A comparison of algorithms for identification of specimens using DNA barcodes: examples from gymnosperms Damon P. Little and Dennis Wm. Stevenson Cullman

A comparison of algorithms for identification of specimens using DNA barcodes: examples

from gymnosperms

Damon P. Little and Dennis Wm. Stevenson

Cullman Program for Molecular Systematic StudiesThe New York Botanical Garden, Bronx, New York

Why is DNA barcoding useful?

Why is DNA barcoding useful?

(1) Non–specialists can identify specimens (e.g., customs inspectors, ethnobotanists).

(2) Morphologically deficient or incomplete specimens can be identified (e.g., powders).

application to conservation:

Cycadopsida:all 305 species are protected by CITES (Convention on International Trade in Endangered Species)

5 genera are appendix I

6 genera are appendix II* Cycas machonie

((GTGCTCGGGC and TCTCGCACTG) and not CGCCTCCCCT)

nrITS 2:

Encephalartos feroxLepidozamia hopei

CITES appendix ICITES appendix II

CGCCTCCCCT

selection of the barcode locus

loci used for barcoding

nuclear: rDNA: 26S, 18S, ITS 1, ITS 2

mitochondrial:COI

chloroplast:trnH-psbA, rbcL

Consortium for the Barcode Of Life (CBOL)

cpDNA: matK, rpoC1, rpoB, YCF5, accD, ndhJ

Edinburgh (UK) => Podocarpus, Araucaria, Asterella, Anastrophyllum

Instituto de Biologia UNAM (Mexico) => Agave

Kew (UK) => Conostylis, Pinus, Equisetum, Dactylorhiza

National Biodiversity Institute (South Africa) => Encephalartos, Mimetes

Natural History Museum (Denmark) => Hordeum, Scalesia, Crocus

Natural History Museum (UK) => Tortella, Ptychomniaceae, Asplenium,

New York Botanical Garden (USA) => Elaphoglossum, Cupressus, Labordia

Universidad de los Andes (Colombia) => Lauraceae

University of Cape Town (South Africa) => Anastrophyllum, Bryum

Universidade Estadual de Feira de Santana (Brazil) => Laelia, Cattleya

measuring precision and accuracy

test data sets

gymnosperm nuclear ribosomal internal transcribed spacer 2 (nrITS 2)

1,037 sequences

413 species71 genera

gymnosperm plastid encoded maturase K (matK)

522 sequences334 species75 genera

pairwise divergence

locus sequences median interquartile rangezero comparisons

nrITS 2

all 30.99% 26.53–34.48% 0.09%

one per species 29.39% 25.75–33.30% 0.21%

matK

all 20.39% 5.95–23.30% 0.54%

one per species 21.38% 8.13–23.89% 0.42%

hierarchical clustering

…alignment

locus sequencesmedian unaligned length (IQR)

aligned length

nrITS 2

all 137 (108–250) bp 8,733 bp

one per species 196 (115–260) bp 6,778 bp

matK

all 1,561 (1,412–1,661) bp 3,975 bp

one per species 1,601 (1,530–1,661) bp 3,906 bp

hierarchical clustering

reference databases:aligned with MUSCLE 3.52

query sequence:aligned to the reference database using MUSCLE (“-profile” option)

parsimony (TNT 1.0):(1) 200 iteration ratchet holding 1 tree(2) SPR holding 1 tree

neighbor joining (PHYLIP 3.63):Jukes–Cantor distance (returns 1 tree)

identification scored using “Least Inclusive Clade”

Will and Rubinoff (2004)...

identification ambiguity due to tree shape

Fitch (1971) optimization of group membership variables

Least Inclusive Clade

…clustering with nrITS 2 and matK

locus method precision accuracy to genus accuracy to species

parsimony ratchet 58% (13%) 98% (95%) 67% (46%)

nrITS 2 SPR search 60% (11%) 98% (96%) 69% (47%)

neighbor joining 65% (8%) 97% (91%) 68% (42%)

parsimony ratchet 71% (41%) 100% (99%) 77% (60%)

matK SPR search 70% (41%) 99% (98%) 78% (58%)

neighbor joining 44% (23%) 99% (97%) 75% (52%)

