1 Zololkree

Systematic Biology Latex Template Assignment


We provide a new automated statistical method for DNA barcoding based on a Bayesian phylogenetic analysis. The method is based on automated database sequence retrieval, alignment, and phylogenetic analysis using a custom-built program for Bayesian phylogenetic analysis. We show on real data that the method outperforms Blast searches as a measure of confidence and can help eliminate 80% of all false assignment based on best Blast hit. However, the most important advance of the method is that it provides statistically meaningful measures of confidence. We apply the method to a re-analysis of previously published ancient DNA data and show that, with high statistical confidence, most of the published sequences are in fact of Neanderthal origin. However, there are several cases of chimeric sequences that are comprised of a combination of both Neanderthal and modern human DNA.

Assignment, barcoding, Bayesian, phylogenetics

The identification of organic material through comparisons of DNA sequences from a sample to DNA sequences from a database is an important research tool in a number of scientific disciplines. In the zoological and ecological literature, identification of unknown specimens based on cytochrome oxidase I (COI) has become know as DNA barcoding (Floyd et al. ; Hebert et al. ; Remigio and Hebert ; Moritz and Cicero ). DNA barcoding has found a wide range of applications, from identification of specimens in conservation biology and molecular ecology to identification of birds that have collided with aircraft. A similar methodology is applied in metagenomics (Tringe and Rubin ,Venter et al. ; Rusch et al. ; Yooseph et al. ) where genomic sequences from environmental samples are obtained and compared to database sequences.

The topics of this article are the methodological issues relating to the assignment of DNA sequences to taxa represented in a sequence database. The classical procedure for such identification has been the use of Blast searches (Altschul et al. ). There are, however, at least three statistical problems associated with this: (1) Blast searches provide a score based on local alignments and not global alignments, leading to a loss of information; (2) Blast searches ignore the population genetic and phylogenetic issues associated with species identification; and (3) the measures of confidence associated with Blast searches represent significance of local sequence similarity and not significance of taxonomic assignment. Blast thus offers no information to help researchers choose among multiple close matches. Whereas the local alignment problem can be circumvented using global alignments, the remaining two problems cannot be addressed without a statistical evaluation of the phylogenetic associations among species.

Several new methods have been developed that attempt to address the problems associated with the use of Blast to identify sequences (Matz and Nielsen Steinke et al. ; Nielsen and Matz ; Abdo and Golding ); most of these methods focus on identifying species affiliation. This question is difficult to address as the evolutionary relationship among genetic markers may not truly reflect the evolutionary relationship among species. In cases where reciprocal monophyly cannot safely be assumed, an analysis quantifying within- and between-species genetic variation forms a more correct basis of assignment. Such analyses, however, require a comprehensive database coverage that is generally not available to the biologist. In this article we describe a purely phylogenetic solution to the DNA barcoding problem. We will not address the species problem but instead attempt to devise an automated method for the assignment of sample sequences to taxa based on the position of the sample sequence in the phylogeny of life. This method leads to improved accuracy and, importantly, it provides a measure of statistical confidence associated with the barcoding assignment.


Sequences can be assigned to taxa using a number of different statistical frameworks. Here we pursue a Bayesian approach that allows us to estimate the probability that the sample sequence is part of a monophyletic group, identified with database sequences. We will thus not address the population genetic questions latent in species assignment but reduce the question to a purely taxonomic, or cladistic, question of assigning the sample sequence to a particular clade in an established phylogeny. The procedure is summarized graphically in Figure 1 and described in detail below.

Figure 1

Flowchart of the assignment procedure. A set of homologues is compiled using information from Blast searches and annotation from NCBI's Taxonomy database. The relevant sequences are retrieved from GenBank and aligned using ClustalW. Based on the resulting multiple alignment a large number of phylogenetic trees are sampled and these are then used to calculate posterior probabilities of assignment.

Figure 1

Flowchart of the assignment procedure. A set of homologues is compiled using information from Blast searches and annotation from NCBI's Taxonomy database. The relevant sequences are retrieved from GenBank and aligned using ClustalW. Based on the resulting multiple alignment a large number of phylogenetic trees are sampled and these are then used to calculate posterior probabilities of assignment.

