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A temporal banding approach for consistent taxonomic ranking above the species level.

ABSTRACT: Comparable taxonomic ranks within clades can facilitate more consistent classifications and objective comparisons among taxa. Here we use a temporal approach to identify taxonomic ranks. This is an extension of the temporal banding approach including a Temporal Error Score that finds an objective cut-off for each taxonomic rank using information for the current classification. We illustrate this method using a data set of the lichenized fungal family Parmeliaceae. To assess its performance, we simulated the effect of taxon sampling and compared our method with the other temporal banding method. For our sampled phylogeny, 11 of the 12 included families remained intact and 55 genera were confirmed, whereas 32 genera were lumped and 15 genera were split. Taxon sampling impacted the method at the genus level, whereas yielded only insignificant changes at the family level. The other available temporal approach also gives a similar cutoff point to our method. Our approach to identify taxonomic ranks enables taxonomists to revise and propose classifications on an objective basis, changing ranks of clades only when inconsistent with most taxa in a phylogenetic tree. An R script to find the time point with the minimal temporal error is provided.

SUBMITTER: Kraichak E

PROVIDER: S-EPMC5442095 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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A temporal banding approach for consistent taxonomic ranking above the species level.

Kraichak Ekaphan E Crespo Ana A Divakar Pradeep K PK Leavitt Steven D SD Lumbsch H Thorsten HT

Scientific reports 20170523 1

Comparable taxonomic ranks within clades can facilitate more consistent classifications and objective comparisons among taxa. Here we use a temporal approach to identify taxonomic ranks. This is an extension of the temporal banding approach including a Temporal Error Score that finds an objective cut-off for each taxonomic rank using information for the current classification. We illustrate this method using a data set of the lichenized fungal family Parmeliaceae. To assess its performance, we s ...[more]

PMID: 28536470

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Project description:BackgroundWhile many authors have discussed models and tools for studying protein evolution at the sequence level, molecular function is usually mediated by complex, higher order features such as independently folding domains and linear motifs that are based on or embedded in a particular arrangment of features such as secondary structure elements, transmembrane domains and regions with intrinsic disorder. This 'protein architecture' can, in its most simplistic representation, be visualized as domain organization cartoons that can be used to compare proteins in terms of the order of their mostly globular domains.MethodologyHere, we describe a visual approach and a webserver for protein comparison that extend the domain organization cartoon concept. By developing an information-rich, compact visualization of different protein features above the sequence level, potentially related proteins can be compared at the level of propensities for secondary structure, transmembrane domains and intrinsic disorder, in addition to PFAM domains. A public Web server is available at www.proteinarchitect.net, while the code is provided at protarchitect.sourceforge.net.Conclusions/significanceDue to recent advances in sequencing technologies we are now flooded with millions of predicted proteins that await comparative analysis. In many cases, mature tools focused on revealing hits with considerable global or local similarity to well-characterized proteins will not be able to lead us to testable hypotheses about a protein's function, or the function of a particular region. The visual comparison of different types of protein features with ProteinArchitect will be useful when assessing the relevance of similarity search hits, to discover subgroups in protein families and superfamilies, and to understand protein regions with conserved features outside globular regions. Therefore, this approach is likely to help researchers to develop testable hypotheses about a protein's function even if is somewhat distant from the more characterized proteins, by facilitating the discovery of features that are conserved above the sequence level for comparison and further experimental investigation.

Project description:BackgroundThere are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Eggs are the parasitic structures most viewed in coprological analysis in both modern and ancient samples; consequently, their presence is indicative of positive diagnosis for infection. The structure of the egg could play a role in genera or species discrimination. Institutional biological collections are taxonomic repositories of specimens described and strictly identified by systematics specialists.MethodsThe present work aims to characterize eggs of capillariid species deposited in institutional helminth collections and to process the morphological, morphometric and ecological data using machine learning (ML) as a new approach for taxonomic identification. Specimens of 28 species and 8 genera deposited at Coleção Helmintológica do Instituto Oswaldo Cruz (CHIOC, IOC/FIOCRUZ/Brazil) and Collection de Nématodes Zooparasites du Muséum National d'Histoire Naturelle de Paris (MNHN/France) were examined under light microscopy. In the morphological and morphometric analyses (MM), the total length and width of eggs as well as plugs and shell thickness were considered. In addition, eggshell ornamentations and ecological parameters of the geographical location (GL) and host (H) were included.ResultsThe performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores.ConclusionsThe present research proposed a novel procedure for taxonomic species identification, integrating data from centenary biological collections and the logic of artificial intelligence techniques. This study will support future research on taxonomic identification and diagnosis of both modern and archaeological capillariids.

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A temporal banding approach for consistent taxonomic ranking above the species level.

Publications

A temporal banding approach for consistent taxonomic ranking above the species level.

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