithmics, Max-Planck-Institut für Informatik, Saarbrücken, Germany.
30
Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany.
31
Stuttgart Research Center Systems Biology, University of Stuttgart, Stuttgart, Germany.
32
Department of Physiology & Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland.
33
Institute of Cell Biology and Immunology, University of Stuttgart, Stuttgart, Germany.
34
Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine,Technische Universität Dresden, Dresden, Germany.
35
Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH-Aachen University Hospital, Aachen, Germany.
36
Department of Medical Informatics, University Medical Center, Göttingen, Germany.
37
Institute for Lung Research/iLung, Universities of Giessen and Marburg Lung Center, Philipps University Marburg, Marburg, Germany, Member of the German Center for Lung Research, Marburg, Germany.
38
Joint Research Center for Computational Biomedicine, AICES, RWTH Aachen University, Aachen, Germany.
39
Department of Mathematics, Technische Universität München, Munich, Germany.
40
Laboratory of Systems Tumor Immunology, Friedrich-Alexander University Erlangen-Nürnberg and Erlangen University Hospital, Erlangen, Germany.
41
Department of Systems Biology and Bioinformatics, Rostock University, Rostock, Germany.
42
Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, Stellenbosch, South Africa.

Abstract

New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.

Publication type

Publication type

5.
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
6.
Algorithms Mol Biol. 2017 Dec 7;12:26. doi: 10.1186/s13015-017-0117-9. eCollection 2017.

Generalized enhanced suffix array construction in external memory.

Author information

1
Department of Computing and Mathematics, University of São Paulo, Av. Bandeirantes, 3900, Ribeirão Preto, 14040-901 Brazil.
2
Institute of Computing, University of Campinas, Av. Albert Einstein, 1251, Campinas, 13083-852 Brazil.
3
Computational Biology, Leibniz Institute on Aging - Fritz Lipman Institute and Friedrich Schiller University Jena, Beutenbergstrasse 11, Jena, 07745 Germany.
4
Institute of Mathematics and Computer Science, University of São Paulo, Av. Trabalhador São-carlense, 400, São Carlos, 13560-970 Brazil.

Abstract

Background:

Suffix arrays, augmented by additional data structures, allow solving efficiently many string processing problems. The external memory construction of the generalized suffix array for a string collection is a fundamental task when the size of the input collection or the data structure exceeds the available internal memory.

Results:

In this article we present and analyze [Formula: see text] [introduced in CPM (External memory generalized suffix and [Formula: see text] arrays construction. In: Proceedings of CPM. pp 201-10, 2013)], the first external memory algorithm to construct generalized suffix arrays augmented with the longest common prefix array for a string collection. Our algorithm relies on a combination of buffers, induced sorting and a heap to avoid direct string comparisons. We performed experiments that covered different aspects of our algorithm, including running time, efficiency, external memory access, internal phases and the influence of different optimization strategies. On real datasets of size up to 24 GB and using 2 GB of internal memory, [Formula: see text] showed a competitive performance when compared to [Formula: see text] and [Formula: see text], which are efficient algorithms for a single string according to the related literature. We also show the effect of disk caching managed by the operating system on our algorithm.

Conclusions:

The proposed algorithm was validated through performance tests using real datasets from different domains, in various combinations, and showed a competitive performance. Our algorithm can also construct the generalized Burrows-Wheeler transform of a string collection with no additional cost except by the output time.

KEYWORDS:

Burrows–Wheeler transform; External memory algorithms; LCP array; String collections; Suffix array

7.
Sci Transl Med. 2017 Nov 22;9(417). pii: eaal4599. doi: 10.1126/scitranslmed.aal4599.

What evidence do we need for biomarker qualification?

