The Practical Evaluation of DNA Barcode Efficacy
This chapter describes a workflow for measuring the efficacy of a barcode in identifying species. First, assemble individual sequence databases corresponding to each barcode marker. A controlled collection of taxonomic data is preferable to GenBank data, because GenBank data can be problematic, particularly when comparing barcodes based on more than one marker. To ensure proper controls when evaluating species identification, specimens not having a sequence in every marker database should be discarded. Second, select a computer algorithm for assigning species to barcode sequences. No algorithm has yet improved notably on assigning a specimen to the species of its nearest neighbor within a barcode database. Because global sequence alignments (e.g., with the Needleman–Wunsch algorithm, or some related algorithm) examine entire barcode sequences, they generally produce better species assignments than local sequence alignments (e.g., with BLAST). No neighboring method (e.g., global sequence similarity, global sequence distance, or evolutionary distance based on a global alignment) has yet shown a notable superiority in identifying species. Finally, “the probability of correct identification” (PCI) provides an appropriate measurement of barcode efficacy. The overall PCI for a data set is the average of the species PCIs, taken over all species in the data set. This chapter states explicitly how to calculate PCI, how to estimate its statistical sampling error, and how to use data on PCR failure to set limits on how much improvements in PCR technology can improve species identification.
Key wordsBarcode efficacy in species identification Probability of correct identification DNA barcode
This research was supported in part by the Intramural Research Program of the NIH, NLM, NCBI.
- 17.Erickson DL, Spouge JL, Resch A et al (2008) DNA barcoding in land plants: developing standards to quantify and maximize success. Taxon 13:1304–1316Google Scholar
- 19.Austerlitz F (2007) Comparing phylogenetic and statistical classification methods for DNA barcoding. Paper presented at the second international barcode of life conference, Taipei, Taiwan, 2007Google Scholar
- 22.Altschul S (1999) Hot papers – bioinformatics – gapped blast and psi-blast: a new generation of protein database search programs by s.F. Altschul, t.L. Madden, a.A. Schaffer, j.H. Zhang, z. Zhang, w. Miller, d.J. Lipman – comments. Scientist 13:15Google Scholar
- 38.Jukes TH, Cantor CR (1969) Evolution of protein molecules. In: Munro HN (ed) Mammalian protein metabolism. Academic, New York, pp 21–123Google Scholar