Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

matK-QR classifier: a patterns based approach for plant species identification

  • Ravi Prabhakar More1, 3,
  • Rupali Chandrashekhar Mane2 and
  • Hemant J. Purohit1Email author
BioData Mining20169:39

DOI: 10.1186/s13040-016-0120-6

Received: 10 May 2016

Accepted: 2 December 2016

Published: 9 December 2016

Abstract

Background

DNA barcoding is widely used and most efficient approach that facilitates rapid and accurate identification of plant species based on the short standardized segment of the genome. The nucleotide sequences of maturaseK (matK) and ribulose-1, 5-bisphosphate carboxylase (rbcL) marker loci are commonly used in plant species identification. Here, we present a new and highly efficient approach for identifying a unique set of discriminating nucleotide patterns to generate a signature (i.e. regular expression) for plant species identification.

Methods

In order to generate molecular signatures, we used matK and rbcL loci datasets, which encompass 125 plant species in 52 genera reported by the CBOL plant working group. Initially, we performed Multiple Sequence Alignment (MSA) of all species followed by Position Specific Scoring Matrix (PSSM) for both loci to achieve a percentage of discrimination among species. Further, we detected Discriminating Patterns (DP) at genus and species level using PSSM for the matK dataset. Combining DP and consecutive pattern distances, we generated molecular signatures for each species. Finally, we performed a comparative assessment of these signatures with the existing methods including BLASTn, Support Vector Machines (SVM), Jrip-RIPPER, J48 (C4.5 algorithm), and the Naïve Bayes (NB) methods against NCBI-GenBank matK dataset.

Results

Due to the higher discrimination success obtained with the matK as compared to the rbcL, we selected matK gene for signature generation. We generated signatures for 60 species based on identified discriminating patterns at genus and species level. Our comparative assessment results suggest that a total of 46 out of 60 species could be correctly identified using generated signatures, followed by BLASTn (34 species), SVM (18 species), C4.5 (7 species), NB (4 species) and RIPPER (3 species) methods As a final outcome of this study, we converted signatures into QR codes and developed a software matK-QR Classifier (http://www.neeri.res.in/matk_classifier/index.htm), which search signatures in the query matK gene sequences and predict corresponding plant species.

Conclusions

This novel approach of employing pattern-based signatures opens new avenues for the classification of species. In addition to existing methods, we believe that matK-QR Classifier would be a valuable tool for molecular taxonomists enabling precise identification of plant species.

Keywords

Plant species identification MatK and rbcL loci Regular expression Signatures MatK classifier software

Background

In recent years, DNA barcoding is considered as a universal species identification method for plants. It mainly involves discrimination of species through standardized molecular marker gene and is gaining support from the taxonomists as well. DNA barcoding has wider applications in different studies namely to predict cryptic species, to study biological samples in forensics and conservation sciences for characterization of biodiversity; to track inventory for plants identity or purity and in ecological species diversity studies [14]. Various molecular markers have been used for DNA barcoding studies. A 650 base pair (bp) of the mitochondrial cytochrome c oxidase unit I (COI) gene was used as a barcode in various organisms such as animals, birds, fishes, insects and nematodes [48]. A specific region of the nuclear ribosomal internal transcribed spacer (ITS) gene is the well-studied DNA barcode for fungi [9]. In plant DNA barcoding, there has been extensive debate about the locus choice; several regions of the genome (trnH, psbA, rpoC1, rpoB, atpF, atpH, psbK, and psbI) were referred as candidate markers with different discrimination success. However, the two loci ribulose-1,5-bisphosphate Carboxylase (rbcL) and maturase K (matK) gene regions are widely used in plant barcoding studies for phylogenetic analyses or species identification [10, 11]. Seven chloroplast loci have been tested for plant species identification by The Consortium for the Barcode of Life (CBOL) Plant Working Group, where the suitability of matK and rbcL loci as a barcode was showed [12]. Vinitha et al. [13] studied a total of 20 species belonging to the family Zingiberaceae from India by using nine plastids and two nuclear loci and reported that matK and rbcL aids in the determination of 15 species (75%) into monophyletic groups. Techen et al. [14] mentioned that the matK region was preferred as a barcode candidate because of high evolutionary rate, low transition/transversion rate, and inter-specific divergence.

In plant DNA barcoding, data mining of marker genes is a noteworthy step that directly assists in species identification. Towards practical uses of DNA barcoding in order to assign sequences to species, various methods have been proposed. The tree-based phylogenetic analysis is a popular method for estimate time of divergence between a group of organisms and relationship among the species. The most common method for species identification is Basic Local Alignment Search Tool (BLAST) followed by distance matrix computations. There could be a possibility that the phylogenetic tree-based method (like Neighbor-joining (NJ) or Maximum Likelihood) gives the lowest accuracy due to unavailability of homologs in databases [15]. To identify species, the TaxI program was developed using distance models to compute the sequence divergences between a query and reference sequences [16]. Diazgranados and Funk [17] published a Quick Response (QR) barcoding system on specimens for biological collections. On a similar line, QR code symbols were implemented to encrypt the five different loci sequences by Liu et al. [18] and performed the species identification based on combined BLAST and distance-based methods.