Author information

1
Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA.
2
Foundation for the National Institutes of Health (NIH), North Bethesda, MD 20852, USA.
3
Takeda Pharmaceuticals International Co., Cambridge, MA 02139, USA.
4
Pfizer Inc., Groton, CT 06340, USA.
5
National Institute of Mental Health, NIH, Bethesda, MD 20892, USA.
6
Critical Path Institute, Tucson, AZ 85718, USA.
7
Pharmaceutical Research and Manufacturers of America, Washington, DC 20004, USA.
8
National Cancer Institute, NIH, Rockville, MD 20850, USA.
9
Biocerna LLC, Maple Lawn, MD 20759, USA.
10
Massachusetts General Hospital, Boston, MA 02114, USA.
11
Merck and Co. Inc., West Point, PA 19486, USA.
12
Genentech Inc., South San Francisco, CA 94080, USA.
13
Foundation for the National Institutes of Health (NIH), North Bethesda, MD 20852, USA. dwholley@fnih.org.

Abstract

Biomarkers can facilitate all aspects of the drug development process. However, biomarker qualification-the use of a biomarker that is accepted by the U.S. Food and Drug Administration-needs a clear, predictable process. We describe a multistakeholder effort including government, industry, and academia that proposes a framework for defining the amount of evidence needed for biomarker qualification. This framework is intended for broad applications across multiple biomarker categories and uses.

PMID:
29167393
DOI:
10.1126/scitranslmed.aal4599
[Indexed for MEDLINE]
Icon for HighWire
8.
Bioinformatics. 2018 Mar 15;34(6):1066-1068. doi: 10.1093/bioinformatics/btx690.

DIEGO: detection of differential alternative splicing using Aitchison's geometry.

Author information

1
Transcriptome Bioinformatics Group, Interdisciplinary Center for Bioinformatics, Leipzig University, 04107 Leipzig.
2
Chair of Bioinformatics, Faculty of Mathematics and Computer Science, Leipzig University, 04107 Leipzig, Germany.
3
ecSeq Bioinformatics, 04103 Leipzig, Germany.
4
Institute of Human Genetics, University of Ulm and University of Ulm Medical Center, 89081 Ulm, Germany.
5
Computational Biology Group, Leibniz Institute on Ageing - Fritz Lipmann Institute (FLI) and Friedrich-Schiller-University Jena, 07745 Jena, Germany.

Abstract

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.

Contact:

steve@bioinf.uni-leipzig.de.

Supplementary information:

Supplementary data are available at Bioinformatics online.

9.
Sci Rep. 2017 Oct 26;7(1):14166. doi: 10.1038/s41598-017-14286-z.

Keeping it complicated: Mitochondrial genome plasticity across diplonemids.

Valach M1, Moreira S2,3, Hoffmann S4, Stadler PF5,6,7,8,9,10,11, Burger G12.

Author information

1
Department of biochemistry and Robert-Cedergren Centre for Bioinformatics and Genomics, Université de Montréal, 2900 Edouard-Montpetit, Montreal, H3T 1J4, QC, Canada. matus.a.valach@gmail.com.
2
Department of biochemistry and Robert-Cedergren Centre for Bioinformatics and Genomics, Université de Montréal, 2900 Edouard-Montpetit, Montreal, H3T 1J4, QC, Canada.
3
Department of Biochemistry and Molecular Biophysics, Columbia University, Hammer Health Science Center, 701 W 168th St, New York, NY, 10031, USA.
4
Leipzig University, LIFE - Leipzig Research Center for Civilization Diseases, Haertelstrasse 16-18, Leipzig, D-04107, Germany.
5
Bioinformatics Group, Department Computer Science, and Interdisciplinary Center for Bioinformatics, University Leipzig, Härtelstrasse 16-18, D-04107, Leipzig, Germany.
6
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, University Leipzig, D-04107, Leipzig, Germany.
7
Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, D-04103, Leipzig, Germany.
8
Fraunhofer Institute for Cell Therapy and Immunology, Perlickstrasse 1, D-04103, Leipzig, Germany.
9
Department of Theoretical Chemistry of the University of Vienna, Währingerstrasse 17, A-1090, Vienna, Austria.
10
Center for RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark.
11
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA.
12
Department of biochemistry and Robert-Cedergren Centre for Bioinformatics and Genomics, Université de Montréal, 2900 Edouard-Montpetit, Montreal, H3T 1J4, QC, Canada. gertraud.burger@umontreal.ca.

