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Kollmann A, Lohr D, Ankenbrand MJ, Bille M, Terekhov M, Hock M, Elabyad I, Baltes S, Reiter T, Schnitter F, Bauer WR, Hofmann U, Schreiber LM. Cardiac function in a large animal model of myocardial infarction at 7 T: deep learning based automatic segmentation increases reproducibility. Sci Rep 2024; 14:11009. [PMID: 38744988 DOI: 10.1038/s41598-024-61417-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/06/2024] [Indexed: 05/16/2024] Open
Abstract
Cardiac magnetic resonance (CMR) imaging allows precise non-invasive quantification of cardiac function. It requires reliable image segmentation for myocardial tissue. Clinically used software usually offers automatic approaches for this step. These are, however, designed for segmentation of human images obtained at clinical field strengths. They reach their limits when applied to preclinical data and ultrahigh field strength (such as CMR of pigs at 7 T). In our study, eleven animals (seven with myocardial infarction) underwent four CMR scans each. Short-axis cine stacks were acquired and used for functional cardiac analysis. End-systolic and end-diastolic images were labelled manually by two observers and inter- and intra-observer variability were assessed. Aiming to make the functional analysis faster and more reproducible, an established deep learning (DL) model for myocardial segmentation in humans was re-trained using our preclinical 7 T data (n = 772 images and labels). We then tested the model on n = 288 images. Excellent agreement in parameters of cardiac function was found between manual and DL segmentation: For ejection fraction (EF) we achieved a Pearson's r of 0.95, an Intraclass correlation coefficient (ICC) of 0.97, and a Coefficient of variability (CoV) of 6.6%. Dice scores were 0.88 for the left ventricle and 0.84 for the myocardium.
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Affiliation(s)
- Alena Kollmann
- Comprehensive Heart Failure Center (CHFC), Chair of Molecular and Cellular Imaging, University Hospital Würzburg, Würzburg, Germany
| | - David Lohr
- Comprehensive Heart Failure Center (CHFC), Chair of Molecular and Cellular Imaging, University Hospital Würzburg, Würzburg, Germany.
| | - Markus J Ankenbrand
- Faculty of Biology, Center for Computational and Theoretical Biology (CCTB), University of Würzburg, Würzburg, Germany
| | - Maya Bille
- Comprehensive Heart Failure Center (CHFC), Chair of Molecular and Cellular Imaging, University Hospital Würzburg, Würzburg, Germany
| | - Maxim Terekhov
- Comprehensive Heart Failure Center (CHFC), Chair of Molecular and Cellular Imaging, University Hospital Würzburg, Würzburg, Germany
| | - Michael Hock
- Comprehensive Heart Failure Center (CHFC), Chair of Molecular and Cellular Imaging, University Hospital Würzburg, Würzburg, Germany
| | - Ibrahim Elabyad
- Comprehensive Heart Failure Center (CHFC), Chair of Molecular and Cellular Imaging, University Hospital Würzburg, Würzburg, Germany
| | - Steffen Baltes
- Comprehensive Heart Failure Center (CHFC), Chair of Molecular and Cellular Imaging, University Hospital Würzburg, Würzburg, Germany
| | - Theresa Reiter
- Comprehensive Heart Failure Center (CHFC), Chair of Molecular and Cellular Imaging, University Hospital Würzburg, Würzburg, Germany
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - Florian Schnitter
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - Wolfgang R Bauer
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - Ulrich Hofmann
- Department of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany
| | - Laura M Schreiber
- Comprehensive Heart Failure Center (CHFC), Chair of Molecular and Cellular Imaging, University Hospital Würzburg, Würzburg, Germany
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Quaresma A, Ankenbrand MJ, Garcia CAY, Rufino J, Honrado M, Amaral J, Brodschneider R, Brusbardis V, Gratzer K, Hatjina F, Kilpinen O, Pietropaoli M, Roessink I, van der Steen J, Vejsnæs F, Pinto MA, Keller A. Semi-automated sequence curation for reliable reference datasets in ITS2 vascular plant DNA (meta-)barcoding. Sci Data 2024; 11:129. [PMID: 38272945 PMCID: PMC10810873 DOI: 10.1038/s41597-024-02962-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
One of the most critical steps for accurate taxonomic identification in DNA (meta)-barcoding is to have an accurate DNA reference sequence dataset for the marker of choice. Therefore, developing such a dataset has been a long-term ambition, especially in the Viridiplantae kingdom. Typically, reference datasets are constructed with sequences downloaded from general public databases, which can carry taxonomic and other relevant errors. Herein, we constructed a curated (i) global dataset, (ii) European crop dataset, and (iii) 27 datasets for the EU countries for the ITS2 barcoding marker of vascular plants. To that end, we first developed a pipeline script that entails (i) an automated curation stage comprising five filters, (ii) manual taxonomic correction for misclassified taxa, and (iii) manual addition of newly sequenced species. The pipeline allows easy updating of the curated datasets. With this approach, 13% of the sequences, corresponding to 7% of species originally imported from GenBank, were discarded. Further, 259 sequences were manually added to the curated global dataset, which now comprises 307,977 sequences of 111,382 plant species.
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Affiliation(s)
- Andreia Quaresma
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
- Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
- Departamento de Biologia, Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, S/N, Edifício FC4, 4169-007, Porto, Portugal
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, 4485-661, Vairão, Vila do Conde, Portugal
- BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, 4485-661, Vairão, Vila do Conde, Portugal
| | - Markus J Ankenbrand
- Center for Computational and Theoretical Biology, Faculty of Biology, Julius-Maximilians-Universität Würzburg, Klara-Oppenheimer-Weg 32, 97074, Würzburg, Germany
| | - Carlos Ariel Yadró Garcia
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
- Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
| | - José Rufino
- Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
- Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Bragança, Portugal
| | - Mónica Honrado
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
- Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
| | - Joana Amaral
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
- Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
| | - Robert Brodschneider
- Institute of Biology, University of Graz, Universitätsplatz 2, 8010, Graz, Austria
| | - Valters Brusbardis
- Latvian Beekeepers' Association (LBA), Rigas iela 22, LV-3004, Jelgava, Latvia
| | - Kristina Gratzer
- Institute of Biology, University of Graz, Universitätsplatz 2, 8010, Graz, Austria
| | - Fani Hatjina
- Ellinikos Georgikos Organismos DIMITRA (ELGO- DIMITRA), Kourtidou 56-58, GR-11145, Athina, Greece
| | - Ole Kilpinen
- Danish Beekeepers Association (DBF), Fulbyvej 15, DK-4180, Sorø, Denmark
| | - Marco Pietropaoli
- Istituto Zooprofilattico Sperimentale del Lazio e della Toscana "M. Aleandri" (IZSLT), Via Appia Nuova 1411, IT-00178, Roma, Italy
| | - Ivo Roessink
- Wageningen Environmental Research, WageningenUniversity&Research, Droevendaalsesteeg 3, 6700 AA, Wageningen, Netherlands
| | | | - Flemming Vejsnæs
- Danish Beekeepers Association (DBF), Fulbyvej 15, DK-4180, Sorø, Denmark
| | - M Alice Pinto
- Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
- Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal
| | - Alexander Keller
- Cellular and Organismic Interactions, Biocenter, Faculty of Biology, Ludwig-Maximilians-Universität München, Großhaderner Str. 2-4, 82152, Planegg-Martinsried, Germany.
