The Experts below are selected from a list of 4539 Experts worldwide ranked by ideXlab platform
David L Hawksworth - One of the best experts on this subject based on the ideXlab platform.
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naming fungi involved in spoilage of food drink and water
Current opinion in food science, 2015Co-Authors: David L HawksworthAbstract:Correct identifications are the key to all data on an organism. Fungi cultured or sequenced from a foodstuff may not always be the spoilage agents, and there are potential pit-falls in culturing and molecular identification. Molecular phylogenetics is resulting in major changes in Fungal Classification, and substantial changes in the rules on naming fungi were agreed in 2011. Different morphs of a single pleomorphic species can no longer have different scientific names, and stability is to be fostered through lists of protected names. The naming of fungi is becoming increasingly fit-for-purpose by taking advantage of the possibilities arising from advances in molecular and digital technologies. A list of the current names of 100 species of spoilage fungi is included.
Kuanliang Liu - One of the best experts on this subject based on the ideXlab platform.
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accurate rapid taxonomic Classification of Fungal large subunit rrna genes
Applied and Environmental Microbiology, 2012Co-Authors: Kuanliang Liu, Andrea Porrasalfaro, Cheryl R Kuske, Stephanie A Eichorst, Gary XieAbstract:ABSTRACT Taxonomic and phylogenetic fingerprinting based on sequence analysis of gene fragments from the large-subunit rRNA (LSU) gene or the internal transcribed spacer (ITS) region is becoming an integral part of Fungal Classification. The lack of an accurate and robust Classification tool trained by a validated sequence database for taxonomic placement of Fungal LSU genes is a severe limitation in taxonomic analysis of Fungal isolates or large data sets obtained from environmental surveys. Using a hand-curated set of 8,506 Fungal LSU gene fragments, we determined the performance characteristics of a naive Bayesian classifier across multiple taxonomic levels and compared the classifier performance to that of a sequence similarity-based (BLASTN) approach. The naive Bayesian classifier was computationally more rapid (>460-fold with our system) than the BLASTN approach, and it provided equal or superior Classification accuracy. Classifier accuracies were compared using sequence fragments of 100 bp and 400 bp and two different PCR primer anchor points to mimic sequence read lengths commonly obtained using current high-throughput sequencing technologies. Accuracy was higher with 400-bp sequence reads than with 100-bp reads. It was also significantly affected by sequence location across the 1,400-bp test region. The highest accuracy was obtained across either the D1 or D2 variable region. The naive Bayesian classifier provides an effective and rapid means to classify Fungal LSU sequences from large environmental surveys. The training set and tool are publicly available through the Ribosomal Database Project (http://rdp.cme.msu.edu/classifier/classifier.jsp).
Gary Xie - One of the best experts on this subject based on the ideXlab platform.
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accurate rapid taxonomic Classification of Fungal large subunit rrna genes
Applied and Environmental Microbiology, 2012Co-Authors: Kuanliang Liu, Andrea Porrasalfaro, Cheryl R Kuske, Stephanie A Eichorst, Gary XieAbstract:ABSTRACT Taxonomic and phylogenetic fingerprinting based on sequence analysis of gene fragments from the large-subunit rRNA (LSU) gene or the internal transcribed spacer (ITS) region is becoming an integral part of Fungal Classification. The lack of an accurate and robust Classification tool trained by a validated sequence database for taxonomic placement of Fungal LSU genes is a severe limitation in taxonomic analysis of Fungal isolates or large data sets obtained from environmental surveys. Using a hand-curated set of 8,506 Fungal LSU gene fragments, we determined the performance characteristics of a naive Bayesian classifier across multiple taxonomic levels and compared the classifier performance to that of a sequence similarity-based (BLASTN) approach. The naive Bayesian classifier was computationally more rapid (>460-fold with our system) than the BLASTN approach, and it provided equal or superior Classification accuracy. Classifier accuracies were compared using sequence fragments of 100 bp and 400 bp and two different PCR primer anchor points to mimic sequence read lengths commonly obtained using current high-throughput sequencing technologies. Accuracy was higher with 400-bp sequence reads than with 100-bp reads. It was also significantly affected by sequence location across the 1,400-bp test region. The highest accuracy was obtained across either the D1 or D2 variable region. The naive Bayesian classifier provides an effective and rapid means to classify Fungal LSU sequences from large environmental surveys. The training set and tool are publicly available through the Ribosomal Database Project (http://rdp.cme.msu.edu/classifier/classifier.jsp).