Abstract

Chromosome rearrangements are important drivers in genome and gene evolution, with implications ranging from speciation to development to disease. In the flagellate Diplonema papillatum (Euglenozoa), mitochondrial genome rearrangements have resulted in nearly hundred chromosomes and a systematic dispersal of gene fragments across the multipartite genome. Maturation into functional RNAs involves separate transcription of gene pieces, joining of precursor RNAs via trans-splicing, and RNA editing by substitution and uridine additions both reconstituting crucial coding sequence. How widespread these unusual features are across diplonemids is unclear. We have analyzed the mitochondrial genomes and transcriptomes of four species from the Diplonema/Rhynchopus clade, revealing a considerable genomic plasticity. Although gene breakpoints, and thus the total number of gene pieces (~80), are essentially conserved across this group, the number of distinct chromosomes varies by a factor of two, with certain chromosomes combining up to eight unrelated gene fragments. Several internal protein-coding gene pieces overlap substantially, resulting, for example, in a stretch of 22 identical amino acids in cytochrome c oxidase subunit 1 and NADH dehydrogenase subunit 5. Finally, the variation of post-transcriptional editing patterns across diplonemids indicates compensation of two adverse trends: rapid sequence evolution and loss of genetic information through unequal chromosome segregation.

10.
Arthritis Rheumatol. 2018 Jan;70(1):80-87. doi: 10.1002/art.40348. Epub 2017 Dec 15.

Predictive Validity of Radiographic Trabecular Bone Texture in Knee Osteoarthritis: The Osteoarthritis Research Society International/Foundation for the National Institutes of Health Osteoarthritis Biomarkers Consortium.

Author information

1
Duke University School of Medicine, Durham, North Carolina.
2
Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
3
Duke Image Analysis Laboratory, Duke University, Durham, North Carolina.
4
University of California, San Francisco.
5
Foundation for the National Institutes of Health, Bethesda, Maryland.
6
Boston University School of Medicine, Boston, Massachusetts, and University of Erlangen-Nuremberg, Erlangen, Germany.
7
Boston University School of Medicine, Boston, Massachusetts.
8
Royal North Shore Hospital and University of Sydney, Sydney, New South Wales, Australia.

Abstract

OBJECTIVE:

To evaluate radiographic subchondral trabecular bone texture (TBT) as a predictor of clinically relevant osteoarthritis (OA) progression (combination of symptom and structural worsening).

METHODS:

The Foundation for the National Institutes of Health (FNIH) OA Biomarkers Consortium undertook a study of progressive knee OA cases (n = 194 knees with both radiographic and pain progression over 24-48 months) and comparators (n = 406 OA knees not meeting the case definition). TBT parameters were extracted from a medial subchondral tibial region of interest by fractal signature analysis of radiographs using validated semiautomated software. Baseline TBT and time-integrated values over 12 and 24 months were evaluated for association with case status and separately with radiographic and pain progression status, adjusted for age, sex, body mass index, race, baseline Kellgren/Lawrence grade, baseline joint space width, Western Ontario and McMaster Universities Osteoarthritis Index pain score, and pain medication use. C statistics were generated from receiver operating characteristic curves.

RESULTS:

Relative to comparators, cases were characterized by thinner vertical and thicker horizontal trabeculae. The summed composite of 3 TBT parameters at baseline and over 12 and 24 months best predicted case status (odds ratios 1.24-1.43). The C statistic for predicting case status using the TBT composite score (0.633-0.649) was improved modestly but statistically significantly over the use of covariates alone (0.608). One TBT parameter, reflecting thickened horizontal trabeculae in cases, at baseline and over 12 and 24 months, predicted risk of any progression (radiographic and/or pain progression).

CONCLUSION:

Although associations are modest, TBT could be an attractive means of enriching OA trials for progressors since it can be generated from screening knee radiographs already standard in knee OA clinical trials.