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Faist H, Ankenbrand MJ, Sickel W, Hentschel U, Keller A, Deeken R. Opportunistic Bacteria of Grapevine Crown Galls Are Equipped with the Genomic Repertoire for Opine Utilization. Genome Biol Evol 2023; 15:evad228. [PMID: 38085065 PMCID: PMC10745273 DOI: 10.1093/gbe/evad228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Young grapevines (Vitis vinifera) suffer and eventually can die from the crown gall disease caused by the plant pathogen Allorhizobium vitis (Rhizobiaceae). Virulent members of A. vitis harbor a tumor-inducing plasmid and induce formation of crown galls due to the oncogenes encoded on the transfer DNA. The expression of oncogenes in transformed host cells induces unregulated cell proliferation and metabolic and physiological changes. The crown gall produces opines uncommon to plants, which provide an important nutrient source for A. vitis harboring opine catabolism enzymes. Crown galls host a distinct bacterial community, and the mechanisms establishing a crown gall-specific bacterial community are currently unknown. Thus, we were interested in whether genes homologous to those of the tumor-inducing plasmid coexist in the genomes of the microbial species coexisting in crown galls. We isolated 8 bacterial strains from grapevine crown galls, sequenced their genomes, and tested their virulence and opine utilization ability in bioassays. In addition, the 8 genome sequences were compared with 34 published bacterial genomes, including closely related plant-associated bacteria not from crown galls. Homologous genes for virulence and opine anabolism were only present in the virulent Rhizobiaceae. In contrast, homologs of the opine catabolism genes were present in all strains including the nonvirulent members of the Rhizobiaceae and non-Rhizobiaceae. Gene neighborhood and sequence identity of the opine degradation cluster of virulent and nonvirulent strains together with the results of the opine utilization assay support the important role of opine utilization for cocolonization in crown galls, thereby shaping the crown gall community.
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Affiliation(s)
- Hanna Faist
- Center for Health & Bioresources, Bioresources Unit, AIT Austrian Institute of Technology GmbH, Tulln 3430, Austria
- Julius-von-Sachs Institute for Biological Sciences, Molecular Plant Physiology and Biophysics, University of Würzburg, Würzburg 97082, Germany
| | - Markus J Ankenbrand
- Faculty of Biology, Center for Computational and Theoretical Biology, University of Würzburg, Würzburg 97074, Germany
| | - Wiebke Sickel
- Institute of Biodiversity, Thuenen-Institute of Biodiversity, Braunschweig 38116, Germany
| | - Ute Hentschel
- RD3 Marine Ecology, RU Marine Symbioses, GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel 24105, Germany
- Sektion Biologie, Christian-Albrechts University of Kiel, Kiel 24105, Germany
| | - Alexander Keller
- Cellular and Organismic Networks, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried 82152, Germany
| | - Rosalia Deeken
- Julius-von-Sachs Institute for Biological Sciences, Molecular Plant Physiology and Biophysics, University of Würzburg, Würzburg 97082, Germany
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4
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Dirk R, Fischer JL, Schardt S, Ankenbrand MJ, Fischer SC. Recognition and reconstruction of cell differentiation patterns with deep learning. PLoS Comput Biol 2023; 19:e1011582. [PMID: 37889897 PMCID: PMC10631711 DOI: 10.1371/journal.pcbi.1011582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 11/08/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran's index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms.
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Affiliation(s)
- Robin Dirk
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
| | - Jonas L. Fischer
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
| | - Simon Schardt
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
| | - Markus J. Ankenbrand
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
| | - Sabine C. Fischer
- Julius-Maximilians-Universität Würzburg, Fakultät für Biologie, Center for Computational and Theoretical Biology, Klara-Oppenheimer-Weg 32, Campus Hubland Nord, Germany
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5
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Li L, Wu F, Wang S, Luo X, Martín-Isla C, Zhai S, Zhang J, Liu Y, Zhang Z, Ankenbrand MJ, Jiang H, Zhang X, Wang L, Arega TW, Altunok E, Zhao Z, Li F, Ma J, Yang X, Puybareau E, Oksuz I, Bricq S, Li W, Punithakumar K, Tsaftaris SA, Schreiber LM, Yang M, Liu G, Xia Y, Wang G, Escalera S, Zhuang X. MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images. Med Image Anal 2023; 87:102808. [PMID: 37087838 DOI: 10.1016/j.media.2023.102808] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 01/11/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023]
Abstract
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. Note that MyoPS refers to both myocardial pathology segmentation and the challenge in this paper. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore the potential of solutions, as well as to provide a benchmark for future research. The average Dice scores of submitted algorithms were 0.614±0.231 and 0.644±0.153 for myocardial scars and edema, respectively. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).
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Affiliation(s)
- Lei Li
- School of Data Science, Fudan University, Shanghai, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Fuping Wu
- School of Data Science, Fudan University, Shanghai, China.
| | - Sihan Wang
- School of Data Science, Fudan University, Shanghai, China.
| | - Xinzhe Luo
- School of Data Science, Fudan University, Shanghai, China
| | - Carlos Martín-Isla
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Shuwei Zhai
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianpeng Zhang
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Yanfei Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, China
| | - Zhen Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Markus J Ankenbrand
- Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center, Wuerzburg University Hospitals, Wuerzburg, Germany
| | - Haochuan Jiang
- School of Engineering, University of Edinburgh, Edinburgh, UK; School of Robotics, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Xiaoran Zhang
- Department of Electrical and Computer Engineering, University of California, LA, USA
| | - Linhong Wang
- Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China
| | | | - Elif Altunok
- Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey
| | - Zhou Zhao
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France
| | - Feiyan Li
- Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jun Ma
- Department of Mathematics, Nanjing University of Science and Technology, Nanjing, China
| | - Xiaoping Yang
- Department of Mathematics, Nanjing University, Nanjing, China
| | - Elodie Puybareau
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France
| | - Ilkay Oksuz
- Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey
| | - Stephanie Bricq
- ImViA Laboratory, Université Bourgogne Franche-Comté, Dijon, France
| | - Weisheng Li
- Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China
| | | | | | - Laura M Schreiber
- Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center, Wuerzburg University Hospitals, Wuerzburg, Germany
| | - Mingjing Yang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Guocai Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, China; National Engineering Laboratory for Robot Visual Perception and Control Technology, Changsha, China
| | - Yong Xia
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Sergio Escalera
- Departament de Matemàtiques & Informàtica, Universitat de Barcelona, Barcelona, Spain; Computer Vision Center, Universitat Autònoma de Barcelona, Spain
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
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Schilcher F, Hilsmann L, Ankenbrand MJ, Krischke M, Mueller MJ, Steffan-Dewenter I, Scheiner R. Corrigendum: Honeybees are buffered against undernourishment during larval stages. Front Insect Sci 2023; 3:1146464. [PMID: 38469509 PMCID: PMC10926457 DOI: 10.3389/finsc.2023.1146464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 03/13/2024]
Abstract
[This corrects the article DOI: 10.3389/finsc.2022.951317.].
