Source:Little DP. DNA barcode sequence identification incorporating taxonomic hierarchy and within taxon variability. PLoS One. 2011;6(8):e20552.

Raunak Shrestha

13th Oct. 2011

What is DNA Barcoding?

Barcoding is a standardized approach to identifying plants and animals by minimal sequences of DNA, called DNA barcodes.

DNA Barcode: A short DNA sequence, from a uniform locality on the genome, used for identifying species.

C A T G

DNA Barcoding developments

2003

DNA Barcoding developments (cont….)

2005

2007

DNA Barcoding developments (cont….)

2008

2009• MULTI-LOCUS GENE APPROACH FOR PLANT DNA BARCODING

• Chloroplast genes matK + rbcL recommended as the barcode regions

COI 1560 bp

BARCODE 648 bp

MINI-COI (186 bp)

Problems with conventional Sequence Identification Engines (SIDEs)

Source: Dr. F. Brinkman. Lecture slide-4 MBB741, 2011

SIDEs such as BLAST does not

consider Taxonomic

Hierarchy Information

Blastp Results

• Even a difference of single nucleotide can have significant impact on DNA Barcoding interpretation

• SIDEs such as BLAST and FASTA “corrects” it to overcome the sampling biasness.

• For closely related species, SIDEs such as BLAST and FASTA usually cannot diagnose such organism as separate species or of different taxon hierarchy

Problems with conventional Sequence Identification Engines (SIDEs) (cont….)

Character based Identification

Problems with conventional Sequence Identification Engines (SIDEs) (cont….)

• In a huge dataset using Parsimonous tree building method can generate large number of possible solution for even a small number of terminals

• “Computationally Expensive”

• Character-based phylogenetic methods requires multiple-sequence alignment (MSA).

• Several MSA tools may not be able to efficiently align the barcode sequences• Barcode sequence:

• Inter Species Variation > Intra-Species Variation • Conserved enough so that it could be amplified with ‘universal PCR

primers’ .

Phylogenetic Method based Identification

BRONX algorithm• BRONX (Barcode Recognition Obtained with Nucleotide

eXpose´s)• use an uncorrected character–based measure of similarity,• work with difficult to align markers, • capitalize upon knowledge of hierarchic evolutionary

relationships, • indicate ambiguous classification assignments, and• account for within taxon variation.

BRONX algorithm (cont…)• Reduces the reference sequences into a series of characters

defined by flanking context (‘pretext’ and ‘postext’)

The size of the pretext/postext used, and the range of text sizes stored, may vary by implementation.

BRONX algorithm (cont…)• Uses exhaustive tree construction algorithm• Then it starts comparing the sequences of each terminal

• Match the pretext and the postext of the paired sequences• If there is a pretext match as well as postext match

• Score for each combination shared with the paired sequences• If no match

• Determine all possible postext combination downstream of the matched pretext

• Choose the nearest postext match to the postext and align sequences accordingly

• Choose next postext and align the sequence• Score all the all alignment• The alignment with the highest final score is(are) considered

identification

Objective of the paper

To test the accuracy of BRONX sequence identification against leading published

SIDEs.

Dataset

• DNA Barcode sequence of matK and rbcL from databases• Sequences chosen only if both the sequences of matK and

rbcL were obtained from same individual (voucher specimen)• Global multiple sequence alignment• Alignment refined with MUSCLE• Sequence trimmed to be amplified with the following PCR

primers• matK 3F (5’-CGTACAGTACTTTTGTGTTTACGAG-3’) • matK 1R (5’-ACCCAGTCCATCTGGAAATCTTGGTTC-3’)

• rbcL aF (5’-ATGTCACCACAAACAGAGACTAAAGC-3)• rbcL aR (5’-GAAACGGTCTCTCCAACGCAT-3’)

• Final dataset: 2083 sequences of each marker representing 990 genera and 1745 species

Dataset

• Mini-barcodes:• Each of 2083 sequences were reduced to 100-200 base

sequences as the mini-barcodes.• Position of the barcodes were randomly chosen

Benchmarking• Benchmark of 11 different algorithms for both DNA barcodes

and mini-barcodes1. B = BRONX;2. C = CAOS; 3. D = DNA–BAR/degenbar;4. F = forced (constrained) tree–search; 5. J = SAP neighbor joining; 6. L = pairwise matching (local alignment); 7. N = NCBI-BLAST; 8. P = pairwise matching (global alignment); 9. S = SAP Barcoder; 10. T = de novo tree–search; 11. W = WU-BLAST.

Results

Genus-level identification

Weak test of species-level identification

Strong test of species-level identification

All test of species-level identification

Tests of identification using full–length barcode queries.

Results• Genus level identification highly successful (>99%) for BRONX,

DNA-BAR/degenbar, NCBI-BLAST and pairwise matching using full-length matK data

• rcbL not variable enough to distinguish between genera (~97% success)

• DNA-BAR/degenbar outperformed all other SIDEs in species-level identification • but BRONX too was significantly better in genus-level

identification

• BRONX should be preferred for genus-level identification queries over other SIDEs.

Results Tests of identification using mini-barcode queries.

Genus-level identification

Weak test of species-level identification

Strong test of species-level identification

All test of species-level identification

Results• For mini-barcode queries, identification success was relatively

lower than that of full-length queries

Identification success for strong test with combined matK and rbcL

Full-length query (DNA-BAR/degenbar)

Mini-barcode query (BRONX)

91 % 47 %

• Performance of DNA-BAR/degenbar was similar to other SIDEs for mini-barcode queries (11.24% success)

• Performance of BRONX for mini-barcode queries were better than all other SIDEs

• Moderate agreement among SIDEs for full-length queries (k=0.487-0.633)

• Little agreement among SIDEs for mini-barcode queries (k =0.191-0.137)

• Identification success did not improve with combined data of matk and rbcL.

Similarity of SIDE performance measured by Fleiss' index of interrater agreement (k)

Results

Conclusion• BRONX to be preferred over other SIDEs when

• Identification of genus are desired• Mini-barcode is used for identification

• DNA-BAR/degenbar exhibit superior performance in species level identification with full-length queries

• Due to inconstant performance no tree-based method should be used for barcode sequence identification

• BLAST is rapid means of sequence identification but other SIDEs provide better accuracy and consistency

Critique• Quality of sequence data in public database -> GIGO

• DNA barcode data depends upon the primer selected to amplify sequence• Use of only a single primer set of each locus• Does this mimic the real world dataset ?

• It would have been even better if the performance was measured in terms of computing time required for analysis.

• It seems that, till date, no algorithm is available which can incorporate both full-length query sequence as well as mini-barcode sequence query and give higher identification success at both genus and species level identification.

Questions ?

Thank you