PMID:
29024470
PMCID:
PMC5745253
[Available on 2019-01-01]
DOI:
10.1002/art.40348
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11.
F1000Res. 2017 Aug 16;6:1490. doi: 10.12688/f1000research.12302.1. eCollection 2017.

BAT: Bisulfite Analysis Toolkit: BAT is a toolkit to analyze DNA methylation sequencing data accurately and reproducibly. It covers standard processing and analysis steps from raw read mapping up to annotation data integration and calculation of correlating DMRs.

Author information

1
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, 04109, Germany.
2
Transcriptome Bioinformatics, Research Center for Civilization Diseases (LIFE), University of Leipzig, Leipzig, 04109, Germany.
3
ecSeq GmbH, Leipzig, 04275, Germany.

Abstract

Here, we present BAT, a modular bisulfite analysis toolkit, that facilitates the analysis of bisulfite sequencing data. It covers the essential analysis steps of read alignment, quality control, extraction of methylation information, and calling of differentially methylated regions, as well as biologically relevant downstream analyses, such as data integration with gene expression, histone modification data, or transcription factor binding site annotation.

KEYWORDS:

DMRs; DNA methylation; RRBS; WGBS; bisulfite sequencing; epigenetics; integrative analysis; software

Conflict of interest statement

Competing interests: No competing interests were disclosed.

12.
J Pathol. 2017 Oct;243(2):242-254. doi: 10.1002/path.4948. Epub 2017 Sep 5.

Genomic and transcriptomic heterogeneity of colorectal tumours arising in Lynch syndrome.

Author information

1
Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany.
2
Institute of Pathology, Centre for Integrated Oncology, University Hospital Cologne, Cologne, Germany.
3
Translational Epigenomics, University Hospital Cologne, Cologne, Germany.
4
Max Planck Institute for Molecular Genetics, Berlin, Germany.
5
Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Strasbourg, France.
6
Université de Strasbourg, Strasbourg, France.
7
Group of Bioinformatics, Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia.
8
Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
9
Department of Hereditary Tumour Syndromes, Surgical Centre, HELIOS Clinic, University Witten/Herdecke, Wuppertal, Germany.
10
Institute of Human Genetics, University Hospital Bonn, Centre for Hereditary Tumour Syndromes, University of Bonn, Bonn, Germany.
11
Department of Internal Medicine I, University Hospital Bonn, Centre for Hereditary Tumour Syndromes, University of Bonn, Bonn, Germany.
12
Department of Medicine, Haematology and Oncology, Ruhr-University of Bochum, Knappschaftskrankenhaus, Bochum, Germany.
13
Institute of Human Genetics and Anthropology, Heinrich-Heine University, Düsseldorf, Germany.
14
Department of Applied Tumour Biology, Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
15
Clinical Cooperation Unit of Applied Tumour Biology, DKFZ (German Cancer Research Centre) Heidelberg, Germany.
16
Molecular Medicine Partnership Unit, University Hospital Heidelberg and EMBL Heidelberg, Heidelberg, Germany.

Abstract

Colorectal cancer (CRC) arising in Lynch syndrome (LS) comprises tumours with constitutional mutations in DNA mismatch repair genes. There is still a lack of whole-genome and transcriptome studies of LS-CRC to address questions about similarities and differences in mutation and gene expression characteristics between LS-CRC and sporadic CRC, about the molecular heterogeneity of LS-CRC, and about specific mechanisms of LS-CRC genesis linked to dysfunctional mismatch repair in LS colonic mucosa and the possible role of immune editing. Here, we provide a first molecular characterization of LS tumours and of matched tumour-distant reference colonic mucosa based on whole-genome DNA-sequencing and RNA-sequencing analyses. Our data support two subgroups of LS-CRCs, G1 and G2, whereby G1 tumours show a higher number of somatic mutations, a higher amount of microsatellite slippage, and a different mutation spectrum. The gene expression phenotypes support this difference. Reference mucosa of G1 shows a strong immune response associated with the expression of HLA and immune checkpoint genes and the invasion of CD4+ T cells. Such an immune response is not observed in LS tumours, G2 reference and normal (non-Lynch) mucosa, and sporadic CRC. We hypothesize that G1 tumours are edited for escape from a highly immunogenic microenvironment via loss of HLA presentation and T-cell exhaustion. In contrast, G2 tumours seem to develop in a less immunogenic microenvironment where tumour-promoting inflammation parallels tumourigenesis. Larger studies on non-neoplastic mucosa tissue of mutation carriers are required to better understand the early phases of emerging tumours. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