Weitschek et al. [19] evaluated successfully the performance of the function-based method i.e. Support Vector Machines (SVM), Naïve Bayes, the rule-based RIPPER, and the decision tree C4.5 for DNA barcodes classification purposes, but their performance was not consistent with all taxonomic studies. The exact taxonomic classification of barcodes with highly similar and closely related taxa sequences remains problematic due to the algorithmic bias. Although matK and rbcL loci are used for most of the studies, but it has been found that they are having variable regions with few base pairs. As more plant DNA barcodes based on multiple loci became available, there will be an inclination towards the study of gene-specific species identification. In a recent study, we uncovered a role for discriminating patterns in 16S rRNA gene sequences and generated four taxa signatures (i.e. Bacilli, Bacillales, Bacillaceae, and Bacillus) for taxonomic classification through alignment-free DNABarID software in Bacteria [20]. Similarly, Weitschek et al. described the use of logic alignment-free approach for bacterial genome identification [21]. However, there is no single software available that uses gene-specific (matK) multiple nucleotide patterns for plant species identification. Therefore, one ongoing challenge for DNA barcoding is to develop a precise and reliable approach for speed up species identification process. It is important to note that marker genes hold the discriminating and unique nucleotide patterns which are mainly responsible for sequence classification. In view of this, Position Specific Scoring Matrix (PSSM) could be implemented to detect variable sites in the gene which can further be used for species identification. PSSM is an alignment based method commonly used for a pattern or motif discovery in nucleotide and amino acid sequences and tested to analyze matK gene sequences of Nymphaea and Nuphur plants of basal angiosperms [22]. However, characters-based PSSM approach is also being used to identify species and is often more effective for analyzing nucleotide sequences [23, 24]. Here we present a signatures-based search method and the corresponding software tool, matK-QR Classifier on the Windows platform (http://www.neeri.res.in/matk_classifier/index.htm), which is different from the early tools in the set of predictive nucleotide patterns and the method of sequence classification. Molecular signatures (here we referred as a regular expression) are defined as DNA nucleotide motifs that are unique and present in target species but different from other species sequences [25].

In the present study, we try to show the pattern based utility for species identification by targeting matK gene using PSSM method. Similar approaches can be carried out in order to study plant DNA barcodes studies on individual or combined loci, where existing methods are not sufficient to discriminate species. Rubino and Attimonelli [26] described the use of Regular Expression Blasting algorithm using the information of multiple aligned sequences. In order to generate regular expression or signatures for species identification, it is important to find the discriminating nucleotide patterns and their precise positions in the marker gene. To meet this goal, we assessed the discrimination power of matK and rbcL loci datasets of the CBOL Plant Working group [12]. We obtained the discriminating patterns of matK gene sequences at genus and species levels and generated 60 species signatures by using the MSA and PSSM data. We encrypted the signatures into the QR code for graphical representation. Additionally, we performed a comparative assessment of signatures with other sequence classification software’s and showed the suitability of the generated signatures in terms of sensitivity and specificity for plant species identification.

Methods

In this section, we described a workflow aspects geared toward methodological signature generation, which includes the methods regarding developing a strategy for signature, use of the statistical method, signature evaluation, and software implementation. The species signature generation consisting of data retrieval, analysis by various methods, search for various patterns and signatures and finally the creation of software. The overall workflow used for present study is illustrated in Fig. 1. All analysis were performed using Perl (BioPerl 1.6.1), R (2.15.2) statistical programming scripts and partly with text editor EditPadPro 7.2.3 (www.editpadpro.com) on a workstation (Intel Xeon CPU, 2.80GHz (2 processors), 12 GB RAM).
Fig. 1

The overall workflow used for pattern-based signatures generation in the present study

Retrieval of dataset for signature generation

We referred the plant species barcode dataset reported by the CBOL Plant Working Group and Janzen DH for the present study, which is available at GenBank Nucleotide Database [12]. The dataset comprised of total 404 species (mainly from angiosperms, cryptogams, and gymnosperms) representing seven candidate plastid DNA barcoding loci (rpoC1, rpoB, matK, trnH-psbA, rbcL, atpF-atpH, and psbK-psbI). The dataset used for downstream analysis have been chosen with the following filters: (i) sequences with matK and rbcL loci; (ii) species with two or more representative sequences; and (iii) excluded species with N’s in the sequences. After filtering, the final dataset comprises 344 DNA Barcode sequences from 125 species, which represents a total of 52 genera. The details of two selected loci datasets are summarized in Additional file 1.