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Affiliation(s)
- Felix Schilcher
- Behavioral Physiology and Sociobiology, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians- Universität Würzburg, Würzburg, Germany
| | - Lioba Hilsmann
- Behavioral Physiology and Sociobiology, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians- Universität Würzburg, Würzburg, Germany
| | - Markus J. Ankenbrand
- Center for Computational and Theoretical Biology (CCTB), Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Markus Krischke
- Julius-von-Sachs-Institute of Biosciences, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Martin J. Mueller
- Julius-von-Sachs-Institute of Biosciences, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Ingolf Steffan-Dewenter
- Animal Ecology and Tropical Biology, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Ricarda Scheiner
- Behavioral Physiology and Sociobiology, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians- Universität Würzburg, Würzburg, Germany
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7
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Hackl T, Laurenceau R, Ankenbrand MJ, Bliem C, Cariani Z, Thomas E, Dooley KD, Arellano AA, Hogle SL, Berube P, Leventhal GE, Luo E, Eppley JM, Zayed AA, Beaulaurier J, Stepanauskas R, Sullivan MB, DeLong EF, Biller SJ, Chisholm SW. Novel integrative elements and genomic plasticity in ocean ecosystems. Cell 2023; 186:47-62.e16. [PMID: 36608657 DOI: 10.1016/j.cell.2022.12.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 09/16/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023]
Abstract
Horizontal gene transfer accelerates microbial evolution. The marine picocyanobacterium Prochlorococcus exhibits high genomic plasticity, yet the underlying mechanisms are elusive. Here, we report a novel family of DNA transposons-"tycheposons"-some of which are viral satellites while others carry cargo, such as nutrient-acquisition genes, which shape the genetic variability in this globally abundant genus. Tycheposons share distinctive mobile-lifecycle-linked hallmark genes, including a deep-branching site-specific tyrosine recombinase. Their excision and integration at tRNA genes appear to drive the remodeling of genomic islands-key reservoirs for flexible genes in bacteria. In a selection experiment, tycheposons harboring a nitrate assimilation cassette were dynamically gained and lost, thereby promoting chromosomal rearrangements and host adaptation. Vesicles and phage particles harvested from seawater are enriched in tycheposons, providing a means for their dispersal in the wild. Similar elements are found in microbes co-occurring with Prochlorococcus, suggesting a common mechanism for microbial diversification in the vast oligotrophic oceans.
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Affiliation(s)
- Thomas Hackl
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA; Groningen Institute for Evolutionary Life Sciences, University of Groningen, 9700CC Groningen, the Netherlands.
| | - Raphaël Laurenceau
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA
| | - Markus J Ankenbrand
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA; University of Würzburg, Center for Computational and Theoretical Biology, 97070 Würzburg, Germany
| | - Christina Bliem
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA
| | - Zev Cariani
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA
| | - Elaina Thomas
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA
| | - Keven D Dooley
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA
| | - Aldo A Arellano
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA
| | - Shane L Hogle
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA
| | - Paul Berube
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA
| | - Gabriel E Leventhal
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA
| | - Elaine Luo
- Daniel K. Inouye Center for Microbial Oceanography, Research and Education, University of Hawai'i Manoa, Honolulu, HI 96822, USA
| | - John M Eppley
- Daniel K. Inouye Center for Microbial Oceanography, Research and Education, University of Hawai'i Manoa, Honolulu, HI 96822, USA
| | - Ahmed A Zayed
- EMERGE Biology Integration Institute, Ohio State University, Columbus, OH 43210, USA; Center of Microbiome Science, Ohio State University, Columbus, OH 43210, USA
| | | | | | - Matthew B Sullivan
- Department of Microbiology & Department of Civil, Environmental, and Geodetic Engineering, Ohio State University, Columbus, OH 43210, USA; EMERGE Biology Integration Institute, Ohio State University, Columbus, OH 43210, USA; Center of Microbiome Science, Ohio State University, Columbus, OH 43210, USA
| | - Edward F DeLong
- Daniel K. Inouye Center for Microbial Oceanography, Research and Education, University of Hawai'i Manoa, Honolulu, HI 96822, USA
| | - Steven J Biller
- Wellesley College, Department of Biological Sciences, Wellesley, MA 02481, USA
| | - Sallie W Chisholm
- Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA 02139, USA; Massachusetts Institute of Technology, Department of Biology, Cambridge, MA 02139, USA.
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Hackl T, Laurenceau R, Ankenbrand MJ, Bliem C, Cariani Z, Thomas E, Dooley KD, Arellano AA, Hogle SL, Berube P, Leventhal GE, Luo E, Eppley JM, Zayed AA, Beaulaurier J, Stepanauskas R, Sullivan MB, DeLong EF, Biller SJ, Chisholm SW. Novel integrative elements and genomic plasticity in ocean ecosystems. Cell 2023. [DOI: doi.org/10.1016/j.cell.2022.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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9
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Keller A, Ankenbrand MJ, Bruelheide H, Dekeyzer S, Enquist BJ, Erfanian MB, Falster DS, Gallagher RV, Hammock J, Kattge J, Leonhardt SD, Madin JS, Maitner B, Neyret M, Onstein RE, Pearse WD, Poelen JH, Salguero‐Gomez R, Schneider FD, Tóth AB, Penone C. Ten (mostly) simple rules to future‐proof trait data in ecological and evolutionary sciences. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.14033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Alexander Keller
- Cellular and Organismic Networks, Faculty of Biology Ludwig‐Maximilians‐Universität München Martinsried Germany
| | - Markus J. Ankenbrand
- Center for Computational and Theoretical Biology Julius‐Maximilians‐Universität Würzburg Würzburg Germany
| | - Helge Bruelheide
- Institute of Biology/Geobotany and Botanical Garden Martin Luther University Halle‐Wittenberg Halle (Saale) Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig Germany
| | | | - Brian J. Enquist
- Department of Ecology and Evolutionary Biology University of Arizona Tucson Arizona USA
- The Santa Fe Institute Santa Fe New Mexico USA
| | | | - Daniel S. Falster
- Evolution & Ecology Research Centre University of New South Wales Sydney Sydney New South Wales Australia
| | - Rachael V. Gallagher
- Hawkesbury Institute for the Environment Western Sydney University Richmond New South Wales Australia
| | - Jennifer Hammock
- National Museum of Natural History, Smithsonian Institution Washington District of Columbia USA
| | - Jens Kattge
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig Germany
- Max Planck Institute for Biogeochemistry Jena Germany
| | - Sara D. Leonhardt
- Plant‐Insect Interactions, TUM School of Life Science Systems Technical University of Munich Freising Germany
| | - Joshua S. Madin
- Hawai'i Institute of Marine Biology University of Hawai'i at Manoa Kāne'ohe Hawai'i USA
| | - Brian Maitner
- Department of Geography University at Buffalo Buffalo New York USA
- Department of Environment and Sustainability University at Buffalo Buffalo New York USA
| | - Margot Neyret
- Senckenberg Biodiversity and Climate Research Center (SBik‐F) Frankfurt Germany
| | - Renske E. Onstein
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig Germany
- Naturalis Biodiversity Center Leiden The Netherlands
| | | | - Jorrit H. Poelen
- Ronin Institute for Independent Scholarship Montclair New Jersey USA
- Cheadle Center for Biodiversity and Ecological Restoration, UC Santa Barbara Santa Barbara California USA
| | | | - Florian D. Schneider
- Senckenberg Biodiversity and Climate Research Center (SBik‐F) Frankfurt Germany
- ISOE ‐ Institute for Social‐Ecological Research Frankfurt am Main Germany
| | - Anikó B. Tóth
- Centre for Ecosystem Science, School of Biological Earth and Environmental Sciences University of New South Wales Sydney New South Wales Australia
| | - Caterina Penone
- Institute of Plant Sciences University of Bern Bern Switzerland
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10
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Schilcher F, Hilsmann L, Ankenbrand MJ, Krischke M, Mueller MJ, Steffan-Dewenter I, Scheiner R. Honeybees are buffered against undernourishment during larval stages. Front Insect Sci 2022; 2:951317. [PMID: 38468773 PMCID: PMC10926507 DOI: 10.3389/finsc.2022.951317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 10/24/2022] [Indexed: 03/13/2024]
Abstract
