Author
Frank Kauff
Other affiliations: University of Giessen, Duke University
Bio: Frank Kauff is an academic researcher from Kaiserslautern University of Technology. The author has contributed to research in topics: Phylogenetic tree & Bendamustine. The author has an hindex of 31, co-authored 47 publications receiving 9052 citations. Previous affiliations of Frank Kauff include University of Giessen & Duke University.
Papers
More filters
TL;DR: Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
Abstract: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence. le formats and multiple sequence alignments, dealing with 3D macromolecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
3,855 citations
Duke University1, Oregon State University2, Clark University3, Natural History Museum4, University of Minnesota5, Field Museum of Natural History6, Kaiserslautern University of Technology7, University of Arizona8, New York Botanical Garden9, University of Iowa10, Technische Universität Darmstadt11, University of Maine12, United States Department of Agriculture13, University of Georgia14, University of Alabama15, University of California, Berkeley16, University of Kansas17, Aberystwyth University18, West Virginia University19, Washington State University20, Harvard University21, University of North Carolina at Chapel Hill22, Centraalbureau voor Schimmelcultures23, University of Tennessee24, Okayama University25, University of Kassel26, Brandon University27, Pennsylvania State University28, Leibniz Association29, University of Hamburg30, Royal Botanic Garden Edinburgh31
TL;DR: It is indicated that there may have been at least four independent losses of the flagellum in the kingdom Fungi, and the enigmatic microsporidia seem to be derived from an endoparasitic chytrid ancestor similar to Rozella allomycis, on the earliest diverging branch of the fungal phylogenetic tree.
Abstract: The ancestors of fungi are believed to be simple aquatic forms with flagellated spores, similar to members of the extant phylum Chytridiomycota (chytrids). Current classifications assume that chytrids form an early-diverging clade within the kingdom Fungi and imply a single loss of the spore flagellum, leading to the diversification of terrestrial fungi. Here we develop phylogenetic hypotheses for Fungi using data from six gene regions and nearly 200 species. Our results indicate that there may have been at least four independent losses of the flagellum in the kingdom Fungi. These losses of swimming spores coincided with the evolution of new mechanisms of spore dispersal, such as aerial dispersal in mycelial groups and polar tube eversion in the microsporidia (unicellular forms that lack mitochondria). The enigmatic microsporidia seem to be derived from an endoparasitic chytrid ancestor similar to Rozella allomycis, on the earliest diverging branch of the fungal phylogenetic tree.
1,682 citations
Duke University1, University of Minnesota2, Clark University3, University of Graz4, Oregon State University5, University of Gdańsk6, Agriculture and Agri-Food Canada7, National Center for Agricultural Utilization Research8, American Museum of Natural History9, Harvard University10, New York Botanical Garden11, University of British Columbia12, Washington State University13, United States Department of Agriculture14, Free University of Berlin15, University of Tokyo16
TL;DR: This study provides a phylogenetic synthesis for the Fungi and a framework for future phylogenetic studies on fungi and the impact of this newly discovered phylogenetic structure on supraordinal classifications is discussed.
Abstract: Based on an overview of progress in molecular systematics of the true fungi (Fungi/Eumycota) since 1990, little overlap was found among single-locus data matrices, which explains why no large-scale multilocus phylogenetic analysis had been undertaken to reveal deep relationships among fungi. As part of the project ‘‘Assembling the Fungal Tree of Life’’ (AFTOL), results of four Bayesian analyses are reported with complementary bootstrap assessment of phylogenetic confidence based on (1) a combined two-locus data set (nucSSU and nucLSU rDNA) with 558 species representing all traditionally recognized fungal phyla (Ascomycota, Basidiomycota, Chytridiomycota, Zygomycota) and the Glomeromycota, (2) a combined three-locus data set (nucSSU, nucLSU, and mitSSU rDNA) with 236 species, (3) a combined three-locus data set (nucSSU, nucLSU rDNA, and RPB2) with 157 species, and (4) a combined four-locus data set (nucSSU, nucLSU, mitSSU rDNA, and RPB2) with 103 species. Because of the lack of complementarity among single-locus data sets, the last three analyses included only members of the Ascomycota and Basidiomycota. The four-locus analysis resolved multiple deep relationships within the Ascomycota and Basidiomycota that were not revealed previously or that received only weak support in previous studies. The impact of this newly discovered phylogenetic structure on supraordinal classifications is discussed. Based on these results and reanalysis of subcellular data, current knowledge of the evolution of septal features of fungal hyphae is synthesized, and a preliminary reassessment of ascomal evolution is presented. Based on previously unpublished data and sequences from GenBank, this study provides a phylogenetic synthesis for the Fungi and a framework for future phylogenetic studies on fungi.