KEYWORDS:

hereditary cancer; immune editing; mismatch repair; tumour heterogeneity

PMID:
28727142
DOI:
10.1002/path.4948
[Indexed for MEDLINE]
Icon for Wiley
13.
J Biotechnol. 2017 Nov 10;261:85-96. doi: 10.1016/j.jbiotec.2017.06.1203. Epub 2017 Jul 1.

Customized workflow development and data modularization concepts for RNA-Sequencing and metatranscriptome experiments.

Author information

1
Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany.
2
Department of Systems Biology & Bioinformatics, University of Rostock, Ulmenstr. 69, 18057 Rostock, Germany.
3
Transcriptome Bioinformatics Group, LIFE Research Complex, University Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany.
4
Department of Systems Biology & Bioinformatics, University of Rostock, Ulmenstr. 69, 18057 Rostock, Germany; Stellenbosch Institute of Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, 7602 Stellenbosch, South Africa.
5
Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany. Electronic address: wolfgang.hess@biologie.uni-freiburg.de.

Abstract

RNA-Sequencing (RNA-Seq) has become a widely used approach to study quantitative and qualitative aspects of transcriptome data. The variety of RNA-Seq protocols, experimental study designs and the characteristic properties of the organisms under investigation greatly affect downstream and comparative analyses. In this review, we aim to explain the impact of structured pre-selection, classification and integration of best-performing tools within modularized data analysis workflows and ready-to-use computing infrastructures towards experimental data analyses. We highlight examples for workflows and use cases that are presented for pro-, eukaryotic and mixed dual RNA-Seq (meta-transcriptomics) experiments. In addition, we are summarizing the expertise of the laboratories participating in the project consortium "Structured Analysis and Integration of RNA-Seq experiments" (de.STAIR) and its integration with the Galaxy-workbench of the RNA Bioinformatics Center (RBC).

KEYWORDS:

Metatranscriptomics; Regulatory RNA; Transcriptomics; Workflow development

PMID:
28676233
DOI:
10.1016/j.jbiotec.2017.06.1203
[Indexed for MEDLINE]
Free full text
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14.
Nucleic Acids Res. 2017 Jul 3;45(W1):W560-W566. doi: 10.1093/nar/gkx409.

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

Author information

1
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, D-79110 Freiburg, Germany.
2
Center for Biological Systems Analysis (ZBSA), University of Freiburg, Habsburgerstr. 49, D-79104 Freiburg, Germany.
3
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany.
4
Berlin Institute for Medical Systems Biology, Max-Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, D-13125, Berlin, Germany.
5
Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria.
6
Department of Systems Biology and Bioinformatics, University of Rostock, Ulmenstr. 69, D-18051 Rostock, Germany.
7
Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Schänzlestr. 1, D-79104 Freiburg, Germany.
8
Department of Urology, Erasmus University Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands.
9
Departments of Biology and Computer Science, Humboldt University, Unter den Linden 6, D-10099 Berlin.
10
Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, D-04103 Leipzig, Germany.
11
Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA.
12
BIOSS Centre for Biological Signaling Studies, University of Freiburg, Schänzlestr. 18, D-79104 Freiburg, Germany.

Abstract

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis.

AVAILABILITY:

The RNA workbench is available at https://github.com/bgruening/galaxy-rna-workbench.

16.
Sci Rep. 2016 Nov 23;6:37393. doi: 10.1038/srep37393.

Changes of bivalent chromatin coincide with increased expression of developmental genes in cancer.

