Panu Somervuo1, Sonja Koskela1, Juho Pennanen1, R Henrik Nilsson2, Otso Ovaskainen3. 1. Department of Biosciences, University of Helsinki, Helsinki, Finland. 2. Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden. 3. Department of Biosciences, University of Helsinki, Helsinki, Finland Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway.
Abstract
MOTIVATION: When targeted to a barcoding region, high-throughput sequencing can be used to identify species or operational taxonomical units from environmental samples, and thus to study the diversity and structure of species communities. Although there are many methods which provide confidence scores for assigning taxonomic affiliations, it is not straightforward to translate these values to unbiased probabilities. We present a probabilistic method for taxonomical classification (PROTAX) of DNA sequences. Given a pre-defined taxonomical tree structure that is partially populated by reference sequences, PROTAX decomposes the probability of one to the set of all possible outcomes. PROTAX accounts for species that are present in the taxonomy but that do not have reference sequences, the possibility of unknown taxonomical units, as well as mislabeled reference sequences. PROTAX is based on a statistical multinomial regression model, and it can utilize any kind of sequence similarity measures or the outputs of other classifiers as predictors. RESULTS: We demonstrate the performance of PROTAX by using as predictors the output from BLAST, the phylogenetic classification software TIPP, and the RDP classifier. We show that PROTAX improves the predictions of the baseline implementations of TIPP and RDP classifiers, and that it is able to combine complementary information provided by BLAST and TIPP, resulting in accurate and unbiased classifications even with very challenging cases such as 50% mislabeling of reference sequences. AVAILABILITY AND IMPLEMENTATION: Perl/R implementation of PROTAX is available at http://www.helsinki.fi/science/metapop/Software.htm CONTACT: panu.somervuo@helsinki.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: When targeted to a barcoding region, high-throughput sequencing can be used to identify species or operational taxonomical units from environmental samples, and thus to study the diversity and structure of species communities. Although there are many methods which provide confidence scores for assigning taxonomic affiliations, it is not straightforward to translate these values to unbiased probabilities. We present a probabilistic method for taxonomical classification (PROTAX) of DNA sequences. Given a pre-defined taxonomical tree structure that is partially populated by reference sequences, PROTAX decomposes the probability of one to the set of all possible outcomes. PROTAX accounts for species that are present in the taxonomy but that do not have reference sequences, the possibility of unknown taxonomical units, as well as mislabeled reference sequences. PROTAX is based on a statistical multinomial regression model, and it can utilize any kind of sequence similarity measures or the outputs of other classifiers as predictors. RESULTS: We demonstrate the performance of PROTAX by using as predictors the output from BLAST, the phylogenetic classification software TIPP, and the RDP classifier. We show that PROTAX improves the predictions of the baseline implementations of TIPP and RDP classifiers, and that it is able to combine complementary information provided by BLAST and TIPP, resulting in accurate and unbiased classifications even with very challenging cases such as 50% mislabeling of reference sequences. AVAILABILITY AND IMPLEMENTATION: Perl/R implementation of PROTAX is available at http://www.helsinki.fi/science/metapop/Software.htm CONTACT: panu.somervuo@helsinki.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Bilal Wajid; Faria Anwar; Imran Wajid; Haseeb Nisar; Sharoze Meraj; Ali Zafar; Mustafa Kamal Al-Shawaqfeh; Ali Riza Ekti; Asia Khatoon; Jan S Suchodolski Journal: Funct Integr Genomics Date: 2021-10-18 Impact factor: 3.410
Authors: Jan Axtner; Alex Crampton-Platt; Lisa A Hörig; Azlan Mohamed; Charles C Y Xu; Douglas W Yu; Andreas Wilting Journal: Gigascience Date: 2019-04-01 Impact factor: 6.524