754 citations
Oregon State University1, Yale University2, Duke University3, University of Tennessee4, Clark University5, Kaiserslautern University of Technology6, Centraalbureau voor Schimmelcultures7, University of Copenhagen8, University of Tabriz9, Harvard University10, University of Pretoria11, ATCC12, Louisiana State University13, University of Texas at Austin14, Aberystwyth University15, United States Department of Agriculture16, Field Museum of Natural History17, Pennsylvania State University18, University of California, Berkeley19, University of North Carolina at Chapel Hill20, Stellenbosch University21, Free University of Berlin22, Washington State University23, Brandon University24, Landcare Research25, University of Helsinki26, University of Giessen27, University of Nottingham28, Swedish Museum of Natural History29, Royal Botanic Garden Edinburgh30
TL;DR: A 6-gene, 420-species maximum-likelihood phylogeny of Ascomycota, the largest phylum of Fungi, and a phylogenetic informativeness analysis of all 6 genes and a series of ancestral character state reconstructions support a terrestrial, saprobic ecology as ancestral are presented.
Abstract: We present a 6-gene, 420-species maximum-likelihood phylogeny of Ascomycota, the largest phylum of Fungi. This analysis is the most taxonomically complete to date with species sampled from all 15 currently circumscribed classes. A number of superclass-level nodes that have previously evaded resolution and were unnamed in classifications of the Fungi are resolved for the first time. Based on the 6-gene phylogeny we conducted a phylogenetic informativeness analysis of all 6 genes and a series of ancestral character state reconstructions that focused on morphology of sporocarps, ascus dehiscence, and evolution of nutritional modes and ecologies. A gene-by-gene assessment of phylogenetic informativeness yielded higher levels of informativeness for protein genes (RPB1, RPB2, and TEF1) as compared with the ribosomal genes, which have been the standard bearer in fungal systematics. Our reconstruction of sporocarp characters is consistent with 2 origins for multicellular sexual reproductive structures in Ascomycota, once in the common ancestor of Pezizomycotina and once in the common ancestor of Neolectomycetes. This first report of dual origins of ascomycete sporocarps highlights the complicated nature of assessing homology of morphological traits across Fungi. Furthermore, ancestral reconstruction supports an open sporocarp with an exposed hymenium (apothecium) as the primitive morphology for Pezizomycotina with multiple derivations of the partially (perithecia) or completely enclosed (cleistothecia) sporocarps. Ascus dehiscence is most informative at the class level within Pezizomycotina with most superclass nodes reconstructed equivocally. Character-state reconstructions support a terrestrial, saprobic ecology as ancestral. In contrast to previous studies, these analyses support multiple origins of lichenization events with the loss of lichenization as less frequent and limited to terminal, closely related species.
592 citations
TL;DR: Use of phylogenetic analyses that incorporate these newly recovered fungi and ancestral state reconstructions that take into account phylogenetic uncertainty provide the basis for estimating trophic transition networks in the Ascomycota and provide a first set of hypotheses regarding the evolution of symbiotrophy and saprotrophy in the most species-rich fungal phylum.
Abstract: Fungi associated with photosynthetic organisms are major determinants of terrestrial biomass, nutrient cycling, and ecosystem productivity from the poles to the equator. Whereas most fungi are known because of their fruit bodies (e.g., saprotrophs), symptoms (e.g., pathogens), or emergent properties as symbionts (e.g., lichens), the majority of fungal diversity is thought to occur among species that rarely manifest their presence with visual cues on their substrate (e.g., the apparently hyperdiverse fungal endophytes associated with foliage of plants). Fungal endophytes are ubiquitous among all lineages of land plants and live within overtly healthy tissues without causing disease, but the evolutionary origins of these highly diverse symbionts have not been explored. Here, we show that a key to understanding both the evolution of endophytism and the diversification of the most species-rich phylum of Fungi (Ascomycota) lies in endophyte-like fungi that can be isolated from the interior of apparently healthy lichens. These "endolichenic" fungi are distinct from lichen my- cobionts or any other previously recognized fungal associates of lichens, represent the same major lineages of Ascomycota as do endophytes, largely parallel the high diversity of endophytes from the arctic to the tropics, and preferentially associate with green algal photobionts in lichen thalli. Using phylogenetic analyses that incorporate these newly recovered fungi and ancestral state reconstructions that take into account phylogenetic uncertainty, we show that endolichenism is an incubator for the evolution of endophytism. In turn, endophytism is evolutionarily transient, with endophytic lineages frequently transitioning to and from pathogenicity. Although symbiotrophic lineages frequently give rise to free-living saprotrophs, reversions to symbiosis are rare. Together, these results provide the basis for estimating trophic transition networks in the Ascomycota and provide a first set of hypotheses regarding the evolution of symbiotrophy and saprotrophy in the most species-rich fungal phylum. (Ancestral state reconstruction; Ascomycota; Bayesian analysis; endolichenic fungi; fungal endophytes; lichens; pathogens; phylogeny; saprotrophy; symbiotrophy; trophic transition network.)