Author information

1
Leipzig University, Chair of Bioinformatics, Leipzig, 04107, Germany.
2
Leipzig University, Transcriptome Bioinformatics Group - Interdisciplinary Center for Bioinformatics, Leipzig, 04107, Germany.
3
Ludwig-Maximilians-University, Institute of Laboratory Medicine, Munich, 81377, Germany.
4
Inserm, U1110 - Institut de Recherche sur les Maladies Virales et Hépatiques, Strasbourg, 67000, France.
5
Université de Strasbourg, Strasbourg, 67000, France.
6
Christian Albrechts University &University Hospital Schleswig-Holstein - Campus Kiel, Institute of Human Genetics, Kiel, 24105, Germany.
7
Christian Albrechts University Kiel &University Hospital Schleswig-Holstein - Campus Kiel, Department of Pediatrics, Kiel, 24105, Germany.
8
Ulm University &Ulm University Medical Center, Institute for Human Genetics, Ulm, 89081, Germany.
9
Leipzig University, LIFE - Leipzig Research Center for Civilization Diseases, Leipzig, 04107, Germany.
10
University of Vienna, Department of Theoretical Chemistry, Vienna, 1090, Austria.
11
Max-Planck-Institute for Mathematics in Sciences, Leipzig, 04103, Germany.
12
Santa Fe Institute, Santa Fe, NM 87501, USA.

Abstract

Bivalent (poised or paused) chromatin comprises activating and repressing histone modifications at the same location. This combination of epigenetic marks at promoter or enhancer regions keeps genes expressed at low levels but poised for rapid activation. Typically, DNA at bivalent promoters is only lowly methylated in normal cells, but frequently shows elevated methylation levels in cancer samples. Here, we developed a universal classifier built from chromatin data that can identify cancer samples solely from hypermethylation of bivalent chromatin. Tested on over 7,000 DNA methylation data sets from several cancer types, it reaches an AUC of 0.92. Although higher levels of DNA methylation are often associated with transcriptional silencing, counter-intuitive positive statistical dependencies between DNA methylation and expression levels have been recently reported for two cancer types. Here, we re-analyze combined expression and DNA methylation data sets, comprising over 5,000 samples, and demonstrate that the conjunction of hypermethylation of bivalent chromatin and up-regulation of the corresponding genes is a general phenomenon in cancer. This up-regulation affects many developmental genes and transcription factors, including dozens of homeobox genes and other genes implicated in cancer. Thus, we reason that the disturbance of bivalent chromatin may be intimately linked to tumorigenesis.

PMID:
27876760
PMCID:
PMC5120258
DOI:
10.1038/srep37393
[Indexed for MEDLINE]
Free PMC Article
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17.
Haematologica. 2016 Nov;101(11):1380-1389. Epub 2016 Jul 6.

Alterations of microRNA and microRNA-regulated messenger RNA expression in germinal center B-cell lymphomas determined by integrative sequencing analysis.

Author information

1
Department of Pediatric Oncology, Hematology and Clinical Immunology, Heinrich-Heine-University, Medical Faculty, Düsseldorf, Germany.
2
Department of Algorithmic Bioinformatics, Heinrich-Heine University, Duesseldorf, Germany.
3
Transcriptome Bioinformatics Group, LIFE Research Center for Civilization Diseases, University of Leipzig, Germany.
4
Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany.
5
Interdisciplinary Center for Bioinformatics, University of Leipzig, Germany.
6
Institute of Pathology, Charité - University Medicine Berlin, Germany.
7
Institute of Human Genetics, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
8
Division Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
9
National Center for Tumor Diseases (NCT), Heidelberg, Germany.
10
German Cancer Consortium (DKTK), Heidelberg, Germany.
11
Department of Pediatric Hematology and Oncology, University Hospital Münster, Germany.
12
Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Germany.
13
Department of Human and Animal Cell Cultures, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
14
Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Heidelberg, Germany.
15
Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology and Bioquant, Heidelberg University, Germany.
16
Friedrich-Ebert Hospital Neumünster, Clinics for Hematology, Oncology and Nephrology, Neumünster, Germany.
17
Department of Internal Medicine II: Hematology and Oncology, University Medical Centre, Campus Kiel, Germany.
18
Hematopathology Section, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
19
EMBL Heidelberg, Genome Biology, Heidelberg, Germany.
20
Institute for Medical Informatics Statistics and Epidemiology, Leipzig, Germany.
21
Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Essen, Germany.
22
Institute of Pathology, University of Würzburg, and Comprehensive Cancer Center Mainfranken, Würzburg, Germany.
23
Hospital of Internal Medicine II, Hematology and Oncology, St-Georg Hospital Leipzig, Germany.
24
Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany.
25
Institute of Pathology, Medical Faculty of the Ulm University, Germany.
26
Department of Pediatric Hematology and Oncology University Hospital Giessen, Germany.
27
Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
28
RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany.
29
Max-Planck-Institute for Mathematics in Sciences, Leipzig, Germany.
30
Santa Fe Institute, NM, USA.
31
Department of Internal Medicine III, University of Ulm, Germany.
32
Department of Hematology and Oncology, Georg-August-University of Göttingen, Germany.