Position specific scoring matrix (PSSM) analysis

We performed multiple sequence alignment (MSA) of matK and rbcL data sets using ClustalW2 software implemented in BioEdit 7.2.5 [27]. Then, the gap region in alignments was trimmed and obtained equal length alignment file for downstream analysis. The distance matrix was created to estimate the evolutionary distances using PSSM in R statistical package. PSSM method includes the relative frequencies obtained by counting the occurrence of each nucleotide at each position of the alignment, followed by normalization of the frequencies. Thus, for a set X with N aligned sequences of alignment length c, the elements of PSSM were calculated using the equation below:
$$ {M}_{k,j}=\frac{1}{N}{\displaystyle \sum_{i=1}^NI\left({X}_{i,j}=k\right)} $$

Where, i = 1, 2, …, N refers to aligned sequence; j = 1, 2, …, c refers to a column position of alignment; I is an indicator function taking value either 0 or 1; k is the set of 4 nucleotides (i.e. A, T, G, and C).

PSSM considers statistical independence among the adjacent positions, as the probabilities for each position are estimated independently of other positions. We considered the sum of the values in a particular position equals 1.0. Each column of the alignment can be viewed as an independent multinomial distribution. To determine the distance between two sequences, the probability of a nucleotide at each position in each sequence was generated. The Euclidean distance between the two probability arrays was calculated as a measure of similarity between the two sequences.

The percentage of species discrimination analysis

To assess the discriminatory power of rbcL and matK loci, the pairwise sequence divergence was calculated using PSSM information and subjected to the percentage of species discrimination. For comparing the sequence variation at different loci, Wilcoxon signed-rank test was used. The success rate of species discrimination is the proportion of species that could be differentiated from all possible species pairs. A pair of species was scored as successfully discriminated if the inter-species distance was higher than the intra-species distance and zero. Finally, we obtained a total number of species by using matK and rbcL loci. All analysis steps were performed with R statistical package.

Identification of discriminating patterns

We performed two-step (genus and species level) analysis to get the discriminatory patterns in the matK gene. In the first step, at the genus level, we grouped the genera wise sequences and performed MSA to get the conserved consensus sequence for each genus. We computed Position Frequency Matrix (PFM) and Position Probability Matrix (PPM) and obtained the variable sites as discriminating patterns among the genus consensus sequences. In the subsequent step, at the species level, we performed MSA of all sequences from each genus to obtained discriminating patterns.

Species signature generation

To generate the signature (i.e. regular expression), we created two datasets (i.e. target species and other species). After discriminating pattern identification, the signature generation process was carried out by the following main steps: the identified position-specific discriminating nucleotides were marked as one in target species and were considered as discriminating patterns; next, we determined the nucleotide bp distances between two consecutive patterns in target species sequences. The patterns and their ordered arrangement with distances in target species were taken into account for signature generation. Additionally, we referred to the MSA for identification of single bp change among the species. We incorporated few nucleotides into the signatures which can discriminate against false positive hits. All the species signatures were generated by performing the same procedure and searched against the target and other species sequences. We determined the recall and precision for each signature as:
  • Recall = True Positives/(True Positives + False Negatives),

  • Precision = True Positives/(True Positives + True Positives), and

  • F-measure = 2((Precision*Recall)/(Precision + Recall)).

Among the different possible signatures for a species, one with higher sensitivity and specificity was selected as the candidate signature for that species.

Comparative assessment of signatures with other methods

To evaluate all the signatures with sequences present in public database, we retrieved matK sequence data set from the NCBI-GenBank database, with the following filters (Species: plants, Source database: INSDC GenBank and RefSeq, Genetic Component: Chloroplast, Sequence length: <2000). The data comprises 89890 matK gene sequences. The sensitivity and specificity of each species signature were tested against the data set.

For comparative assessment of signatures with other methods, we have sub-sampled all the 60 species sequences from the matK dataset. The final data set comprises 442 query sequences. We evaluated classification performance of signatures with homology-based BLASTn, the function-based method Support Vector Machines (SVM), Naïve Bayes, the rule-based RIPPER, and the decision tree C4.5 using WEKA data mining tool. We converted matK FASTA sequences to the WEKA format and used the procedure published by Weitschek et al. [19] for DNA barcode sequence classification using the WEKA tool. We performed similarity search of all query sequences against the GenBank nucleotide (nt) database as on 28-02-2016, using standalone BLASTn 2.2.24 [28]. The species name of the first hit was assigned to each of the query sequences. To test the performance of species identification accuracy, obtained hits were used to determine the recall, precision, and F-measure for all the methods.

QR code signature based ‘matK-QR Classifier’ software

We developed ‘matK-QR Classifier’ software for species identification through the generated 60 plant species signatures. For symbolic representation, we encrypted each signature into QR code using Free QR Creator software (http://www.smp-soft.com). This software performs regular expression based search between species signatures (QR-coded) against a query matK gene sequences and assigns species if the signature is matched. This software is based on Windows platform. Perl script was written for regular expression search, which was converted to the executable file using the software Perl Dev Kit (PDK) (www.activestate.com/perl-dev-kit). The front-end design of the software was implemented in C# programming language.

