title: matK-QR classifier: a patterns based approach for plant species identification.
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 usedmatKandrbcLloci 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 thematKdataset. 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-GenBankmatKdataset.
  
  
  RESULTS
  Due to the higher discrimination success obtained with thematKas compared to therbcL, we selectedmatKgene 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 softwarematK-QR Classifier (http://www.neeri.res.in/matk_classifier/index.htm), which search signatures in the querymatKgene 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 thatmatK-QR Classifier would be a valuable tool for molecular taxonomists enabling precise identification of plant species.
authors.affiliation: Environmental Genomics Division, CSIR-National Environmental Engineering Research Institute, Nagpur, 440020 Maharashtra India ; Present Institute: Division of Molecular Entomology, ICAR- National Bureau of Agricultural Insect Resources (NBAIR), Hebbal, Bengaluru, 560024 Karnataka India.
authors.forename: Ravi Prabhakar
authors.initials: RP
authors.lastname: More
authors.affiliation: MDS Bio-Analytics, Nagpur, 440020 Maharashtra India.
authors.forename: Rupali Chandrashekhar
authors.initials: RC
authors.lastname: Mane
authors.affiliation: Environmental Genomics Division, CSIR-National Environmental Engineering Research Institute, Nagpur, 440020 Maharashtra India.
authors.forename: Hemant J
authors.initials: HJ
authors.lastname: Purohit
keywords: MatK and rbcL loci; MatK classifier software; Plant species identification; Regular expression; Signatures