The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Ruchi Tomar - One of the best experts on this subject based on the ideXlab platform.
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funbarRF: DNA barcode-based Fungal species prediction using multiclass Random Forest supervised learning model
BMC Genetics, 2019Co-Authors: Prabina Kumar Meher, Tanmaya Kumar Sahu, Shachi Gahoi, Ruchi TomarAbstract:Background Identification of unknown Fungal species aids to the conservation of Fungal diversity. As many Fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For Fungal Taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of Fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of Fungal species is challenging due to high degree of variability among ITS regions within species. Results A Random Forest (RF)-based predictor was built for identification of unknown Fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of Fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi. Conclusions An online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for Fungal species identification. Besides, an R-package funbarRF ( https://cran.r-project.org/web/packages/funbarRF/ ) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of Fungal Taxonomy assignments based on DNA barcode.
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funbarRF: DNA barcode-based Fungal species prediction using multiclass Random Forest supervised learning model
BMC Genetics, 2019Co-Authors: Prabina Kumar Meher, Tanmaya Kumar Sahu, Shachi Gahoi, Ruchi TomarAbstract:Identification of unknown Fungal species aids to the conservation of Fungal diversity. As many Fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For Fungal Taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of Fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of Fungal species is challenging due to high degree of variability among ITS regions within species. A Random Forest (RF)-based predictor was built for identification of unknown Fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of Fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi. An online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for Fungal species identification. Besides, an R-package funbarRF ( https://cran.r-project.org/web/packages/funbarRF/ ) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of Fungal Taxonomy assignments based on DNA barcode.
Prabina Kumar Meher - One of the best experts on this subject based on the ideXlab platform.
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funbarRF: DNA barcode-based Fungal species prediction using multiclass Random Forest supervised learning model
BMC Genetics, 2019Co-Authors: Prabina Kumar Meher, Tanmaya Kumar Sahu, Shachi Gahoi, Ruchi TomarAbstract:Background Identification of unknown Fungal species aids to the conservation of Fungal diversity. As many Fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For Fungal Taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of Fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of Fungal species is challenging due to high degree of variability among ITS regions within species. Results A Random Forest (RF)-based predictor was built for identification of unknown Fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of Fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi. Conclusions An online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for Fungal species identification. Besides, an R-package funbarRF ( https://cran.r-project.org/web/packages/funbarRF/ ) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of Fungal Taxonomy assignments based on DNA barcode.
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funbarRF: DNA barcode-based Fungal species prediction using multiclass Random Forest supervised learning model
BMC Genetics, 2019Co-Authors: Prabina Kumar Meher, Tanmaya Kumar Sahu, Shachi Gahoi, Ruchi TomarAbstract:Identification of unknown Fungal species aids to the conservation of Fungal diversity. As many Fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For Fungal Taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of Fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of Fungal species is challenging due to high degree of variability among ITS regions within species. A Random Forest (RF)-based predictor was built for identification of unknown Fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of Fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi. An online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for Fungal species identification. Besides, an R-package funbarRF ( https://cran.r-project.org/web/packages/funbarRF/ ) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of Fungal Taxonomy assignments based on DNA barcode.
Henrik R Nilsson - One of the best experts on this subject based on the ideXlab platform.