In the Bayesian framework (e.g., Pawitan ), the relevant probability of interest is the posterior probability that the query species belong to a particular taxonomic group: where X is the sample-sequence, Ti is taxon i, and D is the set of database sequences representing k disjoint groups. Because the denominator contains a sum over sequences represented in a database, the probability calculated using this approach is the probability of assignment to a taxonomic group given that the sequence has to be assigned to one of the groups represented in the database.

The posterior probability involves a summation over all possible phylogenetic trees and, for each tree, a multiple integral over all combinations of substitution parameters. Hence, the posterior probability cannot be evaluated analytically. However, Markov chain Monte Carlo (MCMC; e.g., Huelsenbeck and Ronquist, ) can be used to sample trees in proportion to their posterior probabilities. The fraction of the time the MCMC sampler visits trees that place the sample sequence within a specific monophyletic group (XTi) is a valid approximation of the posterior probability that the query sequence falls within that group.

Ideally, each sample sequence should be compared to the entire tree of life or as much of it as is represented in the available sequence database. For obvious reasons this is not possible, and a heuristic is required to extract a limited representation of the database. To this end we use sequence homology between the sample sequence and sequences obtained using remote Blast searches against GenBank. A taxonomic annotation for each homologue is retrieved from NCBI's taxonomy browser. Homologues with insufficient taxonomic annotation are disregarded.

The vast majority of taxa represented in the sequence database are not relevant to the analysis because the posterior probabilities of grouping monophyletically with these taxa are not appreciably large. The bulk of sequence homologues representing these taxa can be avoided by including only homologues with a Blast score of at least half that of the best matching homologue.

More often than not, however, this relative similarity cutoff does not reduce the number of sequence homologues to a set that can be handled computationally. To obtain the best possible taxonomic coverage in a limited set, only the best-matching sequence homologue for each species is included. If available, up to 30 different species homologues are included. If, at this point, the relative cutoff described above has not been reached, up to 20 homologues providing further taxonomic diversity are added progressively including up to 10 genera, six families, five orders, three classes, and two phyla in the set. If the relative cutoff is reached before 50 homologues have been included in the set, additional sequences are added for the species already represented in the set by including homologues previously rejected as suboptimal representatives for the species.

The analysis is discontinued if the compiled set does not include at least five Blast hits with an E-value below An alignment of the sample sequence and the set of homologues is produced using ClustalW in slow/accurate mode with default parameters.

Like any other comparable method, our approach can only assign sequences to taxonomic groups represented in the database. Hence, if only a single taxon represents the clade in which the sample sequence belongs, the sample sequence will be assigned to this taxon with probability one. We have in our approach made no attempt to model the structure and sampling representation of the databases to evaluate the probability that the sequence truly belongs to some other taxon not represented in the database.

A computer program, written in C++ by J.P.H., performs the MCMC analysis. This program takes as input the sequence alignment and a file describing any constraints on the topology of the tree. The constraints are of the form of a backbone constraint. In other words, the constraint tree may include only a subset of the sequences included in the alignment. Here, all sequences except the sample sequence are included in a constraint tree specified by the taxonomic annotation. The program assumes that nucleotide substitutions occur according to the general time reversible model () and assumes that the rate of substitution at a site is a random variable drawn from a mean-one gamma distribution (Yang ; Yang ). The Markov chain explores the space of all of the parameters of the model, including the substitution rates, nucleotide frequencies, gamma-shape parameter, and topology/branch lengths of the tree subject to the specified constraints. The proposal mechanisms for all of the non-tree parameters have been described elsewhere (e.g., Huelsenbeck et al. ). We propose new topologies using a stochastic variant of the SPR (subtree pruning and regrafting) tree perturbation often used to find optimal trees in a parsimony or maximum likelihood framework. Ten thousand unrooted trees sampled from the MCMC analysis are analyzed to obtain posterior probabilities of assignment to all taxa represented in the compiled set of homologues.

