WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data
Ontology highlight
ABSTRACT:
SUBMITTER: Kindt R
PROVIDER: S-EPMC7526431 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Misspellings of organism scientific names create barriers to optimal storage and organization of biological data, reconciliation of data stored under different spelling variants of the same name, and appropriate responses from user queries to taxonomic data systems. This study presents an analysis of the nature of the problem from first principles, reviews some available algorithmic approaches, and describes Taxamatch, an improved name matching solution for this information domain. Taxamatch employs a custom Modified Damerau-Levenshtein Distance algorithm in tandem with a phonetic algorithm, together with a rule-based approach incorporating a suite of heuristic filters, to produce improved levels of recall, precision and execution time over the existing dynamic programming algorithms n-grams (as bigrams and trigrams) and standard edit distance. Although entirely phonetic methods are faster than Taxamatch, they are inferior in the area of recall since many real-world errors are non-phonetic in nature. Excellent performance of Taxamatch (as recall, precision and execution time) is demonstrated against a reference database of over 465,000 genus names and 1.6 million species names, as well as against a range of error types as present at both genus and species levels in three sets of sample data for species and four for genera alone. An ancillary authority matching component is included which can be used both for misspelled names and for otherwise matching names where the associated cited authorities are not identical.
| S-EPMC4172526 | biostudies-literature
Project description:How did the forced migration of nearly 11 million enslaved Africans to the Americas influence their knowledge of plants? Vernacular plant names give insight into the process of species recognition, acquisition of new knowledge, and replacement of African species with American ones. This study traces the origin of 2,350 Afro-Surinamese (Sranantongo and Maroon) plant names to those plant names used by local Amerindians, Europeans, and related groups in West and Central Africa. We compared vernacular names from herbarium collections, literature, and recent ethnobotanical fieldwork in Suriname, Ghana, Benin, and Gabon. A strong correspondence in sound, structure, and meaning among Afro-Surinamese vernaculars and their equivalents in other languages for botanically related taxa was considered as evidence for a shared origin. Although 65% of the Afro-Surinamese plant names contained European lexical items, enslaved Africans have recognized a substantial part of the neotropical flora. Twenty percent of the Sranantongo and 43% of the Maroon plant names strongly resemble names currently used in diverse African languages for related taxa, represent translations of African ones, or directly refer to an Old World origin. The acquisition of new ethnobotanical knowledge is captured in vernaculars derived from Amerindian languages and the invention of new names for neotropical plants from African lexical terms. Plant names that combine African, Amerindian, and European words reflect a creolization process that merged ethnobotanical skills from diverse geographical and cultural sources into new Afro-American knowledge systems. Our study confirms the role of Africans as significant agents of environmental knowledge in the New World.
| S-EPMC4273368 | biostudies-literature
Project description:BackgroundThe digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this 'names problem' has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science.ResultsThe TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets.ConclusionsWe show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/.
| S-EPMC3554605 | biostudies-literature
Project description:Metagenomics is a powerful tool for understanding organismal interactions; however, classification, profiling and detection of interactions at the strain level remain challenging. We present an automated pipeline, quantitative metagenomic alignment and taxonomic exact matching (Qmatey), that performs a fast exact matching-based alignment and integration of taxonomic binning and profiling. It interrogates large databases without using metagenome-assembled genomes, curated pan-genes or k-mer spectra that limit resolution. Qmatey minimizes misclassification and maintains strain level resolution by using only diagnostic reads as shown in the analysis of amplicon, quantitative reduced representation and shotgun sequencing datasets. Using Qmatey to analyze shotgun data from a synthetic community with 35% of the 26 strains at low abundance (0.01-0.06%), we revealed a remarkable 85-96% strain recall and 92-100% species recall while maintaining 100% precision. Benchmarking revealed that the highly ranked Kraken2 and KrakenUniq tools identified 2-4 more taxa (92-100% recall) than Qmatey but produced 315-1752 false positive taxa and high penalty on precision (1-8%). The speed, accuracy and precision of the Qmatey pipeline positions it as a valuable tool for broad-spectrum profiling and for uncovering biologically relevant interactions.
| S-EPMC10569747 | biostudies-literature
Project description:Unraveling functional and ancestral relationships between proteins as well as structure-prediction procedures require powerful protein-alignment methods. A structure-alignment method is presented where the problem is mapped onto a cost function containing both fuzzy (Potts) assignment variables and atomic coordinates. The cost function is minimized by using an iterative scheme, where at each step mean field theory methods at finite "temperatures" are used for determining fuzzy assignment variables followed by exact translation and rotation of atomic coordinates weighted by their corresponding fuzzy assignment variables. The approach performs very well when compared with other methods, requires modest central processing unit consumption, and is robust with respect to choice of iteration parameters for a wide range of proteins.