Results

Alignment, PSSM and species discrimination

Multiple sequence alignment of the matK and rbcL loci datasets (125 species) produced a final of 520 and 532 bp aligned regions respectively. To further extend our analysis, we generated the position specific scoring matrix from each alignment and created a conserved matrix of the four nucleotides (i.e. A, T, G, and C). Proportional conservation at each position was calculated ranging from 0 to 1. For instance, if G is completely conserved at the position, then the estimated value is 1. The generated PSSM information comprised a positional frequency matrix (PFM) and positional probability matrix (PPM) for both loci sequences (Additional file 2). Based on PSSM information, the pairwise distances between all species sequences were computed and a distance matrix was created for species discrimination analysis.

Species discrimination results for both loci using PSSM method are given in Table 1. The result revealed that a total of 75 and 71 out of 125 species were discriminated for matK and rbcL respectively. However, remaining species could not be distinguished due to the lack of variable sites in sequences. The discriminated and non-discriminated species names are listed in Additional file 3.
Table 1

The discriminating and not discriminating species in matK and rbcL loci using PSSM method

No.

Locus

No. of discriminating species

No. of non-discriminating species

1

matK

75

50

2

rbcL

71

54

Identification of discriminating patterns and species signature generation

Due to the higher discrimination success given by the matK as compared to the rbcL, we selected matK gene for signature generation. The variable nucleotide sites (i.e. discriminating patterns) for each genus are listed in Additional file 4.

Finally, the genus and species level discriminating patterns were combined and considered for signature generation. Species signatures generated were unique in terms of a combination of species-specific nucleotide patterns and the exact distance between two consecutive patterns.

By performing the sensitivity and specificity test for all the signatures, we generated the signatures for 60 out of the 75 species (Additional file 5). We could not generate signatures for the remaining 15 species due to the mismatch of nucleotides distances. Different signatures have been covered different spanning region in the matK gene. This is due to the fact of a different number of patterns, size, and base pair distances between consecutive patterns. For instance, the Croton gratissimus species signature is given in Fig. 2a. All patterns of the signature are mapped on one representative matK gene sequence having GenBank accession number EU214230.1 (Fig. 2b). The signature comprised of total 9 discriminating patterns, which are spanning a 704 bp region from nucleotide position 39 to 742 bp in total sequence length of 742 bp. The unique discriminating patterns are present at 39, 129, 188, 294, 365, 531, 639 and 735 start positions in the sequence, which make signature unique to this particular species.
Fig. 2

An example of the representation of the species signature in nucleotide maturase K (matK) gene sequence (a) Croton gratissimus (b) Croton gratissimus voucher OM785 (EU214230.1). The patterns are highlighted in the same color in signature and sequences

Nucleotide patterns evolution among the species

To understand the evolution of patterns among the species and its role in sequence discrimination, we described species signatures of three genera, which are representing three different scenarios (Fig. 3). For instance, the species signatures (for Croton gratissimus, Croton megalobotrys, and Croton pseudopulchellus) of the genus Croton are displayed in Fig. 3a. As apparent from Figure, though the nucleotides distance among the patterns is similar in all the three signatures, a single nucleotide substitution in all the nine patterns is found to be responsible for inter-species discrimination. Among the signatures of C. gratissimus and C. megalobotry, the third position in the first pattern (GTCTTGGAATAGG) has been substituted (C/A). However, this substitution is not observed among C. megalobotry and C. pseudopulchellus. Similarly, among the signatures of C. megalobotry and C. pseudopulchellus, the fifth position in the second pattern (TTCTCATGT) has been substituted (C/T) but not between C. gratissimus and C. megalobotry. This shows that dissimilar patterns are responsible for the closest species discrimination. For the genus Populus (Fig. 3b), the distinctive patterns (highlighted in green) along with its bp distances are crucial factors for discrimination of the two species. In the case of the genus Poa (Fig. 3c), it can be observed that a single bp difference (G/C) in the last pattern (highlighted in red) play a significant role in the classification of Poa annua and Poa compressa species.
Fig. 3

Evolution of nucleotide patterns of signatures in species of the genus (a) Croton (b) Populus (c) Poa. The key discriminating nucleotide positions are highlighted in red

Signatures evaluation and comparative assessment of other methods

We evaluated the sequence classification performance of 60 species signatures against the matK sequences in the NCBI-GenBank database. For comparative assessment purpose, we compared generated signatures with the BLASTn, SVM, Naïve Bayes, RIPPER, and C4.5 methods. Recall and precision are usual criteria for identifying the accuracy level of different species identification methods, and the F-measure is one of the important evaluation features which can be applied for selecting the best classification method. To get detailed information about how the sequences are assigned to the same and other species among different algorithms, we determined the recall, precision, and F-measure for all the methods.