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progress in molecular and morphological taxon discovery in fungi and options for formal classification of environmental sequences
Fungal Biology Reviews, 2011Co-Authors: David S. Hibbett, Paul M. Kirk, Henrik R Nilsson, Anders W Ohman, Dylan Glotzer, Mitchell NuhnAbstract:Abstract Fungal Taxonomy seeks to discover, describe, and classify all species of Fungi and provide tools for their identification. About 100,000 Fungal species have been described so far, but it has been estimated that there may be from 1.5 to 5.1 million extant Fungal species. Over the last decade, about 1200 new species of Fungi have been described in each year. At that rate, it may take up to 4000 y to describe all species of Fungi using current specimen-based approaches. At the same time, the number of molecular operational taxonomic units (MOTUs) discovered in ecological surveys has been increasing dramatically. We analyzed ribosomal RNA internal transcribed spacer (ITS) sequences in the GenBank nucleotide database and classified them as “environmental” or “specimen-based”. We obtained 91,225 sequences, of which 30,217 (33 %) were of environmental origin. Clustering at an average 93 % identity in extracted ITS1 and ITS2 sequences yielded 16,969 clusters, including 6230 (37 %) clusters with only environmental sequences, and 2223 (13 %) clusters with both environmental and specimen-based sequences. In 2008 and 2009, the number of purely environmental clusters deposited in GenBank exceeded the number of species described based on specimens, and this does not include the huge number of unnamed MOTUs discovered in pyrosequencing studies. To enable communication about Fungal diversity, there is a pressing need to develop classification systems based on environmental sequences. Assigning Latin binomials to MOTUs would promote their integration with specimen-based taxonomic databases, whereas the use of numerical codes for MOTUs would perpetuate a disconnect with the taxonomic literature. MOTUs could be formally named under the existing International Code of Botanical Nomenclature if the concept of a nomenclatural type was expanded to include environmental samples or illustrations of sequence chromatograms (or alignments). Alternatively, a “candidate species” category could be created for Fungi , based on the candidatus taxon status employed by microbiologists.
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Fungal Taxonomy and systematics in the digital era with an outlook on the cantharelloid clade basidiomycota
2007Co-Authors: Henrik R NilssonAbstract:Fungi form a large and ubiquitous group of organisms where species identification and delimitation on morphological grounds often fall short. DNA sequences have proved an invaluable information source for these pursuits and are now routinely used in most mycological laboratories. Newl generated DNA sequences are typically compared with the entries of the large INSD sequence database for inference of taxonomic affiliations and other properties using the sequence similarity search tool BLAST. This thesis highlights some practical difficulties in using BLAST for these purposes it is for example very sensitive to the length of the sequences - and shows that improper use of BLAST appears to have had considerable repercussion on the general level of taxonomic reliability of the Fungal sequences in INSD, more than 10% of which may be incorrectly identified to species level. An initiative to build a new DNA sequence database for taxonomically reliable DNA-based identification of mycorrhizal (plant-mutualistic) fungi is described. The database differs from similar initiatives in that its entries are determined to species level by pertinent experts; it allows for integrative sequence annotations, including photos and morphological descriptions; and it employs new, phylogeny-based tools for sequence identification to alleviate the concerns with simplistic, similarity-based tools as arbiters of taxonomic affiliation. That phylogenetic analyses can be beneficial also to classification and nomenclatural projects is shown in the mor enterprise, which is a weekly automaton of Agaricomycetes (mushroom-forming fungi) phylogeny. All Fungal sequences from the nuclear ribosomal large subunit gene are assembled on a weekly basis; automated phylogenetic analyses are undertaken; and the resulting phylogenetic trees are displayed and analyzed for changes in clade topology and inclusiveness. The enigmatic cantharelloid clade of the Agaricomycetes is studied using a four-gene phylogenetic approach. While this heterogeneous assembly of mushroom-like, resupinate, clavarioid, and lichen-forming fungi defies any morphological attempt at indicating a close relatedness for its species, the results from the molecular analyses show that there is indeed strong evidence to support that these fungi form a monophyletic group; a restrictive circumscription of the clade to include the genera Botryobasidium, Sistotrema, Multiclavula, Membranomyces, Hydnum, Clavulina, Cantharellus, and Craterellus is advocated. Stichic basidia, and to a lesser extent parenthesome ultrastructure, are found to be characteristic of the clade, and the previously reported divergent rates of evolution for the genera Cantharellus and Craterellus are shown to be limited to the nuclear ribosomal genes. The largely resupinate, and purportedly wood-decaying, genus Sistotrema is demonstrated to hold mycorrhizal lineages, and the molecular evidence to consider the genus polyphyletic is found to be very convincing.