The retrieved taxonomic annotation is mapped onto each sampled tree by associating each clade in the tree with the taxon with lowest taxonomic rank that includes all sequences in the clade (see Fig. 2). By assuming the rooting implicit from the taxonomic annotation the sister clade to the sample sequence is identified. For some trees the position of the root relative to the sample sequence cannot be deduced from the taxonomic annotation. In these cases the taxonomic assignment of all sequences in the tree is recorded. The posterior probability of forming a monophyletic group with a given taxon is then calculated as the fraction of sampled trees where the sister clade to the sample sequence is a member of that taxon.

Figure 2

Assignment of the sample sequence in each sampled tree is done by assuming the root implied by the taxonomic annotation of homologues and then recording the consensus taxonomy for all members of the sister clade from the highest taxonomic level to the most specific level shared by all clade members.

Figure 2

Assignment of the sample sequence in each sampled tree is done by assuming the root implied by the taxonomic annotation of homologues and then recording the consensus taxonomy for all members of the sister clade from the highest taxonomic level to the most specific level shared by all clade members.

The posterior probability serves as a confidence measure associated with each assignment and has a straightforward statistical interpretation as the posterior probability that the assignment is correct given the available sequence information and a uniform prior on tree topology. Posterior probabilities are produced for all levels of taxonomic annotation. This allows the sample sequence to be assigned to a higher ranking taxon, such as genus or family, in cases where homology information is too ambiguous to allow a reliable assignment at the species level. The implementation of our approach, SAP (Statistical Assignment Package), generates scalable vector graphics summarizing assignment results. An example of this is shown in Figure 3.

Figure 3

Graphic representation of assignment. The taxonomic tree shows all taxa obtaining positive probabilities of assignment. For clarity, assignment probabilities below 50% are shaded. In the example shown, sequence evidence is substantial but too ambiguous to allow a reliable assignment at the species and genus level. The evidence at family level, however, is decisive.

Figure 3

Graphic representation of assignment. The taxonomic tree shows all taxa obtaining positive probabilities of assignment. For clarity, assignment probabilities below 50% are shaded. In the example shown, sequence evidence is substantial but too ambiguous to allow a reliable assignment at the species and genus level. The evidence at family level, however, is decisive.

The computational time to compile a homologue set relies heavily on a number of external factors such as the current response time of the online Blast server and bandwidth of the Internet connection for retrieval of sequences and annotation. On a 2-GHz Intel processor, the alignment of fifty bp sequences in ClustalW takes about 2 minutes. The sampling of trees amounts to about an hour and represents the bulk of the computational time for the full analysis. The post-processing of the MCMC output may take up to 10 minutes.

The software can be accessed at woaknb.wz.sk



A benchmark analysis was carried out by assigning a data set of cytochrome oxidase I (COI) and tRNA-Leu (trnL) sequences to taxa. All COI entries for the class Insecta (true insects), and all trnL entries for the class Liliopsida (monocots) are downloaded from GenBank. Taxa represented by only one sequence in GenBank as well as database entries not explicitly targeting the relevant genes are not retrieved. The correct taxonomic annotation associated with each entry was downloaded from NCBI's Taxonomy database. From the 10, Insecta and Liliopsida sequences, are randomly chosen from each set to serve as test sample sequences. Taxonomic assignment of each sample sequence was performed as described, with the exception that the sample sequence itself was disregarded when identified as a homologue in GenBank.

The distribution of posterior probabilities associated with correct and wrong assignments are shown in Figure 4. At the levels of species, genus, and family, 90%, 99%, and 99% of assignments of Insecta sequences are correct and 51%, 90%, and % of assignments of Liliopsida sequences are correct. The false assignments generally have low probabilities and 86% of correct assignments of Insecta sequences and 60% of correct Liliopsida assignments have posterior probabilities above The few false assignments primarily arise when lineage sorting disrupts the true phylogenetic relationship between taxa. False assignments may also arise when the correct taxon and one or more wrong taxa all obtain equally high assignment probabilities. In these cases, the small error in the estimation of assignment probabilities may cause that of a wrong taxon to be marginally greater than that of the correct one, resulting in an incorrect assignment. This problem, however, only affects assignments with probabilities below A global alignment may not always constitute an optimal alignment of all homologues to the sample sequence so that the relative distances to the sample sequence are all represented correctly. However, only the part of each homologue corresponding to the sample sequence is submitted to the multiple alignment leaving little room for incorrect alignment. In addition, the clustering algorithm used by ClustalW assures that faulty alignment is least likely to occur between the most similar sequences in the multiple alignment. This minor source of error is therefore expected to mainly affect assignment in cases where the homology evidence is ambiguous and will thus rarely if ever affect unambiguous assignments based on probabilities over 90%. As a safeguard, the alignment is presented to the user together with the assignment results and should be inspected whenever possible.