We categorized the performance of the signatures according to the F-measure (higher to lower) and assigned them into four main categories based on F-measure value interval namely A (0.75–1), B (0.50–0.74), C (0.25–0.49), and D (0–0.24). We displayed the classification accuracy of the 60 species signatures with all other methods (Fig. 4). The detailed distribution of each species with the recall and precision and F-measure values of all methods are presented in Additional file 6. In A category, we obtained 46, 34, 18, 4, 3, and 7 species out of the 60 species for signatures, BLASTn, SVM, NB, RIPPER, and C4.5 respectively. In the ‘B’ category, there were 13, 14, 13, 11, 6, and 7 species could show comparable F-measure value for signatures, BLASTn, SVM, NB, RIPPER, and C4.5 respectively. We noticed that only three species grouped into C and D categories for Signatures, whereas 12, 29, 42, 51, and 37 species are grouped in BLASTn, SVM, NB, RIPPER, and C4.5 respectively. These results demonstrate that the performance of other methods in this category is the worse than signatures. However, BLASTn performance has better F-measure, followed by SVM, NB, RIPPER, and C4.5 considering A and B categories. The C4.5 method performs best in category B, which classified 16 species in comparison with Signatures (13 species), BLASTn (14 species), SVM (13 species), NB (11 species), and RIPPER (6 species) methods. Overall, from this result, we observed that signatures are the best in terms of recall and precision followed by BLASTn, SVM, C4.5, NB and RIPPER methods.
Fig. 4

The comparative assessment of species signatures with BLASTn, SVM, Naive Bayesian, RIPPER, and C4.5 methods using F-measure values (a) from 0.75 to 1 (b) from 0.5 to 0.74 (c) from 0.25 to 0.49, and (d) from 0 to 0.24

QR code signature based ‘matK-QR Classifier’ software

In order to show that the signatures are capable of identifying the correct plant species, we have developed and tested ‘matK-QR Classifier’ software with QR code symbology. The graphical interface and encrypted QR codes of the signatures are shown in Fig. 5. For species identification using signatures, the user may need to upload query matK sequences in FASTA format using the menu option (Sequence- open-Browse) (Fig. 5a). The user can choose the species QR signature of interest by using the option ‘Signature Menu-Open QR signature- Select QR code signature (ex. Acacia exuvialis.png)’ (Fig. 5b). Now the software decodes QR-code into the regular expression and proceeds further for searching it against query matK sequences. Finally, ‘Search’ option performs the species identification task by searching for the signature in the query sequence. The species name of the matched signature is assigned to the query sequence. After clicking on ‘show result’, the identified and non-identified species sequences summary is returned to the result panel. The output file contains information on sequence length, the location of signature in sequences and results can export in text files. Also, there is an option to convert FASTA sequence into the QR code and vice-versa under the ‘Signature menu-Generate QR codes’. The step-by-step analysis procedure is mentioned in the user manual to perform species identification using QR signatures (Additional file 7). The matK-QR Classifier, QR Signatures, and sample sequences file can be obtained from http://www.neeri.res.in/matk_classifier/index.htm.
Fig. 5

The matK-QR Classifier software (a) Graphical user interface (b) 60 species signatures encrypted into QR-codes

Discussion

In the present study, we showed the applicability of signature-based identification of plant species by using matK gene sequences. For the signature generation, the marker gene selection is an important consideration and it should hold enough variation to classify the species [29]. Our results revealed that matK gene showed greater discrimination success as compared to the rbcL gene (Table 1). Based on species percentage discrimination, we confirmed that the matK gene could be the best target for the signature generation due to the presence of variable nucleotide sites. As a consequence, we generated 60 species signatures to classify closely related sequences by using the PSSM information (Additional file 5). These results are consistent with earlier plant DNA barcoding studies that mentioned matK as a suitable barcode; this gene can discriminate morphologically similar and closely related species [13, 20, 30, 31].

The signature generation is a multistep procedure which depends on data set selection, alignment, PSSM nucleotide conservation, discriminating patterns identification and distance calculation among sequential patterns (Fig. 1). The generated species signatures showed that matK gene contains species-specific distinguishing patterns separated by a precise distance which can be considered as a regular expression (Fig. 2). In most cases, we noticed that a single nucleotide substitution was responsible for discrimination instead of larger patterns across species in the same genus (Fig. 3). This is due to the fact that the matK gene is highly conserved with low evolutionary distance rate [32].

In Weitschek et al. [19], the results indicated that the SVM and Naïve Bayes method performs the best than the RIPPER and C4.5 methods mainly on the data sets. In the present study, we showed the signature perform better than supervised methods (SVM and Naïve Bayes) specific to matK DNA barcodes of targeted plant taxa. This result supports an earlier finding by Seo TK [33] study that SVM method poorly performed in sequence classification for amphibians and dipterans due to the reduction in time durations of the ancestral populations during the evolutionary processes. In the case of species identification of insect (class Insecta) with COI gene sequences, the naïve Bayesian classifier method was used and showed better results [34]. In contrast, we obtained poor performance of the naïve Bayesian method as compared to signatures methods for targeted plant species classification. In previous studies, VIP Barcoding and BLOG tools were developed based on CV and K2P nearest-neighbour, and character-based methods to analyze DNA barcodes sequences [15, 35]. On similar approach, we used the discriminative nucleotide pattern to classify DNA barcodes using MSA and PSSM method. However, we generate matK gene-specific signatures for plant species identification. As stated above in the results section, the majority of the species are correctly classified by signatures method in category A with a high level of F-measure confidence. A signature method obtains superior classification performances with respect to all other methods. This finding confirms the fact that these signatures are more reliable for corresponding species identification. Although accuracy is low in the ‘B’ category, we find a high level of congruence between the signatures with all other methods. The possible reason behind this is that few bp variations exist in the matK gene sequence; due to this, all methods could not provide correct species identification. Signatures performed well in C and D categories to classify sequence with respective species, which underlies the importance of pattern based approach. Though signatures performed well in sequence classification, there could be probable limitations of signatures. 16 out of 60 species signatures produced false positive predictions (matched with other species sequences). It may be speculated that the possible reasons for this observation are partial sequence length, ambiguous nucleotide position, and the mismatch of the nucleotide distances between the two patterns. We acknowledge that signatures are not suitable to test on simulated data set as it was generated on the real DNA barcode dataset available in public databases.

