University of the Free State

About: University of the Free State is a education organization based out in Bloemfontein, South Africa. It is known for research contribution in the topics: Population & Photoluminescence. The organization has 8429 authors who have published 16531 publications receiving 240919 citations. The organization is also known as: UFS.

Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi

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.

The Top 10 fungal pathogens in molecular plant pathology

Abstract: The aim of this review was to survey all fungal pathologists with an association with the journal Molecular Plant Pathology and ask them to nominate which fungal pathogens they would place in a 'Top 10' based on scientific/economic importance. The survey generated 495 votes from the international community, and resulted in the generation of a Top 10 fungal plant pathogen list for Molecular Plant Pathology. The Top 10 list includes, in rank order, (1) Magnaporthe oryzae; (2) Botrytis cinerea; (3) Puccinia spp.; (4) Fusarium graminearum; (5) Fusarium oxysporum; (6) Blumeria graminis; (7) Mycosphaerella graminicola; (8) Colletotrichum spp.; (9) Ustilago maydis; (10) Melampsora lini, with honourable mentions for fungi just missing out on the Top 10, including Phakopsora pachyrhizi and Rhizoctonia solani. This article presents a short resume of each fungus in the Top 10 list and its importance, with the intent of initiating discussion and debate amongst the plant mycology community, as well as laying down a bench-mark. It will be interesting to see in future years how perceptions change and what fungi will comprise any future Top 10.

Accurate prediction of protein structures and interactions using a three-track neural network

Abstract: DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.