…clustering time (s)

method 25TH percentile 50TH percentile 75TH percentile

parsimony ratchet 841 855 864

SPR search 11 11 12

neighbor joining 110 111 112

N = 29; 3.06 GHz Intel Pentium 4; 1 GB of RAM; Ubuntu Linux 5.04 (Hoary Hedgehog)

similarity methods

similarity methods

BLASTn (version 2.2.10) BLAT (version 32)megaBLAST (version 2.2.10)

default parameters

best match(es) taken as ID

…similarity methods with nrITS 2 and matK


BLAST 94% (81%) 100% (100%)! 67% (63%)

nrITS 2 BLAT 94% (82%) 99% (99%) 66% (62%)

megaBLAST 94% (80%) 95% (95%) 72% (68%)

BLAST 99% (67%) 100% (100%)! 84% (68%)

matK BLAT 99% (69%) 99% (99%) 82% (67%)

megaBLAST 99% (61%) 100% (99%) 84% (64%)

… similarity time (s)


BLAST 1 1 2

BLAT 1 1 2

megaBLAST 0< 1 2


combination methods (cf. BOLD–ID)

combination methods (cf. BOLD–ID):

(1) get the top 100 BLAST hits

(2) align with MUSCLE

(a) 200 iteration ratchet holding 1 tree

(b) SPR holding 1 tree

(c) neighbor joining with Jukes–Cantor distances

…combination methods with nrITS 2 and matK

locus method precisionaccuracy to

genusaccuracy to

species

nrITS 2

BLAST only 94% (81%) 100% (100%) 67% (63%)

SPR only 60% (11%) 98% (96%) 69% (47%)

BLAST/parsimony ratchet 86% (74%) 99% (98%) 78% (67%)

BLAST/SPR 87% (73%) 100% (99%) 79% (67%)

BLAST/neighbor joining 93% (71%) 99% (97%) 80% (64%)

matK

BLAST only 99% (67%) 100% (100%) 84% (68%)

SPR only 60% (11%) 98% (96%) 69% (47%)

BLAST/parsimony ratchet 77% (55%) 100% (99%) 80% (60%)

BLAST/SPR 76% (53%) 100% (99%) 78% (61%)

BLAST/neighbor joining 95% (56%) 100% (99%) 86% (56%)

…combination time (s)


BLAST only 1 1 2

SPR only 11 11 12

BLAST/parsimony ratchet 186 219 278

BLAST/SPR search 170 196 262

BLAST/neighbor joining 171 198 264


diagnostic methods

DNA–BAR (DasGupta et al 2005):

each sequence and its reverse complement (separated by 50 ``N'' symbols)

presence/absence matrix of “distinguishers” up to 50 bp long

degenbar

DNA–BAR (DasGupta et al 2005):

matrix of distinguishers

query + PERL script

ID = the reference sequence(s) with the greatest number of matching presence/absence scores

C. arizonica 1 matches = 582C. arizonica 2 matches = 582C. lusitanica 1 matches = 582

DNA–BAR... distinguisher matrix

locus sequences

distinguishers

unidentifiable sequencestotal unique

nrITS 2

all 1,997 49 5%

one per species 813 27 5%

matK

all 808 8 14%

one per species 582 10 14%

diagnostic methods: DOME ID

reference database (via PERL and MySQL): (1) all sequence strings of 10 nucleotides offset by 5 nucleotides were extracted from the reference sequences(2) each string was classified as diagnostic (unique to a particular species) or non–diagnostic(3) diagnostic strings were inserted into the diagnostic barcode