For practical application of signatures, we developed the user-friendly graphical ‘matK-QR Classifier’ software on the Windows platform for signature based species identification (Fig. 5). This software accepts matK gene sequences and uses the discriminating pattern information in the form of signatures to identify plant species. The software allows users to select the QR-coded signature for a species of interest out of 60 signatures (Fig. 5b). It performs regular expression search against user defined query matK sequence. matK-QR Classifier software has various advantages over other types of DNA barcoding methods. Firstly, the user doesn’t need to pre-process input barcode sequences, alignment, and parameters setting to run the analysis like WEKA supervised methods (SVM, NB, and others). Earlier, it was pointed out that the alignment-free approach mainly features vectors and rule-based methods take low computing processing power [36, 37], due to this reason a signatures-based method is faster than the other methods because of regular expression based search. Secondly, matK-QR Classifier delivers ease-of-use, an easily accessible graphical user interface to the user. Third, matK-QR Classifier does not require pre-defined parameters and configuration prior to signature search against sequences. In contrast, several steps are required to be performed for other methods with different parameters, which are rather computationally expensive in case of a large number of sequences [38, 39]. Lastly, there is no requirement of training dataset or reference knowledge to perform analysis, which is one of the limitations of supervised methods like SVM and the Naïve Bayes [19].

Taking all these observations into account, our study suggests that PSSM method can be helpful to classify closely related species by considering large numbers of the matK sequences as compared to rbcL sequences. This outcome of study leads to generate signatures by using the matK sequences. To our knowledge, matK-QR Classifier is the only signature-based software to identify plant species based on matK gene-specific discriminating patterns. matK Classifier software addresses an important need of users, a graphic user interface providing a simple FASTA format sequence input and QR code signatures. We propose that signatures-based matK-QR Classifier can useful software for facilitating the plant species identification along with existing DNA barcoding methods and may also be applicable as a species identification tool for other organisms. In conclusion, we suggest that a similar approach could generate the signatures with single or multiple loci for plant species identification that is important in biodiversity analysis and routine identification purpose in plant evolutionary studies.

Abbreviations

BLAST: 

Basic local alignment search tool

Bp: 

Base pair

CBOL: 

The consortium for the barcode of life

matK: 

MaturaseK

MSA: 

Multiple sequence alignment.

PSSM: 

Position specific scoring matrix

QR: 

Quick response

rbcL: 

Ribulose-1, 5-bisphosphate carboxylase

Declarations

Acknowledgements

The first author acknowledges the Swami Ramanand Teerth Marathwada University (SRTMU), Nanded, Maharashtra, India for carrying out this study as a part of Ph.D. We thank the support of the Dr. Dhananjay V. Raje for statistical analysis and Ms. Trupti Kahate (MDS Bio-Analytics, Nagpur, Maharashtra, India) for helping in software development. The authors would like to thank Ms. Malathi V. M. (DST-SRF, ICAR- NBAIR, Bangalore) for help in manuscript writing. Authors are thankful to Director, CSIR-NEERI, Nagpur, Maharashtra, India for supporting this study under CSIR- ESC108 program.

Funding

This study was not funded by any agency.

Availability of data and materials

Project name: Bioinformatics project

Project home page: http://www.neeri.res.in/matk_classifier/index.htm

Archived version: 1.0

Operating system(s): Windows 32/64 bit

Programming language: Perl and C#

Other requirements: Microsoft.NET Framework 4

License: None

Any restrictions to use by non-academics: None

Authors’ contributions

RPM, RCM, HJP contributed to the experiment implementation; RPM, RCM, HJP contributed to data analysis; HJP contributed to the principal design & troubleshooting; RPM, RCM, HJP contributed to manuscript writing. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Environmental Genomics Division, CSIR-National Environmental Engineering Research Institute
(2)
MDS Bio-Analytics
(3)
Present Institute: Division of Molecular Entomology, ICAR- National Bureau of Agricultural Insect Resources (NBAIR)