320 citations
Cited by
More filters
TL;DR: This work presents HTSeq, a Python library to facilitate the rapid development of custom scripts for high-throughput sequencing data analysis, and presents htseq-count, a tool developed with HTSequ that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.
Abstract: Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an opensource software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
15,744 citations
University of California, Berkeley1, Stellenbosch University2, University of Jyväskylä3, University of Cambridge4, Google5, University of Toronto6, University of Birmingham7, Temple University8, University of British Columbia9, Amazon.com10, University of Georgia11, University of Oxford12, Los Alamos National Laboratory13, University of California, Irvine14
TL;DR: In this paper, the authors review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data, and their evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
Abstract: Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
7,624 citations
University of California, Berkeley1, Stellenbosch University2, University of Jyväskylä3, University of Cambridge4, Google5, University of Toronto6, University of Birmingham7, Temple University8, Amazon.com9, University of British Columbia10, University of Georgia11, University of Oxford12, Los Alamos National Laboratory13, University of California, Irvine14
TL;DR: How a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data is reviewed.
Abstract: Array programming provides a powerful, compact, expressive syntax for accessing, manipulating, and operating on data in vectors, matrices, and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material science, engineering, finance, and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves and the first imaging of a black hole. Here we show how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring, and analyzing scientific data. NumPy is the foundation upon which the entire scientific Python universe is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Because of its central position in the ecosystem, NumPy increasingly plays the role of an interoperability layer between these new array computation libraries.
4,342 citations
Conrad L. Schoch1, Keith A. Seifert, Sabine M. Huhndorf2, Vincent Robert3 +157 more•Institutions (59)
TL;DR: Among the regions of the ribosomal cistron, the internal transcribed spacer (ITS) region has the highest probability of successful identification for the broadest range of fungi, with the most clearly defined barcode gap between inter- and intraspecific variation.
Abstract: Six DNA regions were evaluated as potential DNA barcodes for Fungi, the second largest kingdom of eukaryotic life, by a multinational, multilaboratory consortium. The region of the mitochondrial cytochrome c oxidase subunit 1 used as the animal barcode was excluded as a potential marker, because it is difficult to amplify in fungi, often includes large introns, and can be insufficiently variable. Three subunits from the nuclear ribosomal RNA cistron were compared together with regions of three representative protein-coding genes (largest subunit of RNA polymerase II, second largest subunit of RNA polymerase II, and minichromosome maintenance protein). Although the protein-coding gene regions often had a higher percent of correct identification compared with ribosomal markers, low PCR amplification and sequencing success eliminated them as candidates for a universal fungal barcode. Among the regions of the ribosomal cistron, the internal transcribed spacer (ITS) region has the highest probability of successful identification for the broadest range of fungi, with the most clearly defined barcode gap between inter- and intraspecific variation. The nuclear ribosomal large subunit, a popular phylogenetic marker in certain groups, had superior species resolution in some taxonomic groups, such as the early diverging lineages and the ascomycete yeasts, but was otherwise slightly inferior to the ITS. The nuclear ribosomal small subunit has poor species-level resolution in fungi. ITS will be formally proposed for adoption as the primary fungal barcode marker to the Consortium for the Barcode of Life, with the possibility that supplementary barcodes may be developed for particular narrowly circumscribed taxonomic groups.
4,116 citations
TL;DR: Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
Abstract: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence. le formats and multiple sequence alignments, dealing with 3D macromolecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.
3,855 citations