The negative impact of juvenile undernourishment on adult behavior has been well reported for vertebrates, but relatively little is known about invertebrates. In honeybees, nutrition has long been known to affect task performance and timing of behavioral transitions. Whether and how a dietary restriction during larval development affects the task performance of adult honeybees is largely unknown. We raised honeybees in-vitro, varying the amount of a standardized diet (150 µl, 160 µl, 180 µl in total). Emerging adults were marked and inserted into established colonies. Behavioral performance of nurse bees and foragers was investigated and physiological factors known to be involved in the regulation of social organization were quantified. Surprisingly, adult honeybees raised under different feeding regimes did not differ in any of the behaviors observed. No differences were observed in physiological parameters apart from weight. Honeybees were lighter when undernourished (150 µl), while they were heavier under the overfed treatment (180 µl) compared to the control group raised under a normal diet (160 µl). These data suggest that dietary restrictions during larval development do not affect task performance or physiology in this social insect despite producing clear effects on adult weight. We speculate that possible effects of larval undernourishment might be compensated during the early period of adult life.
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Affiliation(s)
- Felix Schilcher
- Behavioral Physiology and Sociobiology, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Lioba Hilsmann
- Behavioral Physiology and Sociobiology, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Markus J. Ankenbrand
- Center for Computational and Theoretical Biology (CCTB), Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Markus Krischke
- Julius-von-Sachs-Institute of Biosciences, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Martin J. Mueller
- Julius-von-Sachs-Institute of Biosciences, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Ingolf Steffan-Dewenter
- Animal Ecology and Tropical Biology, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Ricarda Scheiner
- Behavioral Physiology and Sociobiology, Theodor-Boveri-Institute, Biocenter, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
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11
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John M, Ankenbrand MJ, Artmann C, Freudenthal JA, Korte A, Grimm DG. Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions. Bioinformatics 2022; 38:ii5-ii12. [PMID: 36124808 PMCID: PMC9486594 DOI: 10.1093/bioinformatics/btac455] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) are an integral tool for studying the architecture of complex genotype and phenotype relationships. Linear mixed models (LMMs) are commonly used to detect associations between genetic markers and a trait of interest, while at the same time allowing to account for population structure and cryptic relatedness. Assumptions of LMMs include a normal distribution of the residuals and that the genetic markers are independent and identically distributed-both assumptions are often violated in real data. Permutation-based methods can help to overcome some of these limitations and provide more realistic thresholds for the discovery of true associations. Still, in practice, they are rarely implemented due to the high computational complexity. RESULTS We propose permGWAS, an efficient LMM reformulation based on 4D tensors that can provide permutation-based significance thresholds. We show that our method outperforms current state-of-the-art LMMs with respect to runtime and that permutation-based thresholds have lower false discovery rates for skewed phenotypes compared to the commonly used Bonferroni threshold. Furthermore, using permGWAS we re-analyzed more than 500 Arabidopsis thaliana phenotypes with 100 permutations each in less than 8 days on a single GPU. Our re-analyses suggest that applying a permutation-based threshold can improve and refine the interpretation of GWAS results. AVAILABILITY AND IMPLEMENTATION permGWAS is open-source and publicly available on GitHub for download: https://github.com/grimmlab/permGWAS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maura John
- To whom correspondence should be addressed. or
| | - Markus J Ankenbrand
- Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany
| | - Carolin Artmann
- Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany
| | - Jan A Freudenthal
- Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany
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12
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Heidenreich JF, Gassenmaier T, Ankenbrand MJ, Bley TA, Wech T. Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction. Eur J Radiol 2021; 141:109817. [PMID: 34144308 DOI: 10.1016/j.ejrad.2021.109817] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 05/07/2021] [Accepted: 06/07/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy. METHODS In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework ("nnU-net") was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels. Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the Sørensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson's r correlation and Bland-Altman analysis. RESULTS The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 ± 0.03 for myocardium and 0.72 ± 0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network. CONCLUSION The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR.
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Affiliation(s)
- Julius F Heidenreich
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany.
| | - Tobias Gassenmaier
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany
| | - Markus J Ankenbrand
- Department of Cellular and Molecular Imaging, Comprehensive Heart Failure Center, University Hospital Würzburg, Germany; Center for Computational and Theoretical Biology, University of Würzburg, Germany
| | - Thorsten A Bley
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany
| | - Tobias Wech
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Germany
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13
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Ankenbrand MJ, Shainberg L, Hock M, Lohr D, Schreiber LM. Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI. BMC Med Imaging 2021; 21:27. [PMID: 33588786 PMCID: PMC7885570 DOI: 10.1186/s12880-021-00551-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 01/24/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. RESULTS We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. CONCLUSIONS Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.
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Affiliation(s)
- Markus J Ankenbrand
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany.
| | - Liliia Shainberg
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
| | - Michael Hock
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
| | - David Lohr
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
| | - Laura M Schreiber
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Am Schwarzenberg 15, 97078, Würzburg, Germany
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14
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Freudenthal JA, Pfaff S, Terhoeven N, Korte A, Ankenbrand MJ, Förster F. A systematic comparison of chloroplast genome assembly tools. Genome Biol 2020; 21:254. [PMID: 32988404 PMCID: PMC7520963 DOI: 10.1186/s13059-020-02153-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 08/22/2020] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Chloroplasts are intracellular organelles that enable plants to conduct photosynthesis. They arose through the symbiotic integration of a prokaryotic cell into an eukaryotic host cell and still contain their own genomes with distinct genomic information. Plastid genomes accommodate essential genes and are regularly utilized in biotechnology or phylogenetics. Different assemblers that are able to assess the plastid genome have been developed. These assemblers often use data of whole genome sequencing experiments, which usually contain reads from the complete chloroplast genome. RESULTS The performance of different assembly tools has never been systematically compared. Here, we present a benchmark of seven chloroplast assembly tools, capable of succeeding in more than 60% of known real data sets. Our results show significant differences between the tested assemblers in terms of generating whole chloroplast genome sequences and computational requirements. The examination of 105 data sets from species with unknown plastid genomes leads to the assembly of 20 novel chloroplast genomes. CONCLUSIONS We create docker images for each tested tool that are freely available for the scientific community and ensure reproducibility of the analyses. These containers allow the analysis and screening of data sets for chloroplast genomes using standard computational infrastructure. Thus, large scale screening for chloroplasts within genomic sequencing data is feasible.