References

  1. Hebert PD, Stoeckle MY, Zemlak TS, Francis CM. Identification of birds through DNA barcodes. PLoS Biol. 2004;2(10):e312. doi:10.1371/journal.pbio.0020312.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Tyagi A, Bag SK, Shukla V, Roy S, Tuli R. Oligonucleotide frequencies of barcoding loci can discriminate species across kingdoms. PLoS One. 2010;5(8):e12330. doi:10.1371/journal.pone.0012330.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Galimberti A, De Mattia F, Losa A, Bruni I, Federici S, Casiraghi M, et al. DNA barcoding as a new tool for food traceability. Food Res Int. 2012;50(1):55–63. doi:10.1016/j.foodres.2012.09.036.View ArticleGoogle Scholar
  4. Huxley‐Jones EL, Shaw JL, Fletcher C, Parnell J, Watts PC. Use of DNA barcoding to reveal species composition of convenience seafood. Conserv Biol. 2012;26(2):367–71. doi:10.1111/j.1523-1739.2011.01813.x.View ArticlePubMedGoogle Scholar
  5. Hebert PDN, Ratnasingham S, deWaard JR. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc Biol Sci. 2003;270(1):S96–9. doi:10.1098/rsbl.2003.0025.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Lijtmaer DA, Kerr KC, Stoeckle MY, Tubaro PL. DNA barcoding birds: from field collection to data analysis. Methods Mol Biol. 2012;858:127–52. doi:10.1007/978-1-61779-591-6_7.View ArticlePubMedGoogle Scholar
  7. Knebelsberger T, Landi M, Neumann H, Kloppmann M, Sell AF, Campbell PD, et al. A reliable DNA barcode reference library for the identification of the North European shelf fish fauna. Mol Ecol Resour. 2014;14(5):1060–71. doi:10.1111/1755-0998.12238.PubMedGoogle Scholar
  8. Kiewnick S, Holterman M, van den Elsen S, van Megen H, Frey JE, Helder J. Comparison of two short DNA barcoding loci (COI and COII) and two longer ribosomal DNA genes (SSU &amp; LSU rRNA) for specimen identification among quarantine root-knot nematodes (Meloidogyne spp.) and their close relatives. Eur J Plant Pathol. 2014;140(1):97–110.View ArticleGoogle Scholar
  9. Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for fungi. Proc Natl Acad Sci U S A. 2012;109(16):6241–6. doi:10.1073/pnas.1117018109.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Li X, Yang Y, Henry RJ, Rossetto M, Wang Y, Chen S. Plant DNA barcoding: from gene to genome. Biol Rev Camb Philos Soc. 2015;90(1):157–66. doi:10.1111/brv.12104.View ArticlePubMedGoogle Scholar
  11. Hollingsworth PM. DNA barcoding plants in biodiversity hot spots: progress and outstanding questions. Heredity. 2008;101(1):1–2. doi:10.1038/hdy.2008.16.View ArticlePubMedGoogle Scholar
  12. Plant Working Group CBOL, Janzen DH. A DNA barcode for land plants. Proc Natl Acad Sci U S A. 2009;106(31):12794–7. doi:10.1073/pnas.0905845106.View ArticleGoogle Scholar
  13. Vinitha MR, Kumar US, Aishwarya K, Sabu M, Thomas G. Prospects for discriminating Zingiberaceae species in India using DNA barcodes. J Integr Plant Biol. 2014;56(8):760–73. doi:10.1111/jipb.12189.View ArticlePubMedGoogle Scholar
  14. Techen N, Parveen I, Pan Z, Khan IA. DNA barcoding of medicinal plant material for identification. Curr Opin Biotechnol. 2014;25:103–10. doi:10.1016/j.copbio.2013.09.010.View ArticlePubMedGoogle Scholar
  15. Fan L, Hui JH, Yu ZG, Chu KH. VIP Barcoding: composition vector‐based software for rapid species identification based on DNA barcoding. Mol Ecol Resour. 2014;14(4):871–81. doi:10.1111/1755-0998.12235.View ArticlePubMedGoogle Scholar
  16. Steinke D, Vences M, Salzburger W, Meyer A. TaxI: a software tool for DNA barcoding using distance methods. Philos Trans R Soc Lond B Biol Sci. 2005;360(1462):1975–80. doi:10.1098/rstb.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Diazgranados M, Funk VA. Utility of QR codes in biological collections. Phytokeys. 2013;34(25):21–34. doi:10.3897/phytokeys.25.5175.View ArticleGoogle Scholar
  18. Liu C, Shi L, Xu X, Li H, Xing H, Liang D, et al. DNA barcode goes two-dimensions : DNA QR code Web. PLoS One. 2012;7(5):e35146. doi:10.1371/journal.pone.0035146.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Weitschek E, Fiscon G, Felici G. Supervised DNA barcodes species classification: analysis, comparisons and results. BioData Min. 2014;7(1):4. doi:10.1186/1756-0381-7-4.View ArticlePubMedPubMed CentralGoogle Scholar
  20. More RP, Purohit HJ. The identification of discriminating patterns from 16S rRNA gene to generate signature for bacillus genus. J Comput Biol. 2016;23(8):651–61. doi:10.1089/cmb.2016.0002.View ArticlePubMedGoogle Scholar