Figure 4

Distributions of assignment probabilities for correct and wrong assignments. At the levels of species, genus, and family, 90%, 99%, and 99% of assignments of Insecta sequences are correct and 51%, 90%, and % of assignments of Liliopsida sequences are correct. Wrong assignments are generally associated with low probabilities, whereas most correct assignments achieve probabilities above 95%.

Figure 4

Distributions of assignment probabilities for correct and wrong assignments. At the levels of species, genus, and family, 90%, 99%, and 99% of assignments of Insecta sequences are correct and 51%, 90%, and % of assignments of Liliopsida sequences are correct. Wrong assignments are generally associated with low probabilities, whereas most correct assignments achieve probabilities above 95%.

To compare the performance of our approach to that of simple Blast searches, all sample sequences are assigned using new Blast searches. To our knowledge there is no canonical way to use Blast for taxonomic assignment. Here we use the taxonomic annotation associated with the best Blast hit to GenBank, disregarding matches to the sample sequence itself. Blast results were retrieved using remote Blast. In cases of equally high-scoring hits to multiple species, one of these was chosen at random to form the basis of assignment.

Figure 5 compares the two approaches by plotting the tradeoff between sensitivity and specificity in the range of most to least stringent assignment criteria used. Sensitivity is the fraction of sample sequences that are correctly assigned, whereas specificity is the fraction of accepted assignments that are correct. The posterior probability of assignment provided by SAP allows rejection of assignments that do not exceed a minimum assignment probability cutoff. Increasing the stringency of this assignment criterion imposes a more conservative sensitivity-specificity tradeoff. For Blast, the assignment criterion used was a maximum log(E-value) cutoff. The so called ROC plots in Figure 5 show how specificity of SAP can be raised at the expense of sensitivity by changing the assignment probability cutoff from zero to the maximal probability obtained in the analysis. For the Insecta set, sensitivity of Blast was almost identical to that of SAP when all assignments where accepted. For all other sensitivity-specificity combinations, however, the performance of SAP exceeded that of Blast. At the most permissive assignment criteria, the overlap in correct assignments of Insecta sequences was almost complete, with only 3% specific to SAP and 4% to Blast. For the Liliopsida set, the overlap was smaller, with 20% of correct assignments specific SAP and 14% to Blast. The proportion of wrong Blast assignments avoided as a function of posterior probability cutoff (Fig. 6) shows that a large proportion of wrong Blast assignments would be rejected using a stringent assignment criterion in our approach.

Figure 5

ROC (receiver operating characteristic) curves summarizing the tradeoff between sensitivity and specificity in the range of most to least stringent assignment criteria used. Sensitivity is the fraction of all sequences that are correctly assigned, specificity is the fraction of assignments that are correct. The performance of SAP exceeds that of Blast for any sensitivity-specificity combination except when blindly accepting all assignments.

Figure 5

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    Restrictions on data availability should be noted during submission and in the manuscript. "Data not shown" should be avoided: authors are encouraged to publish all observations related to the submitted manuscript as Supplementary Material. "Unpublished data" intended for publication in a manuscript that is either planned, "in preparation" or "submitted" but not yet accepted, should be cited in the text and a reference should be added in the References section. "Personal Communication" should also be cited in the text and reference added in the References section. (see also the MDPI reference list and citations style guide).

    Remote Hosting and Large Data Sets

    Data may be deposited with specialized service providers or institutional/subject repositories, preferably those that use the DataCite mechanism. Large data sets and files greater than 60 MB must be deposited in this way. For a list of repositories specialized in scientific and experimental data, please consult woaknb.wz.sk or woaknb.wz.sk The data repository name, link to the data set (URL) and accession number, doi or handle number of the data set must be provided in the paper. The journal Data also accepts submissions of data set papers.

