Analyzing multi-locus plant barcoding datasets with a composition vector method based on adjustable weighted distance

Phylogenomics of tomato chloroplasts using assembly and alignment-free method

Phylogenetics and population genetics are central disciplines in evolutionary biology. Both are based on the comparison of single DNA sequences, or a concatenation of a number of these. However, with the advent of next-generation DNA sequencing technologies, the approaches that consider large genomic data sets are of growing importance for the elucidation of evolutionary relationships among species. Among these approaches, the assembly and alignment-free methods which allow an efficient distance computation and phylogeny reconstruction are of great importance. However, it is not yet clear under what quality conditions and abundance of genomic data such methods are able to infer phylogenies accurately. In the present study we assess the method originally proposed by Fan et al. for whole genome data, in the elucidation of Tomatoes' chloroplast phylogenetics using short read sequences. We find that this assembly and alignment-free method is capable of reproducing previous results under conditions of high coverage, given that low frequency k-mers (i.e. error prone data) are effectively filtered out. Finally, we present a complete chloroplast phylogeny for the best data quality candidates of the recently published 360 tomato genomes.

VIP Barcoding: composition vector-based software for rapid species identification based on DNA barcoding

Species identification based on short sequences of DNA markers, that is, DNA barcoding, has emerged as an integral part of modern taxonomy. However, software for the analysis of large and multilocus barcoding data sets is scarce. The Basic Local Alignment Search Tool (BLAST) is currently the fastest tool capable of handling large databases (e.g. >5000 sequences), but its accuracy is a concern and has been criticized for its local optimization. However, current more accurate software requires sequence alignment or complex calculations, which are time-consuming when dealing with large data sets during data preprocessing or during the search stage. Therefore, it is imperative to develop a practical program for both accurate and scalable species identification for DNA barcoding. In this context, we present VIP Barcoding: a user-friendly software in graphical user interface for rapid DNA barcoding. It adopts a hybrid, two-stage algorithm. First, an alignment-free composition vector (CV) method is utilized to reduce searching space by screening a reference database. The alignment-based K2P distance nearest-neighbour method is then employed to analyse the smaller data set generated in the first stage. In comparison with other software, we demonstrate that VIP Barcoding has (i) higher accuracy than Blastn and several alignment-free methods and (ii) higher scalability than alignment-based distance methods and character-based methods. These results suggest that this platform is able to deal with both large-scale and multilocus barcoding data with accuracy and can contribute to DNA barcoding for modern taxonomy. VIP Barcoding is free and available at http://msl.sls.cuhk.edu.hk/vipbarcoding/.

Species discrimination in Sisyrinchium (Iridaceae): assessment of DNA barcodes in a taxonomically challenging genus

DNA barcoding aims to develop an efficient tool for species identification based on short and standardized DNA sequences. In this study, the DNA barcode paradigm was tested among the genera of the tribe Sisyrinchieae (Iridoideae). Sisyrinchium, with more than 77% of the species richness in the tribe, is a taxonomically complex genus. A total of 185 samples belonging to 98 species of Sisyrinchium, Olsynium, Orthrosanthus and Solenomelus were tested using matK, trnH-psbA and internal transcribed spacer (ITS). Candidate DNA barcodes were analysed either as single markers or in combination. Detection of a barcoding gap, similarity-based methods and tree-based analyses were used to assess the discrimination efficiency of DNA barcodes. The levels of species identification obtained from plastid barcodes were low and ranged from 17.35% to 20.41% for matK and 5.11% to 7.14% for trnH-psbA. The ITS provided better results with 30.61-38.78% of species identified. The analyses of the combined data sets did not result in a significant improvement in the discrimination rate. Among the tree-based methods, the best taxonomic resolution was obtained with Bayesian inference, particularly when the three data sets were combined. The study illustrates the difficulties for DNA barcoding to identify species in evolutionary complex lineages. Plastid markers are not recommended for barcoding Sisyrinchium due to the low discrimination power observed. ITS gave better results and may be used as a starting point for species identification.

DNA barcoding in plants: evolution and applications of in silico approaches and resources

Bioinformatics has played an important role in the analysis of DNA barcoding data. The process of DNA barcoding initially involves the available data collection from the existing databases. Many databases have been developed in recent years, e.g. MMDBD [Medicinal Materials DNA Barcode Database], BioBarcode, etc. In case of non-availability of sequences, sequencing has to be done in vitro for which a recently developed software ecoPrimers can be helpful. This is followed by multiple sequence alignment. Further, basic sequence statistics computation and phylogenetic analysis can be performed by MEGA and PHYLIP/PAUP tools respectively. Some of the recent tools for in silico and statistical analysis specifically designed for barcoding viz. CAOS (Character Based DNA Barcoding), BRONX (DNA Barcode Sequence Identification Incorporating Taxonomic Hierarchy and within Taxon Variability), Spider (Analysis of species identity and evolution, particularly DNA barcoding), jMOTU and Taxonerator (Turning DNA Barcode Sequences into Annotated OTUs), OTUbase (Analysis of OTU data and taxonomic data), SAP (Statistical Assignment Package), etc. have been discussed and analysed in this review. The paper presents a comprehensive overview of the various in silico methods, tools, softwares and databases used for DNA barcoding of plants.