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Affiliation(s)
- Jan A. Freudenthal
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074 Germany
- AnaLife Data Science, Wiesengrund 16, Würzburg, 97295 Waldbrunn Germany
| | - Simon Pfaff
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074 Germany
- Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland, Würzburg, 97074 Germany
| | - Niklas Terhoeven
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074 Germany
- AnaLife Data Science, Wiesengrund 16, Würzburg, 97295 Waldbrunn Germany
| | - Arthur Korte
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074 Germany
| | - Markus J. Ankenbrand
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074 Germany
- AnaLife Data Science, Wiesengrund 16, Würzburg, 97295 Waldbrunn Germany
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center, University Hospital Würzburg, Josef-Schneider-Str. 2, Würzburg, 97080 Germany
| | - Frank Förster
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074 Germany
- Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland, Würzburg, 97074 Germany
- Fraunhofer IME-BR, Ohlebergsweg 12, Gießen, 35392 Germany
- Bioinformatics Core Facility of the University of Gießen, Heinrich-Buff-Ring 58, Gießen, 35392 Germany
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15
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Freudenthal JA, Pfaff S, Terhoeven N, Korte A, Ankenbrand MJ, Förster F. A systematic comparison of chloroplast genome assembly tools. Genome Biol 2020; 21:254. [PMID: 32988404 DOI: 10.1101/665869] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 08/22/2020] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND Chloroplasts are intracellular organelles that enable plants to conduct photosynthesis. They arose through the symbiotic integration of a prokaryotic cell into an eukaryotic host cell and still contain their own genomes with distinct genomic information. Plastid genomes accommodate essential genes and are regularly utilized in biotechnology or phylogenetics. Different assemblers that are able to assess the plastid genome have been developed. These assemblers often use data of whole genome sequencing experiments, which usually contain reads from the complete chloroplast genome. RESULTS The performance of different assembly tools has never been systematically compared. Here, we present a benchmark of seven chloroplast assembly tools, capable of succeeding in more than 60% of known real data sets. Our results show significant differences between the tested assemblers in terms of generating whole chloroplast genome sequences and computational requirements. The examination of 105 data sets from species with unknown plastid genomes leads to the assembly of 20 novel chloroplast genomes. CONCLUSIONS We create docker images for each tested tool that are freely available for the scientific community and ensure reproducibility of the analyses. These containers allow the analysis and screening of data sets for chloroplast genomes using standard computational infrastructure. Thus, large scale screening for chloroplasts within genomic sequencing data is feasible.
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Affiliation(s)
- Jan A Freudenthal
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074, Germany
| | - Simon Pfaff
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074, Germany
- AnaLife Data Science, Wiesengrund 16, Würzburg, 97295 Waldbrunn, Germany
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center, University Hospital Würzburg, Josef-Schneider-Str. 2, Würzburg, 97080, Germany
| | - Niklas Terhoeven
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074, Germany
- AnaLife Data Science, Wiesengrund 16, Würzburg, 97295 Waldbrunn, Germany
| | - Arthur Korte
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074, Germany
- Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland, Würzburg, 97074, Germany
| | - Markus J Ankenbrand
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074, Germany
- AnaLife Data Science, Wiesengrund 16, Würzburg, 97295 Waldbrunn, Germany
| | - Frank Förster
- Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord, Würzburg, 97074, Germany.
- Department of Bioinformatics, University of Würzburg, Biozentrum, Am Hubland, Würzburg, 97074, Germany.
- Fraunhofer IME-BR, Ohlebergsweg 12, Gießen, 35392, Germany.
- Bioinformatics Core Facility of the University of Gießen, Heinrich-Buff-Ring 58, Gießen, 35392, Germany.
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16
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Gallagher RV, Falster DS, Maitner BS, Salguero-Gómez R, Vandvik V, Pearse WD, Schneider FD, Kattge J, Poelen JH, Madin JS, Ankenbrand MJ, Penone C, Feng X, Adams VM, Alroy J, Andrew SC, Balk MA, Bland LM, Boyle BL, Bravo-Avila CH, Brennan I, Carthey AJR, Catullo R, Cavazos BR, Conde DA, Chown SL, Fadrique B, Gibb H, Halbritter AH, Hammock J, Hogan JA, Holewa H, Hope M, Iversen CM, Jochum M, Kearney M, Keller A, Mabee P, Manning P, McCormack L, Michaletz ST, Park DS, Perez TM, Pineda-Munoz S, Ray CA, Rossetto M, Sauquet H, Sparrow B, Spasojevic MJ, Telford RJ, Tobias JA, Violle C, Walls R, Weiss KCB, Westoby M, Wright IJ, Enquist BJ. Open Science principles for accelerating trait-based science across the Tree of Life. Nat Ecol Evol 2020; 4:294-303. [PMID: 32066887 DOI: 10.1038/s41559-020-1109-6] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 01/10/2020] [Indexed: 01/22/2023]
Abstract
Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.
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Affiliation(s)
- Rachael V Gallagher
- Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia.
