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Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood

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Abstract

Main conclusion

Machine-learning approaches (MLAs) for DNA barcoding outperform distance- and tree-based methods on identification accuracy and cost-effectiveness to arrive at species-level identification of wood.

DNA barcoding is a promising tool to combat illegal logging and associated trade, and the development of reliable and efficient analytical methods is essential for its extensive application in the trade of wood and in the forensics of natural materials more broadly. In this study, 120 DNA sequences of four barcodes (ITS2, matK, ndhF-rpl32, and rbcL) generated in our previous study and 85 downloaded from National Center for Biotechnology Information (NCBI) were collected to establish a reference data set for six commercial Pterocarpus woods. MLAs (BLOG, BP-neural network, SMO and J48) were compared with distance- (TaxonDNA) and tree-based (NJ tree) methods based on identification accuracy and cost-effectiveness across these six species, and also were applied to discriminate the CITES-listed species Pterocarpus santalinus from its anatomically similar species P. tinctorius for forensic identification. MLAs provided higher identification accuracy (30.8–100%) than distance- (15.1–97.4%) and tree-based methods (11.1–87.5%), with SMO performing the best among the machine learning classifiers. The two-locus combination ITS2 + matK when using SMO classifier exhibited the highest resolution (100%) with the fewest barcodes for discriminating the six Pterocarpus species. The CITES-listed species P. santalinus was discriminated successfully from P. tinctorius using MLAs with a single barcode, ndhF-rpl32. This study shows that MLAs provided higher identification accuracy and cost-effectiveness for forensic application over other analytical methods in DNA barcoding of Pterocarpus wood.

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Abbreviations

BLOG:

Barcoding with logic

CITES:

Convention on International Trade in Endangered Species of Wild Fauna and Flora

MLAs:

Machine learning approaches

NCBI:

National Center for Biotechnology Information

NJ:

Neighbor Joining

SMO:

Sequential Minimal Optimization

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Acknowledgements

This work was financially supported by National Natural Science Foundation of China (Grant No. 31600451), the National High-level Talent for Special Support Program of China (Grant No. W02020331), and the China Scholarship Council (Grant No. 2017-3109). We express our gratitude to Professor Xiaomei Jiang, Dr. Min Yu, Dr. Bo Liu and Dr. Prabu Ravindran for their assistance and suggestions on this study. We thank Sarah Friedrich for her help with the figure works.

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Authors and Affiliations

  1. Department of Wood Anatomy and Utilization, Chinese Research Institute of Wood Industry, Chinese Academy of Forestry, Beijing, 100091, China

    Tuo He, Lichao Jiao & Yafang Yin

  2. Wood Collections (WOODPEDIA), Chinese Academy of Forestry, Beijing, 100091, China

    Tuo He, Lichao Jiao & Yafang Yin

  3. Forest Products Laboratory, Center for Wood Anatomy Research, USDA Forest Service, Madison, WI, 53726, USA

    Tuo He & Alex C. Wiedenhoeft

  4. Department of Botany, University of Wisconsin, Madison, WI, 53706, USA

    Tuo He & Alex C. Wiedenhoeft

  5. Department of Forestry and National Resources, Purdue University, West Lafayette, IN, 47907, USA

    Alex C. Wiedenhoeft

  6. Ciências Biológicas (Botânica), Univesidade Estadual Paulista, Botucatu, São Paulo, Brazil

    Alex C. Wiedenhoeft

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  1. Tuo He
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Fig. S1

The confusion matrix generated by the BP-neural network for all the single barcodes and their combinations (PDF 42 kb)

Fig. S2

The criterions for assessing the SMO classifier based on the four single barcodes and their combinations (PDF 65 kb)

Fig. S3

The decision trees constructed by the diagnostic position of DNA sequences based on the four barcodes and their combinations (PDF 323 kb)

Fig. S4

Identification success rates of four barcodes and their combinations based on “best match” and “best close match” functions of TaxonDNA (PDF 19 kb)

Fig. S5

Phylogenetic trees generated from the four barcodes and their combinations based on neighbor-joining analysis (PDF 367 kb)

Table S1

DNA sequences generated from our previous study (Jiao et al. 2018) and downloaded from the NCBI GenBank (XLSX 18 kb)

Table S2

The formulae generated by BLOG for discrimination of six Pterocarpus timber species based on the four barcodes and their combinations (XLSX 16 kb)

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He, T., Jiao, L., Wiedenhoeft, A.C. et al. Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood. Planta 249, 1617–1625 (2019). https://doi.org/10.1007/s00425-019-03116-3

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