Abstract

MicroRNA are well-established players in post-transcriptional gene regulation. However, information on the effects of microRNA deregulation mainly relies on bioinformatic prediction of potential targets, whereas proof of the direct physical microRNA/target messenger RNA interaction is mostly lacking. Within the International Cancer Genome Consortium Project "Determining Molecular Mechanisms in Malignant Lymphoma by Sequencing", we performed miRnome sequencing from 16 Burkitt lymphomas, 19 diffuse large B-cell lymphomas, and 21 follicular lymphomas. Twenty-two miRNA separated Burkitt lymphomas from diffuse large B-cell lymphomas/follicular lymphomas, of which 13 have shown regulation by MYC. Moreover, we found expression of three hitherto unreported microRNA. Additionally, we detected recurrent mutations of hsa-miR-142 in diffuse large B-cell lymphomas and follicular lymphomas, and editing of the hsa-miR-376 cluster, providing evidence for microRNA editing in lymphomagenesis. To interrogate the direct physical interactions of microRNA with messenger RNA, we performed Argonaute-2 photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation experiments. MicroRNA directly targeted 208 messsenger RNA in the Burkitt lymphomas and 328 messenger RNA in the non-Burkitt lymphoma models. This integrative analysis discovered several regulatory pathways of relevance in lymphomagenesis including Ras, PI3K-Akt and MAPK signaling pathways, also recurrently deregulated in lymphomas by mutations. Our dataset reveals that messenger RNA deregulation through microRNA is a highly relevant mechanism in lymphomagenesis.

PMID:
27390358
PMCID:
PMC5394868
DOI:
10.3324/haematol.2016.143891
[Indexed for MEDLINE]
Free PMC Article
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18.
Sci Rep. 2016 Oct 7;6:34589. doi: 10.1038/srep34589.

Differential transcriptional responses to Ebola and Marburg virus infection in bat and human cells.

Author information

1
RNA Bioinformatics and High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany.
2
Institute of Virology, Philipps University Marburg, Hans-Meerwein-Str. 2, 35043 Marburg, Germany.
3
German Center for Infection Research (DZIF), partner site Gießen-Marburg-Langen, Hans-Meerwein Str. 2, 35043, Marburg, Germany.
4
Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
5
FLI Leibniz Institute for Age Research, Beutenbergstraße 11, 07745 Jena, Germany.
6
Transcriptome Bioinformatics, Junior Research Group, Leipzig Research Center for Civilization Diseases, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
7
Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark.
8
Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark.
9
Theoretical Biochemistry Group, Institute of Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Vienna, Austria.
10
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110, Freiburg, Germany.
11
Research Group Theoretical Systems Biology, Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
12
Institute of Computer Science, Martin-Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120, Halle/Saale, Germany.
13
Department of Soil Ecology, UFZ - Helmholtz Centre for Environmental Research, Theodor-Lieser-Str. 4, 06120, Halle/Saale, Germany.
14
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany.
15
Biozentrum, University of Basel, Klingelbergstraße 50/70, CH-4056, Basel, Switzerland.
16
Chair of Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
17
Junior Professorship for Computational EvoDevo, Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
18
TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
19
Paul-Flechsig-Institute for Brain Research, University of Leipzig, Jahnallee 54, 04109, Leipzig, Germany.
20
Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI), Systems Biology and Bioinformatics, Beutenbergstraße 11a, 07745, Jena, Germany.
21
Department of Bioanalytical Ecotoxicology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany.
22
Doctoral School of Science and Technology, AZM Center for Biotechnology Research, Lebanese University, Tripoli, Lebanon.
23
TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University Mainz gGmbH, Mainz, Germany.
24
Institute of Aquaculture, University of Stirling, Stirling FK9 4LA, Scotland, U.K.
25
Medical University of Vienna, Center for Anatomy and Cell Biology, Währingerstraße 13, 1090, Vienna, Austria.
26
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany.
27
Research group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Währingerstraße 29, 1090, Vienna, Austria.
28
Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Brunswiker Str. 10, 24105, Kiel, Germany.