Tanmaya Kumar Sahu - One of the best experts on this subject based on the ideXlab platform.
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funbarRF: DNA barcode-based Fungal species prediction using multiclass Random Forest supervised learning model
BMC Genetics, 2019Co-Authors: Prabina Kumar Meher, Tanmaya Kumar Sahu, Shachi Gahoi, Ruchi TomarAbstract:Background Identification of unknown Fungal species aids to the conservation of Fungal diversity. As many Fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For Fungal Taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of Fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of Fungal species is challenging due to high degree of variability among ITS regions within species. Results A Random Forest (RF)-based predictor was built for identification of unknown Fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of Fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi. Conclusions An online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for Fungal species identification. Besides, an R-package funbarRF ( https://cran.r-project.org/web/packages/funbarRF/ ) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of Fungal Taxonomy assignments based on DNA barcode.
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funbarRF: DNA barcode-based Fungal species prediction using multiclass Random Forest supervised learning model
BMC Genetics, 2019Co-Authors: Prabina Kumar Meher, Tanmaya Kumar Sahu, Shachi Gahoi, Ruchi TomarAbstract:Identification of unknown Fungal species aids to the conservation of Fungal diversity. As many Fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For Fungal Taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of Fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of Fungal species is challenging due to high degree of variability among ITS regions within species. A Random Forest (RF)-based predictor was built for identification of unknown Fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of Fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi. An online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for Fungal species identification. Besides, an R-package funbarRF ( https://cran.r-project.org/web/packages/funbarRF/ ) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of Fungal Taxonomy assignments based on DNA barcode.
Shachi Gahoi - One of the best experts on this subject based on the ideXlab platform.
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funbarRF: DNA barcode-based Fungal species prediction using multiclass Random Forest supervised learning model
BMC Genetics, 2019Co-Authors: Prabina Kumar Meher, Tanmaya Kumar Sahu, Shachi Gahoi, Ruchi TomarAbstract:Background Identification of unknown Fungal species aids to the conservation of Fungal diversity. As many Fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For Fungal Taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of Fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of Fungal species is challenging due to high degree of variability among ITS regions within species. Results A Random Forest (RF)-based predictor was built for identification of unknown Fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of Fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi. Conclusions An online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for Fungal species identification. Besides, an R-package funbarRF ( https://cran.r-project.org/web/packages/funbarRF/ ) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of Fungal Taxonomy assignments based on DNA barcode.
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funbarRF: DNA barcode-based Fungal species prediction using multiclass Random Forest supervised learning model
BMC Genetics, 2019Co-Authors: Prabina Kumar Meher, Tanmaya Kumar Sahu, Shachi Gahoi, Ruchi TomarAbstract:Identification of unknown Fungal species aids to the conservation of Fungal diversity. As many Fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For Fungal Taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of Fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of Fungal species is challenging due to high degree of variability among ITS regions within species. A Random Forest (RF)-based predictor was built for identification of unknown Fungal species. The reference and query sequences were mapped onto numeric features based on gapped base pair compositions, and then used as training and test sets respectively for prediction of Fungal species using RF. More than 85% accuracy was found when 4 sequences per species in the reference set were utilized; whereas it was seen to be stabilized at ~88% if ≥7 sequence per species in the reference set were used for training of the model. The proposed model achieved comparable accuracy, while evaluated against existing methods through cross-validation procedure. The proposed model also outperformed several existing models used for identification of different species other than fungi. An online prediction server “funbarRF” is established at http://cabgrid.res.in:8080/funbarrf/ for Fungal species identification. Besides, an R-package funbarRF ( https://cran.r-project.org/web/packages/funbarRF/ ) is also available for prediction using high throughput sequence data. The effort put in this work will certainly supplement the future endeavors in the direction of Fungal Taxonomy assignments based on DNA barcode.