    References in Supplementary Files

    Citations and References in Supplementary files are permitted provided that they also appear in the reference list of the main text.

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    Research and Publication Ethics

    Research Ethics

    Research Involving Human Subjects

    When reporting on research that involves human subjects, human material, human tissues, or human data, authors must declare that the investigations were carried out following the rules of the Declaration of Helsinki of (woaknb.wz.sk), revised in According to point 23 of this declaration, an approval from an ethics committee should have been obtained before undertaking the research. At a minimum, a statement including the project identification code, date of approval and name of the ethics committee or institutional review board should be cited in the Methods Section of the article. Data relating to individual participants must be described in detail, but private information identifying participants need not be included unless the identifiable materials are of relevance to the research (for example, photographs of participants’ faces that show a particular symptom). Editors reserve the right to reject any submission that does not meet these requirements.

    Example of an ethical statement: "All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of XXX (Project identification code)."

    A written informed consent for publication must be obtained from participating patients who can be identified (including by the patients themselves). Patients’ initials or other personal identifiers must not appear in an image. For manuscripts that include any case details, personal information, and/or images of patients, authors must obtain signed informed consent from patients (or their relatives/guardians) before submitting to an MDPI journal. Patient details must be anonymized as far as possible, e.g., do not mention specific age, ethnicity, or occupation where they are not relevant to the conclusions.

    You may refer to our sample form and provide an appropriate form after consulting with your affiliated institution. Alternatively, you may provide a detailed justification of why informed consent is not necessary. For the purposes of publishing in MDPI journals, a consent, permission, or release form should include unlimited permission for publication in all formats (including print, electronic, and online), in sublicensed and reprinted versions (including translations and derived works), and in other works and products under open access license. To respect patients’ and any other individual’s privacy, please do not send signed forms. The journal reserves the right to ask authors to provide signed forms if necessary.

    Ethical Guidelines for the Use of Animals in Research

    The editors will require that the benefits potentially derived from any research causing harm to animals are significant in relation to any cost endured by animals, and that procedures followed are unlikely to cause offense to the majority of readers. Authors should particularly ensure that their research complies with the commonly-accepted '3Rs':

    • Replacement of animals by alternatives wherever possible,
    • Reduction in number of animals used, and
    • Refinement of experimental conditions and procedures to minimize the harm to animals.

    Any experimental work must also have been conducted in accordance with relevant national legislation on the use of animals for research. For further guidance authors should refer to the Code of Practice for the Housing and Care of Animals Used in Scientific Procedures [1].

    Manuscripts containing original descriptions of research conducted in experimental animals must contain details of approval by a properly constituted research ethics committee. As a minimum, the project identification code, date of approval and name of the ethics committee or institutional review board should be cited in the Methods section.

    Behavioral Sciences endorses the ARRIVE guidelines (woaknb.wz.sk) for reporting experiments using live animals. Authors and reviewers can use the ARRIVE guidelines as a checklist, which can be found at woaknb.wz.sk

    1. Home Office. Animals (Scientific Procedures) Act Code of Practice for the Housing and Care of Animals Used in Scientific Procedures. Available online: woaknb.wz.sk

    Research Involving Cell Lines

    Methods sections for submissions reporting on research with cell lines should state the origin of any cell lines. For established cell lines the provenance should be stated and references must also be given to either a published paper or to a commercial source. If previously unpublished de novo cell lines were used, including those gifted from another laboratory, details of institutional review board or ethics committee approval must be given, and confirmation of written informed consent must be provided if the line is of human origin.

    An example of Ethical Statements:

    The HCT cell line was obtained from XXXX. The MLH1+ cell line was provided by XXXXX, Ltd. The DLD-1 cell line was obtained from Dr. XXXX. The DR-GFP and SA-GFP reporter plasmids were obtained from Dr. XXX and the Rad51KA expression vector was obtained from Dr. XXXX.

    Research Involving Plants

    Experimental research on plants (either cultivated or wild) including collection of plant material, must comply with institutional, national, or international guidelines. We recommend that authors comply with the Convention on Biological Diversity and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.