database

GCGTTGATGG GTTGGGCGTT CATACGTTGG GTCACCATAC CCTTTGTTTG AGGGACCTTT CTGAGCATCG GTGCACTGAG TTCTCGATGC GGCGTTTCTC TAGCTGGCGT AGGTCTAGCT GGCTGAGGTC GCTTGCATCG CCCTAGCTTG AATGTGCGCA GATGCAATGT TAGCCGGCGT CTGTCTAGCC GCCTTGCCCC ATGCCCCCTG ATCGTGGTGC CCCTGCAAGT AGTGTGCGCA TAGACGACGT CTGTCTAGAC GACTTGCCCC CTTGCGGATC CGGCCTGACT ACCCCCGGCC CGTGAACCCC CTGCCTGACT CCCCCCTGCC TGGGCCGTCA CGCGATGGGC ATACGCGCGA GCCCTTTGAG TGCGGTGGGA CAAGTGAGGA TCGGGCAAGT TAAAATCGTC CAAACCCGTC GTGCATGTGC CGTGCGTGCA CTTCCCACGA CCGTCCCGCA GCATTTGCGG CTCGGGGAGC AAGACCCGTC GCGGCAAGAC GTGCGTGCGT TGCAGAGGGG TTCTCACGAA AGGTTCTCCC GTGCCAGGTT TGCGTCCCGC TTGTTTGCGT TTTCATTGTT GGCGGCATGA TCCCCTGCCC CTTGCTTTTT GGCGGCTTGC CGGCGGGCGG CGGCACGGCG CTTTACGGCA AGACTCCGCG GATCGAGACT CAAGTGATCG GGTGTCAAGT GGTGGCCCCC GGCTCATCAT TGAAACGTGC CCCAAGACGG CGTGCCCCAA AGGACCGGGA TGGGGGTGGG CCGCGTGGGG GACCTCCATT AAACCGACCT AAAGAAAAGA TCCAAGAAAA GCCTGTTTTC GGTCAGCCTG CATGCGTGCG TCAAGGATCC CGGTTTCAAG CGACGCGGTT GTGCTCGGAA GGGATGTGCT CTACGGTCGA GTCGCCTACG ATAGTCTTCA CGGCGATAGT TGTTTTCATG GATGGTGTTT GTCCCTATCA ATTAAAATAC CGATCCGAGT GCGGGTGAGA TCCCCCCCAA AGGATGACGA GCAAAAGGAT ACATGATTCG AATACAACTC CGCAAGCGGC GGCGTGGAAT TCAGCGTTGG ACGGGTCAGC GATAGTCCGT GATCCGATAG GCATTGGGGG GATATTTGAT TAGCCCAAAA TCGCCTAGCC GCCCTTCGGC CATGCGCCCT CTACTCTTTC AACGTCTACT CACGCGAGAG CGCGTCACGC CGCGTATCTT AGCGTGCATC GGGGGAGCGT GCTACGGGGG CGAGGCGTCC GGAACCGAGG TTTCACGGGT GCCGATCCGG AATGCGCCGA GTACTCGCGA TGGCAAGGAT GCCGGTACCG CAACGGCCGG AAGCGGGCAG GCAGCAAGCG CGAGACGATG GACGACGAGA AGACCCGGGA CGAGCCTTCA CGGATGAGAA TTGCGCGGAT CTCCATAGGT TTCCCCCAAG AATCGTTCCC CGCCTCGATG CCGAGCCTCG TTCAAGAATC GTGAATTCAA AAAATTCACG TCGTCCGCCG GCGACCCAGC GAAGCGCGAC ACGGGTGCCG CGTGTAATGT AACGACGTGT AGTAAAGGTC GCTCAAGTAA GACGTGCTCA TGCTGGACGT TAGATGGCTG GGCGGTATGT CCGATGCGAT ATCCCCCGAT TCCTGTCCTC GAGACTCCAA ACCGGCGTTG CAAAGACCGG ACTGAAATGA AGGGCTCGGC ATATCGTCGG CAGGAATCCC AATTGCAGGA CCAACGATGA ACATCCCAAC TGTCAACATC CCTCTCCCGT GGTTGGACGG TTGATGGTTG GGGGATTGAT AATCTAGTTG AGGGGAATCT CTCTTTCCAA CGCCTCTCTT CTGTGCGCCT TCGACCTGTG CTTTCTCGAC CGCTACTTTC AGCGCCGCTA ATCTCAGCGC TGGGTATCTC CTCGTTGGGT TCGCGCTCGT GTGTGTCGCG CTTGACGTCC AAAGCCTCGT CTTCGAAAGC CCGATGCGCT TCTCGCCGAT CCCTGTCTCG GTTGGAGGGT TGATCGTTGG TTGATTGATC GGTGATTGAT TCGTGGGTGA TCTTCTCGTG GCTATTCTTC GACGGGCTAT TAGCTGACGG CTGGATAGCT CAGCACTGGA GGCTTCAGCA TCGCGGGCTT GTGATTGCTG CCGCCGTGAT CTGCCCCGCC CTTCTCTGCC CCTGACTTCT CGTTGCCTGA GCTGCCGTTG TGCTGGCTGC TCCAGTGCTG GGCTATCCAG CCGTGGGCTA GCGCCCCGTG CTGTTGCGCC CGAGGCTGTT CTTTACGCCT GCGCCCTTTA GAAAGGGCTT GATCGGAAAG TGTTGCATGT GGTCCTGTTG TTGTCGGTCC CATGGTTGTC


