DNA Barcode sequence identification incorporating taxonomic hierarchy and within taxon variability

1. DNA Barcode sequence identification incorporating taxonomic hierarchy and within taxon variability Damon P. Little Cullman Program for Molecular Systematics Studies The New York Botanical Garden, Bronx, New York

3. test data sets (Little and Stevenson 2007) • gymnosperm nuclear ribosomal internal transcribed spacer 2 (nrITS 2) • 1,037 sequences • 413 species • 71 genera • gymnosperm plastid encoded maturase K (matK) • 522 sequences • 334 species • 75 genera

4. …alignment

5. pairwise divergence

6. measuring precision and accuracy

10. precision

15. accuracy to species

16. lessons learned

17. “global” alignments do not work

18. precision

19. accuracy to species

20. “fuzzy” matches are not precise

21. precision

22. accuracy to species

23. autoapomorphies (unique characters) work... but not always present

24. precision

25. accuracy to species

26. some sequences are simply unidentifiable

27. ...remaining (insoluble) problems • identical sequences for multiple terminals • shared alleles between terminals • use allele frequency as a predictor?

28. desirable methodologies and properties of Sequence IDentification Engines (SIDEs)

29. Sequence IDentification Engines (SIDEs) • avoid global alignment by comparing short segments: pseudo–alignment • use exact matches • use autoapomorphies where possible • ...but allow the use of other characters too

30. context/text DNA recoding • characters are defined by flanking context • => pretext and postext • permit “alignment–free” comparisons • size and separation between pretext and postext must be arbitrarily delimited • states (text) limited by the proximity of context • terminals can be individual sequences or composites representing taxa

31. context/text DNA recoding

32. context/text DNA recoding • characters are defined by flanking context • => pretext and postext • permit “alignment–free” comparisons • size and separation between pretext and postext is arbitrarily • possible states (text) is limited by the length of the text • terminals can be individual sequences or composites representing taxa

33. querying text/context database • find pretext/text/postext in the query sequence and match to references

34. querying text/context database

35. querying text/context database • find pretext/text/postext in the query sequence and match to references • score terminals based on the number of matches • final score can be raw or based a weighting function

36. possible weighting functions • equal weights (raw score) • number of distinct texts • => up weights more variable characters • 1/(number of distinct texts) • => down weights more variable characters • (number of texts)/(number of scores)

37. precision

38. accuracy to species

39. BRONX conclusions • BRONX is more precise than existing algorithms • BRONX is sometimes more accurate than existing algorithms • BRONX is an incremental improvement

40. future directions • improve the scoring function in BRONX • dynamically size context/text • benchmark additional datasets for all methods • incorporate context/text recoding into a scalable version of the ATIM algorithm

41. acknowledgments • Kenneth Cameron • Santiago Madriñán • Christian Schulz • Dennis Stevenson