Abstract

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.

PMID:
27713552
PMCID:
PMC5054393
DOI:
10.1038/srep34589
[Indexed for MEDLINE]
Free PMC Article
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19.
Clin Infect Dis. 2016 Aug 15;63 Suppl 2:S52-6. doi: 10.1093/cid/ciw317.

Patient-Reported Outcome Assessments as Endpoints in Studies in Infectious Diseases.

Author information

1
George Washington University School of Medicine.
2
ICON PLC, San Francisco, California.
3
Foundation for the National Institutes of Health, Bethesda, Maryland.
4
Llorens Consulting, Bay Village, Ohio.
5
Talbot Advisors, LLC, Anna Maria, Florida.

Abstract

The goal of administering medical interventions is to help patients live longer or live better. In keeping with this goal, there has been increasing interest in taking the "voice" of the patient into account during the development process, specifically in the evaluation of treatment benefits of medical interventions, and use of patient-centered outcome data to justify reimbursement. Patient-reported outcomes (PROs) are outcome assessments (OAs) used to define endpoints that can provide direct evidence of treatment benefit on how patients feel or function. When PROs are appropriately developed, they can increase the efficiency and clinical relevance of clinical trials. Several PROs have been developed for OA in specific infectious diseases indications, and more are under development. PROs also hold promise for use in evaluating adherence, adverse effects, satisfaction with care, and routine clinical practice.

KEYWORDS:

clinical practice; clinical trials; endpoints; infectious diseases; patient-reported outcomes

PMID:
27481954
PMCID:
PMC5006216
DOI:
10.1093/cid/ciw317
[Indexed for MEDLINE]
Free PMC Article
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20.
Nat Commun. 2016 Jun 24;7:11807. doi: 10.1038/ncomms11807.

MYC/MIZ1-dependent gene repression inversely coordinates the circadian clock with cell cycle and proliferation.

Author information

1
Heidelberg University, Biochemistry Center, Im Neuenheimer Feld 328, D-69120 Heidelberg, Germany.
2
Division Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.
3
Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Im Neuenheimer Feld 364, D-69120 Heidelberg, Germany.

Abstract

The circadian clock and the cell cycle are major cellular systems that organize global physiology in temporal fashion. It seems conceivable that the potentially conflicting programs are coordinated. We show here that overexpression of MYC in U2OS cells attenuates the clock and conversely promotes cell proliferation while downregulation of MYC strengthens the clock and reduces proliferation. Inhibition of the circadian clock is crucially dependent on the formation of repressive complexes of MYC with MIZ1 and subsequent downregulation of the core clock genes BMAL1 (ARNTL), CLOCK and NPAS2. We show furthermore that BMAL1 expression levels correlate inversely with MYC levels in 102 human lymphomas. Our data suggest that MYC acts as a master coordinator that inversely modulates the impact of cell cycle and circadian clock on gene expression.

PMID:
27339797
PMCID:
PMC4931031
DOI:
10.1038/ncomms11807
[Indexed for MEDLINE]
Free PMC Article
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