  21. Weitschek E, Fabio C, Giovanni F. LAF: logic alignment free and its application to bacterial genomes classification. BioData Min. 2015;8(1):1. doi:10.1186/s13040-015-0073-1.View ArticleGoogle Scholar
  22. Biswal DK, Debnath M, Kumar S, Tandon P. Phylogenetic reconstruction in the order nymphaeales : ITS2 secondary structure analysis and in silico testing of maturase k (matK) as a potential marker for DNA bar coding. BMC Bioinf. 2012;13(17):S26. doi:10.1186/1471-2105-13-S17-S26.Google Scholar
  23. Rach J, DeSalle R, Sarkar IN, Schierwater B, Hadrys H. Character-based DNA barcoding allows discrimination of genera, species and populations in Odonata. Proc Biol Sci. 1632;2008(275):237–47. doi:10.1098/rspb.2007.1290.Google Scholar
  24. Frith MC, Spouge JL, Hansen U, Weng Z. Statistical significance of clusters of motifs represented by position specific scoring matrices in nucleotide sequences. Nucleic Acids Res. 2002;30(14):3214–24. doi:10.3410/f.1008304.104358.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Lee HP, Sheu TF. An algorithm of discovering signatures from DNA databases on a computer cluster. BMC Bioinf. 2014;15:339. doi:10.1186/1471-2105-15-339.View ArticleGoogle Scholar
  26. Rubino F, Attimonelli M. RegExpBlasting (REB), a regular expression blasting algorithm based on multiply aligned sequences. BMC Bioinf. 2009;10(6):S5. doi:10.1186/1471-2105-10-S6-S5.View ArticleGoogle Scholar
  27. Hall TA. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp Ser. 1999;41:95–8. doi:10.1021/bk-1999-0734.ch008.Google Scholar
  28. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402. doi:10.1093/nar/25.17.3389.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Selvaraj D, Sarma RK, Shanmughanandhan D, Srinivasan R, Ramalingam S. Evaluation of DNA barcode candidates for the discrimination of the large plant family Apocynaceae. Plant Syst Evol. 2015;301(4):1263–73. doi:10.1007/s00606-014-1149-y.View ArticleGoogle Scholar
  30. Parveen I, Singh HK, Raghuvanshi S, Pradhan UC, Babbar SB. DNA barcoding of endangered Indian Paphiopedilum species. Mol Ecol Resour. 2012;12(1):82–90. doi:10.1111/j.1755-0998.2011.03071.x.View ArticlePubMedGoogle Scholar
  31. Nithaniyal S, Newmaster SG, Ragupathy S, Krishnamoorthy D, Vassou SL, Parani M. DNA barcode authentication of wood samples of threatened and commercial timber trees within the tropical dry evergreen forest of India. PLoS One. 2014;9(9):e107669. doi:10.1371/journal.pone.0107669.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Saarela JM, Sokoloff PC, Gillespie LJ, Consaul LL, Bull RD. DNA barcoding the Canadian arctic flora: core plastid barcodes (rbcL + matK) for 490 vascular plant species. PLoS One. 2013;8(10):1–36. doi:10.1371/journal.pone.0077982.View ArticleGoogle Scholar
  33. Seo TK. Classification of nucleotide sequences using support vector machines. J Mol Evol. 2010;71(4):250–67. doi:10.1007/s00239-010-9380-9.View ArticlePubMedGoogle Scholar
  34. Porter TM, Gibson JF, Shokralla S, Baird DJ, Golding GB, Hajibabaei M. Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier. Mol Ecol Resour. 2014;14:929–42.PubMed CentralGoogle Scholar
  35. Weitschek E, Velzen R, Felici G, Bertolazzi P. BLOG 2.0: a software system for character-based species classification with DNA barcode sequences. What it does, how to use it. Mol Ecol Resour. 2013;13(6):1043–6. doi:10.1111/1755-0998.12073.PubMedGoogle Scholar
  36. Polychronopoulos D, Weitschek E, Dimitrieva S, Bucher P, Felici G, Almirantis Y. Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers. Genomics. 2014;104(2):79–86. doi:10.1016/j.ygeno.2014.07.004.View ArticlePubMedGoogle Scholar
  37. Kuksa P, Pavlovic V. Efficient alignment-free DNA barcode analytics. BMC Bioinf. 2009;10(14):S9. doi:10.1186/1471-2105-10-S14-S9.View ArticleGoogle Scholar
  38. Wheeler WC. Sources of ambiguity in nucleic acid sequence alignmen. In: Schierwater B, Streit B, Wagner GP, DeSalle R, editors. Molecular ecology and evolution: approaches and applications. Basel: Birkhauser Verlag; 1994. p. 323–52.View ArticleGoogle Scholar
  39. Morrison DA, Ellis JT. Effects of nucleotide sequence alignment on phylogeny estimation: a case study of 18S rDNAs of apicomplexa. Mol Biol Evol. 1997;14(4):428–41. doi:10.1186/1471-2148-10-210.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2016

Advertisement