| S-EPMC218691 | biostudies-literature
Project description:The List of Available Names in Zoology (LAN) is an inventory of names with specific scope in time and content, presented and approved in parts, and constituted as a cumulative index of names available for use in zoological nomenclature. It was defined in Article 79 in the fourth edition of the International Code of Zoological Nomenclature. The LAN is likely to gain importance with the development of the online Official Registry for Zoological Nomenclature (ZooBank) as it is potentially a source of many nomenclaturally certified names. Article 79 describes the deliberative process for adding large numbers of names to the LAN simultaneously, detailing steps and chronology for submission of a candidate Part to the LAN and consideration of a candidate Part by the public and Commission, but it is largely mute about the contents of a candidate Part. It does make clear that a name within the scope of a Part but not on the LAN has no nomenclatural standing, even if it had previously been considered available, thereby preventing long-forgotten names from displacing accepted ones and the accumulation of nomina dubia. Thus, for taxa on the LAN, nomenclatural archaeology - the resurrecting of old unused names to replace by priority names in current usage - will not be worthwhile. Beyond that, it has been unclear if Article 79 is intended to document every available name known within the scope of the Part, or if its intention is to pare the inventory of available names within the scope of the Part. Consideration by the Commission and two committees to deal with the LAN have defined steps to implement Article 79 with the latter intent. Procedures for consideration of a candidate Part are defined in a manual, published as an appendix in this volume.
| S-EPMC4741223 | biostudies-other
Project description:Bipartite projections are used in a wide range of network contexts including politics (bill co-sponsorship), genetics (gene co-expression), economics (executive board co-membership), and innovation (patent co-authorship). However, because bipartite projections are always weighted graphs, which are inherently challenging to analyze and visualize, it is often useful to examine the 'backbone,' an unweighted subgraph containing only the most significant edges. In this paper, we introduce the R package backbone for extracting the backbone of weighted bipartite projections, and use bill sponsorship data from the 114th session of the United States Senate to demonstrate its functionality.
| S-EPMC7787471 | biostudies-literature
Project description:BACKGROUND:Reliable taxonomy underpins communication in all of biology, not least nature conservation and sustainable use of ecosystem resources. The flexibility of taxonomic interpretations, however, presents a serious challenge for end-users of taxonomic concepts. Users need standardised and continuously harmonised taxonomic reference systems, as well as high-quality and complete taxonomic data sets, but these are generally lacking for non-specialists. The solution is in dynamic, expertly curated web-based taxonomic tools. The Pan-European Species-directories Infrastructure (PESI) worked to solve this key issue by providing a taxonomic e-infrastructure for Europe. It strengthened the relevant social (expertise) and information (standards, data and technical) capacities of five major community networks on taxonomic indexing in Europe, which is essential for proper biodiversity assessment and monitoring activities. The key objectives of PESI were: 1) standardisation in taxonomic reference systems, 2) enhancement of the quality and completeness of taxonomic data sets and 3) creation of integrated access to taxonomic information. NEW INFORMATION:This paper describes the results of PESI and its future prospects, including the involvement in major European biodiversity informatics initiatives and programs.
| S-EPMC4609752 | biostudies-literature
Project description:The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging task with many potential pitfalls. Algorithm efficiency can be measured based on the usage of different resources. In software engineering, algorithmic productivity is a property of an algorithm execution identified with the computational resources the algorithm consumes. Resource usage in algorithm execution could be determined, and for maximum efficiency, the goal is to minimize resource usage. Guided by the fact that standard measures of algorithm efficiency, such as execution time, directly depend on the number of executed actions. Without touching the problematics of computer power consumption or memory, which also depends on the algorithm type and the techniques used in algorithm development, we have developed a methodology which enables the researchers to choose an efficient algorithm for a specific domain. String searching algorithms efficiency is usually observed independently from the domain texts being searched. This research paper aims to present the idea that algorithm efficiency depends on the properties of searched string and properties of the texts being searched, accompanied by the theoretical analysis of the proposed approach. In the proposed methodology, algorithm efficiency is expressed through character comparison count metrics. The character comparison count metrics is a formal quantitative measure independent of algorithm implementation subtleties and computer platform differences. The model is developed for a particular problem domain by using appropriate domain data (patterns and texts) and provides for a specific domain the ranking of algorithms according to the patterns' entropy. The proposed approach is limited to on-line exact string-matching problems based on information entropy for a search pattern. Meticulous empirical testing depicts the methodology implementation and purports soundness of the methodology.
| S-EPMC7824336 | biostudies-literature
Project description:This article presents efficient quantum solutions for exact multiple pattern matching to process the biological sequences. The classical solution takes Ο(mN) time for matching m patterns over N sized text database. The quantum search mechanism is a core for pattern matching, as this reduces time complexity and achieves computational speedup. Few quantum methods are available for multiple pattern matching, which executes search oracle for each pattern in successive iterations. Such solutions are likely acceptable because of classical equivalent quantum designs. However, these methods are constrained with the inclusion of multiplicative factor m in their complexities. An optimal quantum design is to execute multiple search oracle in parallel on the quantum processing unit with a single-core that completely removes the multiplicative factor m, however, this method is impractical to design. We have no effective quantum solutions to process multiple patterns at present. Therefore, we propose quantum algorithms using quantum processing unit with C quantum cores working on shared quantum memory. This quantum parallel design would be effective for searching all t exact occurrences of each pattern. To our knowledge, no attempts have been made to design multiple pattern matching algorithms on quantum multicore processor. Thus, some quantum remarkable exact single pattern matching algorithms are enhanced here with their equivalent versions, namely enhanced quantum memory processing based exact algorithm and enhanced quantum-based combined exact algorithm for multiple pattern matching. Our quantum solutions find all t exact occurrences of each pattern inside the biological sequence in O((m/C)N) and O((m/C)t) time complexities. This article shows the hybrid simulation of quantum algorithms to validate quantum solutions. Our theoretical-experimental results justify the significant improvements that these algorithms outperform over the existing classical solutions and are proven effective in quantum counterparts.
| S-EPMC9138144 | biostudies-literature