    For each submitted manuscript supporting genetic information and origin must be provided. For research manuscripts involving rare and non-model plants (other than, e.g., Arabidopsis thaliana, Nicotiana benthamiana, Oriza sativa, or many other typical model plants), voucher specimens must be deposited in an accessible herbarium or museum. Vouchers may be requested for review by future investigators to verify the identity of the material used in the study (especially if taxonomic rearrangements occur in the future). They should include details of the populations sampled on the site of collection (GPS coordinates), date of collection, and document the part(s) used in the study where appropriate. For rare, threatened or endangered species this can be waived but it is necessary for the author to describe this in the cover letter.

    Editors reserve the rights to reject any submission that does not meet these requirements.

    An example of Ethical Statements:

    Torenia fournieri plants were used in this study. White-flowered Crown White (CrW) and violet-flowered Crown Violet (CrV) cultivars selected from ‘Crown Mix’ (XXX Company, City, Country) were kindly provided by Dr. XXX (XXX Institute, City, Country).

    Arabidopis mutant lines (SALKxxxx, SAILxxxx,…) were kindly provided by Dr. XXX , institute, city, country).

    Publication Ethics Statement

    Behavioral Sciences is a member of the Committee on Publication Ethics (COPE). We fully adhere to its Code of Conduct and to its Best Practice Guidelines.

    The editors of this journal enforce a rigorous peer-review process together with strict ethical policies and standards to ensure to add high quality scientific works to the field of scholarly publication. Unfortunately, cases of plagiarism, data falsification, image manipulation, inappropriate authorship credit, and the like, do arise. The editors of Behavioral Sciences take such publishing ethics issues very seriously and are trained to proceed in such cases with a zero tolerance policy.

    Authors wishing to publish their papers in Behavioral Sciences must abide to the following:

    • Any facts that might be perceived as a possible conflict of interest of the author(s) must be disclosed in the paper prior to submission.
    • Authors should accurately present their research findings and include an objective discussion of the significance of their findings.
    • Data and methods used in the research need to be presented in sufficient detail in the paper, so that other researchers can replicate the work.
    • Raw data should preferably be publicly deposited by the authors before submission of their manuscript. Authors need to at least have the raw data readily available for presentation to the referees and the editors of the journal, if requested. Authors need to ensure appropriate measures are taken so that raw data is retained in full for a reasonable time after publication.
    • Simultaneous submission of manuscripts to more than one journal is not tolerated.
    • Republishing content that is not novel is not tolerated (for example, an English translation of a paper that is already published in another language will not be accepted).
    • If errors and inaccuracies are found by the authors after publication of their paper, they need to be promptly communicated to the editors of this journal so that appropriate actions can be taken. Please refer to our policy regarding publication of publishing addenda and corrections.
    • Your manuscript should not contain any information that has already been published. If you include already published figures or images, please obtain the necessary permission from the copyright holder to publish under the CC-BY license. For further information, see the Rights and Permissions page.
    • Plagiarism, data fabrication and image manipulation are not tolerated.
      • Plagiarism is not acceptable in Behavioral Sciences submissions.

        Plagiarism includes copying text, ideas, images, or data from another source, even from your own publications, without giving any credit to the original source.

        Reuse of text that is copied from another source must be between quotes and the original source must be cited. If a study's design or the manuscript's structure or language has been inspired by previous works, these works must be explicitly cited.

        If plagiarism is detected during the peer review process, the manuscript may be rejected. If plagiarism is detected after publication, we may publish a correction or retract the paper.

      • Image files must not be manipulated or adjusted in any way that could lead to misinterpretation of the information provided by the original image.

        Irregular manipulation includes: 1) introduction, enhancement, moving, or removing features from the original image; 2) grouping of images that should obviously be presented separately (e.g., from different parts of the same gel, or from different gels); or 3) modifying the contrast, brightness or color balance to obscure, eliminate or enhance some information.

        If irregular image manipulation is identified and confirmed during the peer review process, we may reject the manuscript. If irregular image manipulation is identified and confirmed after publication, we may correct or retract the paper.

      Our in-house editors will investigate any allegations of publication misconduct and may contact the authors' institutions or funders if necessary. If evidence of misconduct is found, appropriate action will be taken to correct or retract the publication. Authors are expected to comply with the best ethical publication practices when publishing with MDPI.

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