| | - Daniel S Falster
- Evolution and Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Brian S Maitner
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Roberto Salguero-Gómez
- Department of Zoology, Oxford University, Oxford, UK.,Centre for Biodiversity and Conservation Science, University of Queensland, Brisbane, Queensland, Australia.,Evolutionary Demography Laboratory, Max Plank Institute for Demographic Research, Rostock, Germany
| | - Vigdis Vandvik
- Department of Biological Sciences, University of Bergen, Bergen, Norway.,Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
| | - William D Pearse
- Ecology Center and Department of Biology, Utah State University, Logan, UT, USA
| | | | - Jens Kattge
- Max Planck Institute for Biogeochemistry, Jena, Germany.,German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | | | - Joshua S Madin
- Hawai'i Institute of Marine Biology, University of Hawai'i at Manoa, Manoa, HI, USA
| | - Markus J Ankenbrand
- Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany.,Center for Computational and Theoretical Biology, Biocenter, University of Wuerzburg, Wuerzburg, Germany.,Comprehensive Heart Failure Center, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Caterina Penone
- Institute of Plant Sciences, University of Bern, Bern, Switzerland
| | - Xiao Feng
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Vanessa M Adams
- Discipline of Geography and Spatial Sciences, University of Tasmania, Hobart, Tasmania, Australia
| | - John Alroy
- Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Samuel C Andrew
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australian Capital Territory, Australia
| | - Meghan A Balk
- Bio5 Institute, University of Arizona, Tucson, AZ, USA
| | - Lucie M Bland
- School of Life and Environmental Sciences, Centre for Integrative Ecology, Deakin University, Geelong, Victoria, Australia
| | - Brad L Boyle
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Catherine H Bravo-Avila
- Department of Biology, University of Miami, Miami, FL, USA.,Fairchild Tropical Botanic Garden, Coral Gables, FL, USA
| | - Ian Brennan
- Research School of Biology, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Alexandra J R Carthey
- Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Renee Catullo
- Research School of Biology, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Brittany R Cavazos
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
| | - Dalia A Conde
- Species360 Conservation Science Alliance, Bloomington, MN, USA.,Interdisciplinary Center on Population Dynamics, University of Southern Denmark, Odense, Denmark.,Department of Biology, University of Southern Denmark, Odense, Denmark
| | - Steven L Chown
- School of Biological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Belen Fadrique
- Department of Biology, University of Miami, Miami, FL, USA
| | - Heloise Gibb
- Department of Ecology, Environment and Evolution and Centre for Future Landscapes, La Trobe University, Melbourne, Victoria, Australia
| | - Aud H Halbritter
- Department of Biological Sciences, University of Bergen, Bergen, Norway.,Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
| | - Jennifer Hammock
- National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
| | - J Aaron Hogan
- International Center for Tropical Botany, Department of Biological Sciences, Florida International University, Miami, FL, USA
| | - Hamish Holewa
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australian Capital Territory, Australia
| | - Michael Hope
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australian Capital Territory, Australia
| | - Colleen M Iversen
- Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Malte Jochum
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.,Institute of Plant Sciences, University of Bern, Bern, Switzerland.,Institute of Biology, Leipzig University, Leipzig, Germany
| | - Michael Kearney
- School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alexander Keller
- Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany.,Center for Computational and Theoretical Biology, Biocenter, University of Wuerzburg, Wuerzburg, Germany
| | - Paula Mabee
- Department of Biology, University of South Dakota, Vermillion, SD, USA
| | - Peter Manning
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt, Germany
| | - Luke McCormack
- Center for Tree Science, The Morton Arboretum, Lisle, IL, USA
| | - Sean T Michaletz
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Daniel S Park
- Department of Organismic and Evolutionary Biology and Harvard University Herbaria, Harvard University, Cambridge, MA, USA
| | - Timothy M Perez
- Department of Biology, University of Miami, Miami, FL, USA.,Fairchild Tropical Botanic Garden, Coral Gables, FL, USA
| | - Silvia Pineda-Munoz
- School of Biological Sciences and School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Courtenay A Ray
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Maurizio Rossetto
- National Herbarium of New South Wales, Royal Botanic Gardens and Domain Trust, Sydney, New South Wales, Australia.,Queensland Alliance of Agriculture and Food Innovation, University of Queensland, Brisbane, Queensland, Australia
| | - Hervé Sauquet
- Evolution and Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia.,National Herbarium of New South Wales, Royal Botanic Gardens and Domain Trust, Sydney, New South Wales, Australia.,Ecologie Systématique Evolution, Univ. Paris-Sud, CNRS, AgroParisTech, Universite Paris-Saclay, Orsay, France
| | - Benjamin Sparrow
- TERN / School of Biological Sciences, Faculty of Science, The University of Adelaide, Adelaide, South Australia, Australia
| | - Marko J Spasojevic
- Department of Evolution, Ecology, and Organismal Biology, University of California Riverside, Riverside, CA, USA
| | - Richard J Telford
- Department of Biological Sciences, University of Bergen, Bergen, Norway.,Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
| | - Joseph A Tobias
- Department of Life Sciences, Imperial College London, London, UK
| | - Cyrille Violle
- CEFE, CNRS, Univ Montpellier, Université Paul Valéry Montpellier, Montpellier, France
| | | | | | - Mark Westoby
- Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Ian J Wright
- Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Brian J Enquist
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA.,Santa Fe Institute, Santa Fe, NM, USA
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17
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Keller A, Hohlfeld S, Kolter A, Schultz J, Gemeinholzer B, Ankenbrand MJ. BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. Bioinformatics 2020; 36:2630-2631. [DOI: 10.1093/bioinformatics/btz960] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 11/06/2019] [Accepted: 12/31/2019] [Indexed: 11/13/2022] Open
Abstract
Abstract
Summary
DNA barcoding and meta-barcoding have become irreplaceable in research and applications, where identification of taxa alone or within a mixture, respectively, becomes relevant. Pioneering studies were in the microbiological context, yet nowadays also plants and animals become targeted. Given the variety of markers used, formatting requirements for classifiers and constant growth of primary databases, there is a need for dedicated reference database creation. We developed a web and command-line interface to generate such on-the-fly for any applicable marker and taxonomic group with optional filtering, formatting and restriction specific for (meta-)barcoding purposes. Also, databases optionally receive a DOI, making them well-documented with meta-data, publicly sharable and citable.
Availability and implementation
source code: https://www.github.com/molbiodiv/bcdatabaser, webservice: https://bcdatabaser.molecular.eco, documentation: https://molbiodiv.github.io/bcdatabaser.
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Affiliation(s)
- Alexander Keller
- Center for Computational and Theoretical Biology, University of Würzburg, Hubland Nord, Würzburg 97074, Germany
- Department of Bioinformatics, University of Würzburg, Biocenter, Am Hubland, Würzburg 97074, Germany
| | - Sonja Hohlfeld
- Center for Computational and Theoretical Biology, University of Würzburg, Hubland Nord, Würzburg 97074, Germany
| | - Andreas Kolter
- Department of Biology, Systematic Botany, Justus-Liebig-University Giessen, Giessen 35392, Germany
| | - Jörg Schultz
- Center for Computational and Theoretical Biology, University of Würzburg, Hubland Nord, Würzburg 97074, Germany
- Department of Bioinformatics, University of Würzburg, Biocenter, Am Hubland, Würzburg 97074, Germany
| | - Birgit Gemeinholzer
- Department of Biology, Systematic Botany, Justus-Liebig-University Giessen, Giessen 35392, Germany
| | - Markus J Ankenbrand
- Center for Computational and Theoretical Biology, University of Würzburg, Hubland Nord, Würzburg 97074, Germany
- Department of Bioinformatics, University of Würzburg, Biocenter, Am Hubland, Würzburg 97074, Germany
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18
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Voulgari‐Kokota A, Ankenbrand MJ, Grimmer G, Steffan‐Dewenter I, Keller A. Linking pollen foraging of megachilid bees to their nest bacterial microbiota. Ecol Evol 2019; 9:10788-10800. [PMID: 31624582 PMCID: PMC6787775 DOI: 10.1002/ece3.5599] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 07/25/2019] [Accepted: 07/28/2019] [Indexed: 12/17/2022] Open
Abstract
Solitary bees build their nests by modifying the interior of natural cavities, and they provision them with food by importing collected pollen. As a result, the microbiota of the solitary bee nests may be highly dependent on introduced materials. In order to investigate how the collected pollen is associated with the nest microbiota, we used metabarcoding of the ITS2 rDNA and the 16S rDNA to simultaneously characterize the pollen composition and the bacterial communities of 100 solitary bee nest chambers belonging to seven megachilid species. We found a weak correlation between bacterial and pollen alpha diversity and significant associations between the composition of pollen and that of the nest microbiota, contributing to the understanding of the link between foraging and bacteria acquisition for solitary bees. Since solitary bees cannot establish bacterial transmission routes through eusociality, this link could be essential for obtaining bacterial symbionts for this group of valuable pollinators. OPEN RESEARCH BADGES This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://www.ebi.ac.uk/ena/data/view/PRJEB27223, https://www.ebi.ac.uk/ena/data/view/PRJEB31610, and https://doi.org/10.5061/dryad.qk36k8q.