Andrea Porrasalfaro - One of the best experts on this subject based on the ideXlab platform.
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accurate rapid taxonomic Classification of Fungal large subunit rrna genes
Applied and Environmental Microbiology, 2012Co-Authors: Kuanliang Liu, Andrea Porrasalfaro, Cheryl R Kuske, Stephanie A Eichorst, Gary XieAbstract:ABSTRACT Taxonomic and phylogenetic fingerprinting based on sequence analysis of gene fragments from the large-subunit rRNA (LSU) gene or the internal transcribed spacer (ITS) region is becoming an integral part of Fungal Classification. The lack of an accurate and robust Classification tool trained by a validated sequence database for taxonomic placement of Fungal LSU genes is a severe limitation in taxonomic analysis of Fungal isolates or large data sets obtained from environmental surveys. Using a hand-curated set of 8,506 Fungal LSU gene fragments, we determined the performance characteristics of a naive Bayesian classifier across multiple taxonomic levels and compared the classifier performance to that of a sequence similarity-based (BLASTN) approach. The naive Bayesian classifier was computationally more rapid (>460-fold with our system) than the BLASTN approach, and it provided equal or superior Classification accuracy. Classifier accuracies were compared using sequence fragments of 100 bp and 400 bp and two different PCR primer anchor points to mimic sequence read lengths commonly obtained using current high-throughput sequencing technologies. Accuracy was higher with 400-bp sequence reads than with 100-bp reads. It was also significantly affected by sequence location across the 1,400-bp test region. The highest accuracy was obtained across either the D1 or D2 variable region. The naive Bayesian classifier provides an effective and rapid means to classify Fungal LSU sequences from large environmental surveys. The training set and tool are publicly available through the Ribosomal Database Project (http://rdp.cme.msu.edu/classifier/classifier.jsp).
Marlis Reich - One of the best experts on this subject based on the ideXlab platform.
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a phylogenetic framework for the kingdom fungi based on 18s rrna gene sequences
Marine Genomics, 2017Co-Authors: Pablo Yarza, Pelin Yilmaz, Katrin Panzer, Frank Oliver Glockner, Marlis ReichAbstract:The usage of molecular phylogenetic approaches is critical to advance the understanding of systematics and community processes in the kingdom Fungi. Among the possible phylogenetic markers (or combinations of them), the 18S rRNA gene appears currently as the most prominent candidate due to its large availability in public databases and informative content. The purpose of this work was the creation of a reference phylogenetic framework that can serve as ready-to-use package for its application on Fungal Classification and community analysis. The current database contains 9329 representative 18S rRNA gene sequences covering the whole Fungal kingdom, a manually curated alignment, an annotated and revised phylogenetic tree with all the sequence entries, updated information on current taxonomy, and recommendations of use. Out of 201 total Fungal taxa with more than two sequences in the dataset, 179 were monophyletic. From another perspective, 66% of the entries had a tree-derived Classification identical to that obtained from the NCBI taxonomy, whereas 34% differed in one or the other rank. Most of the differences were associated to missing taxonomic assignments in NCBI taxonomy, or the unexpected position of sequences that positioned out of their theoretically corresponding clades. The strong correlation observed with current Fungal taxonomy evidences that 18S rRNA gene sequence-based phylogenies are adequate to reflect genealogy of Fungi at the levels of order and above, and justify their further usage and exploration.