reference database (via PERL and MySQL): (1) all sequence strings of 10 nucleotides offset by 5 nucleotides were extracted from the reference sequences(2) each string was classified as diagnostic (unique to a particular species) or non–diagnostic(3) diagnostic strings were inserted into the diagnostic barcode

database

diagnostic barcode database


query + MySQL + PERL script

ID = the reference sequence(s) with the greatest number of matching presence/absence scores

C. arizonica matches = 43

diagnostic barcode database

diagnostic methods: ATIM

presence/absence matrix of all possible of 10 bp combinations [1,048,576 motifs]

PERL script


1,048,576 character presence/absence matrix

TNT (parsimony ratchet)

reference tree (strict consensus)


query+

1,048,576 character presence/absence matrix+

reference tree (positive constraint)

TNT (TBR hold 20)

identification scored using “Least Inclusive Clade”

…diagnostic methods with nrITS 2 and matK


nrITS 2

DNA–BAR 98% (89%)! 86% (86%) 65% (62%)

DOME ID 80% (80%) 86% (84%) 67% (66%)

ATIM 100% (83%) 99% (98%) 83% (71%)!

matK

DNA–BAR 100% (79%)! 96% (96%) 73% (62%)

DOME ID 60% (60%) 53% (53%) 50% (50%)

ATIM 100 (67%) 98% (97%) 87% (53%)

…diagnostic time (s)


DNA–BAR 1 1 1

DOME ID 16 16 22

ATIM 132 133 134


DAWG I “training” dataset

…the DAWG I “training” dataset

method precision accuracy to species

SPR 71% (41%) 86% (81%)

SPR 60–70% (11–41%) 69–78% (47–58%)

BLAST 100% (78%) 83% (83%)

BLAST 94–99% (67–81%) 67–84% (63 –68%)

DNA–BAR 97% (90%) 43% (42%)

DNA–BAR 98–100% (79–89%) 65–73% (62%)

ATIM 100% (72%) 75% (69%)

ATIM 100% (67–83%) 83–87% (53–71%)

conclusions:

all methods are relatively precise => expect accuracy to approximate precision

observed accuracy of species level identification is lower=> failure of the algorithms to correspond to species delimitations (shared haplotypes or haplotypes of a species are more similar to those of different species)=> for accurate identification, the reference database must contain virtually all haplotypes

none of the methods performed particularly well=> computer time

=> BLAST (BLAT and megaBLAST too)=> DNA–BAR

acknowledgments

brilliant insights &tc:

K. Cameron

C. Chaboo

T. Dikow

C. Martin

R. Meier

M. Mundry

money:

Cullman Program for Molecular Systematic Studies

DIMACS/NSF

Documents

A comparison of algorithms for identification of specimens using DNA barcodes: examples from gymnosperms Damon P. Little and Dennis Wm. Stevenson Cullman