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Affiliation(s)
- Anna Voulgari‐Kokota
- Department of BioinformaticsBiocenterUniversity of WuerzburgWuerzburgGermany
- Center for Computational and Theoretical BiologyUniversity of WuerzburgWuerzburgGermany
| | - Markus J. Ankenbrand
- Department of BioinformaticsBiocenterUniversity of WuerzburgWuerzburgGermany
- Center for Computational and Theoretical BiologyUniversity of WuerzburgWuerzburgGermany
| | - Gudrun Grimmer
- Department of Animal Ecology and Tropical BiologyBiocenterUniversity of WuerzburgWuerzburgGermany
| | - Ingolf Steffan‐Dewenter
- Department of Animal Ecology and Tropical BiologyBiocenterUniversity of WuerzburgWuerzburgGermany
| | - Alexander Keller
- Department of BioinformaticsBiocenterUniversity of WuerzburgWuerzburgGermany
- Center for Computational and Theoretical BiologyUniversity of WuerzburgWuerzburgGermany
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19
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Keller A, Brandel A, Becker MC, Balles R, Abdelmohsen UR, Ankenbrand MJ, Sickel W. Wild bees and their nests host Paenibacillus bacteria with functional potential of avail. Microbiome 2018; 6:229. [PMID: 30579360 PMCID: PMC6303958 DOI: 10.1186/s40168-018-0614-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 12/13/2018] [Indexed: 05/10/2023]
Abstract
BACKGROUND In previous studies, the gram-positive firmicute genus Paenibacillus was found with significant abundances in nests of wild solitary bees. Paenibacillus larvae is well-known for beekeepers as a severe pathogen causing the fatal honey bee disease American foulbrood, and other members of the genus are either secondary invaders of European foulbrood or considered a threat to honey bees. We thus investigated whether Paenibacillus is a common bacterium associated with various wild bees and hence poses a latent threat to honey bees visiting the same flowers. RESULTS We collected 202 samples from 82 individuals or nests of 13 bee species at the same location and screened each for Paenibacillus using high-throughput sequencing-based 16S metabarcoding. We then isolated the identified strain Paenibacillus MBD-MB06 from a solitary bee nest and sequenced its genome. We did find conserved toxin genes and such encoding for chitin-binding proteins, yet none specifically related to foulbrood virulence or chitinases. Phylogenomic analysis revealed a closer relationship to strains of root-associated Paenibacillus rather than strains causing foulbrood or other accompanying diseases. We found anti-microbial evidence within the genome, confirmed by experimental bioassays with strong growth inhibition of selected fungi as well as gram-positive and gram-negative bacteria. CONCLUSIONS The isolated wild bee associate Paenibacillus MBD-MB06 is a common, but irregularly occurring part of wild bee microbiomes, present on adult body surfaces and guts and within nests especially in megachilids. It was phylogenetically and functionally distinct from harmful members causing honey bee colony diseases, although it shared few conserved proteins putatively toxic to insects that might indicate ancestral predisposition for the evolution of insect pathogens within the group. By contrast, our strain showed anti-microbial capabilities and the genome further indicates abilities for chitin-binding and biofilm-forming, suggesting it is likely a useful associate to avoid fungal penetration of the bee cuticula and a beneficial inhabitant of nests to repress fungal threats in humid and nutrient-rich environments of wild bee nests.
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Affiliation(s)
- Alexander Keller
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
- Present Address: Center for Computational and Theoretical Biology, Biocenter, University of Würzburg, Hubland Nord, 97074, Würzburg, Germany.
- Present Address: Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
| | - Annette Brandel
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany
- Present Address: Faculty of Biology, Albert-Ludwigs-University Freiburg, Schänzlestraße 1, 79104, Freiburg, Germany
- Present Address: BIOSS Centre for Biological Signalling Studies, Albert-Ludwigs-University Freiburg, Schänzlestraße 18, 79104, Freiburg, Germany
| | - Mira C Becker
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany
- Present Address: Department of Behavioral Physiology & Sociobiology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany
| | - Rebecca Balles
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany
| | - Usama Ramadan Abdelmohsen
- Department of Botany II, Julius-von-Sachs Institute for Biological Sciences, University of Würzburg, Julius-von-Sachs-Platz 3, 97082, Würzburg, Germany
- Present Address: Department of Pharmacognosy, Faculty of Pharmacy, Minia University, Minia, 61519, Egypt
| | - Markus J Ankenbrand
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany
- Present Address: Center for Computational and Theoretical Biology, Biocenter, University of Würzburg, Hubland Nord, 97074, Würzburg, Germany
- Present Address: Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany
| | - Wiebke Sickel
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany
- Present Address: Molecular Biology of the Rhizosphere, Institute of Crop Science and Resource Conservation, Nussallee 13, 53115, Bonn, Germany
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20
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Affiliation(s)
- Markus J. Ankenbrand
- Department of BioinformaticsUniversity of Würzburg Würzburg Germany
- Center for Computational and Theoretical BiologyUniversity of Würzburg Würzburg Germany
| | - Sonja C. Y. Hohlfeld
- Center for Computational and Theoretical BiologyUniversity of Würzburg Würzburg Germany
| | | | - Frank Förster
- Department of BioinformaticsUniversity of Würzburg Würzburg Germany
- Center for Computational and Theoretical BiologyUniversity of Würzburg Würzburg Germany
- Fraunhofer Institute for Molecular Biology and Applied Ecology IMEBranch for Bioresources of the Fraunhofer IME Gießen Germany
| | - Alexander Keller
- Department of BioinformaticsUniversity of Würzburg Würzburg Germany
- Center for Computational and Theoretical BiologyUniversity of Würzburg Würzburg Germany
- Department of Animal Ecology and Tropical BiologyUniversity of Würzburg Würzburg Germany
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21
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Ankenbrand MJ, Terhoeven N, Hohlfeld S, Förster F, Keller A. biojs-io-biom, a BioJS component for handling data in Biological Observation Matrix (BIOM) format. F1000Res 2017; 5:2348. [PMID: 28105307 PMCID: PMC5224677 DOI: 10.12688/f1000research.9618.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/09/2017] [Indexed: 11/20/2022] Open
Abstract
The Biological Observation Matrix (BIOM) format is widely used to store data from high-throughput studies. It aims at increasing interoperability of bioinformatic tools that process this data. However, due to multiple versions and implementation details, working with this format can be tricky. Currently, libraries in Python, R and Perl are available, whilst such for JavaScript are lacking. Here, we present a BioJS component for parsing BIOM data in all format versions. It supports import, modification, and export via a unified interface. This module aims to facilitate the development of web applications that use BIOM data. Finally, we demonstrate its usefulness by two applications that already use this component. Availability: https://github.com/molbiodiv/biojs-io-biom, https://dx.doi.org/10.5281/zenodo.218277.
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Affiliation(s)
- Markus J Ankenbrand
- Department of Animal Ecology and Tropical Biology (Zoology III), University of Würzburg, Würzburg, Germany
| | - Niklas Terhoeven
- Department of Plant Physiology and Biophysics (Botany I), University of Würzburg, Würzburg, Germany; Center for Computational and Theoretical Biology (CCTB), University of Würzburg, Würzburg, Germany
| | - Sonja Hohlfeld
- Department of Animal Ecology and Tropical Biology (Zoology III), University of Würzburg, Würzburg, Germany
| | - Frank Förster
- Center for Computational and Theoretical Biology (CCTB), University of Würzburg, Würzburg, Germany; Department of Bioinformatics, University of Würzburg, Würzburg, Germany
| | - Alexander Keller
- Department of Animal Ecology and Tropical Biology (Zoology III), University of Würzburg, Würzburg, Germany
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22
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Ankenbrand MJ, Weber L, Becker D, Förster F, Bemm F. TBro: visualization and management of de novo transcriptomes. Database (Oxford) 2016; 2016:baw146. [PMID: 28025338 PMCID: PMC5199188 DOI: 10.1093/database/baw146] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 10/12/2016] [Accepted: 10/18/2016] [Indexed: 01/24/2023]
Abstract
RNA sequencing (RNA-seq) has become a powerful tool to understand molecular mechanisms and/or developmental programs. It provides a fast, reliable and cost-effective method to access sets of expressed elements in a qualitative and quantitative manner. Especially for non-model organisms and in absence of a reference genome, RNA-seq data is used to reconstruct and quantify transcriptomes at the same time. Even SNPs, InDels, and alternative splicing events are predicted directly from the data without having a reference genome at hand. A key challenge, especially for non-computational personnal, is the management of the resulting datasets, consisting of different data types and formats. Here, we present TBro, a flexible de novo transcriptome browser, tackling this challenge. TBro aggregates sequences, their annotation, expression levels as well as differential testing results. It provides an easy-to-use interface to mine the aggregated data and generate publication-ready visualizations. Additionally, it supports users with an intuitive cart system, that helps collecting and analysing biological meaningful sets of transcripts. TBro’s modular architecture allows easy extension of its functionalities in the future. Especially, the integration of new data types such as proteomic quantifications or array-based gene expression data is straightforward. Thus, TBro is a fully featured yet flexible transcriptome browser that supports approaching complex biological questions and enhances collaboration of numerous researchers. Database URL: tbro.carnivorom.com
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Affiliation(s)
- Markus J Ankenbrand
- Department of Animal Ecology and Tropical Biology, Biocenter, Am Hubland, 97074 Würzburg, Germany
| | - Lorenz Weber
- Department of Bioinformatics, Biocenter, Am Hubland, 97074 Würzburg, Germany.,Center for Computational and Theoretical Biology, University of Würzburg, 97074 Würzburg, Germany
| | - Dirk Becker
- Institute for Molecular Plant Physiology and Biophysics, University of Würzburg, 97082 Würzburg, Germany
| | - Frank Förster
- Department of Bioinformatics, Biocenter, Am Hubland, 97074 Würzburg, Germany.,Center for Computational and Theoretical Biology, University of Würzburg, 97074 Würzburg, Germany
| | - Felix Bemm
- Department of Bioinformatics, Biocenter, Am Hubland, 97074 Würzburg, Germany .,Department Molecular Biology (Detlef Weigel), Max-Planck-Institute for Developmental Biology, 72076 Tübingen, Germany
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23
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Abstract
The need for multi-gene analyses in scientific fields such as phylogenetics and DNA barcoding has increased in recent years. In particular, these approaches are increasingly important for differentiating bacterial species, where reliance on the standard 16S rDNA marker can result in poor resolution. Additionally, the assembly of bacterial genomes has become a standard task due to advances in next-generation sequencing technologies. We created a bioinformatic pipeline, bcgTree, which uses assembled bacterial genomes either from databases or own sequencing results from the user to reconstruct their phylogenetic history. The pipeline automatically extracts 107 essential single-copy core genes, found in a majority of bacteria, using hidden Markov models and performs a partitioned maximum-likelihood analysis. Here, we describe the workflow of bcgTree and, as a proof-of-concept, its usefulness in resolving the phylogeny of 293 publically available bacterial strains of the genus Lactobacillus. We also evaluate its performance in both low- and high-level taxonomy test sets. The tool is freely available at github ( https://github.com/iimog/bcgTree ) and our institutional homepage ( http://www.dna-analytics.biozentrum.uni-wuerzburg.de ).
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Affiliation(s)
- Markus J Ankenbrand
- Department of Animal Ecology and Tropical Biology, University of Würzburg, Germany.,Department of Animal Ecology and Tropical Biology, University of Würzburg, Germany
| | - Alexander Keller
- Department of Animal Ecology and Tropical Biology, University of Würzburg, Germany.,Department of Animal Ecology and Tropical Biology, University of Würzburg, Germany
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24
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Ankenbrand MJ, Keller A, Wolf M, Schultz J, Förster F. ITS2 Database V: Twice as Much: Table 1. Mol Biol Evol 2015; 32:3030-2. [DOI: 10.1093/molbev/msv174] [Citation(s) in RCA: 166] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Accepted: 07/25/2015] [Indexed: 11/13/2022] Open
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25
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Sickel W, Ankenbrand MJ, Grimmer G, Holzschuh A, Härtel S, Lanzen J, Steffan-Dewenter I, Keller A. Increased efficiency in identifying mixed pollen samples by meta-barcoding with a dual-indexing approach. BMC Ecol 2015; 15:20. [PMID: 26194794 PMCID: PMC4509727 DOI: 10.1186/s12898-015-0051-y] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 06/25/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Meta-barcoding of mixed pollen samples constitutes a suitable alternative to conventional pollen identification via light microscopy. Current approaches however have limitations in practicability due to low sample throughput and/or inefficient processing methods, e.g. separate steps for amplification and sample indexing. RESULTS We thus developed a new primer-adapter design for high throughput sequencing with the Illumina technology that remedies these issues. It uses a dual-indexing strategy, where sample-specific combinations of forward and reverse identifiers attached to the barcode marker allow high sample throughput with a single sequencing run. It does not require further adapter ligation steps after amplification. We applied this protocol to 384 pollen samples collected by solitary bees and sequenced all samples together on a single Illumina MiSeq v2 flow cell. According to rarefaction curves, 2,000-3,000 high quality reads per sample were sufficient to assess the complete diversity of 95% of the samples. We were able to detect 650 different plant taxa in total, of which 95% were classified at the species level. Together with the laboratory protocol, we also present an update of the reference database used by the classifier software, which increases the total number of covered global plant species included in the database from 37,403 to 72,325 (93% increase). CONCLUSIONS This study thus offers improvements for the laboratory and bioinformatical workflow to existing approaches regarding data quantity and quality as well as processing effort and cost-effectiveness. Although only tested for pollen samples, it is furthermore applicable to other research questions requiring plant identification in mixed and challenging samples.
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Affiliation(s)
- Wiebke Sickel
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
| | - Markus J Ankenbrand
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
| | - Gudrun Grimmer
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
| | - Andrea Holzschuh
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
| | - Stephan Härtel
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
| | - Jonathan Lanzen
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
| | - Ingolf Steffan-Dewenter
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
| | - Alexander Keller
- Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074, Würzburg, Germany.
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