| Literature DB >> 25526752 |
Wenpan Dong1, Han Liu2, Chao Xu3, Yunjuan Zuo4, Zhongjian Chen5, Shiliang Zhou6.
Abstract
BACKGROUND: Universal conventional DNA barcodes will become more and more popular in biological material identifications. However, in many cases such as processed medicines or canned food, the universal conventional barcodes are unnecessary and/or inapplicable due to DNA degradation. DNA mini-barcode is a solution for such specific purposes. Here we exemplify how to develop the best mini-barcodes for specific taxa using the ginseng genus (Panax) as an example.Entities:
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Year: 2014 PMID: 25526752 PMCID: PMC4293818 DOI: 10.1186/s12863-014-0138-z
Source DB: PubMed Journal: BMC Genet ISSN: 1471-2156 Impact factor: 2.797
Figure 1Representative map of the chloroplast genome of The annotation of the genome was performed using DOGMA. The genes that are drawn outside of the circle are transcribed clockwise, while those inside are counterclockwise. Small single copy (SSC), large single copy (LSC), and inverted repeats (IRa, IRb) are indicated.
Figure 2The patterns of nucleotide substitutions among the two chloroplast genomes. The patterns were divided into 6 types as indicated by the six non-strand-specific base-substitution types (i.e., numbers of considered G to A and C to T sites for each respective set of associated mutation types). The chloroplast genome of P. notoginseng was used as a standard.
Figure 3Sliding window plots of nucleotide diversity (π) across the complete chloroplast genome of the two species (window length: 600 bp, step size: 25 bp). Y-axes: nucleotide diversity (π) of each window; X-axes: position of the midpoint of a window.
Variability of the three new markers and universal chloroplast DNA barcode in
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| 637 | 15 | 2.35 | 15 | 2.35 | 0.0066 | 0 | 13 |
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| 818 | 30 | 3.67 | 30 | 3.67 | 0.0082 | 0 | 6 |
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| 476 | 23 | 4.83 | 31 | 6.51 | 0.0140 | 6 | 11 |
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| 848 | 23 | 2.71 | 26 | 3.07 | 0.0060 | 6 | 9 |
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| 1094 | 116 | 10.60 | 113 | 10.33 | 0.0284 | 2 | 12 |
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| 1186 | 69 | 5.81 | 74 | 6.24 | 0.0167 | 2 | 15 |
The shortest length for a candidate barcode to reach the maximum discrimination success using genetic distance method
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| 91.67 | 480 | m-rbcLF | ACAAATTGACTTATTATACTCCTGA |
| m-rbcLR | TCGTCTTTGGTAAAATCAAGTCCA | |||
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| 62.5 | 90 | m-matKF | CTTCTTGAACGAATCTATTTCTA |
| m-matKR | CCATAAATTAACAAAGTAATATGT | |||
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| 62.5 | 50 | m-HAF | TAATCTAGAATTTAGCTACTTCTTC |
| m-HAR | CCTTGATCCACTTGGCTACATCC | |||
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| 83.33 | 280 | m-rps16F | ATAGGAATGAAGGTGCTCTTG |
| m-rps16R | ATCCTTCCAACAAAATGGCAGCA | |||
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| 91.67 | 60 | m-ycf1aF | TTATTACCGAGTTGGAACAACA |
| m-ycf1aR | TTGAGTACGCATAGAACCTTTGAT | |||
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| 100 | 110 | m-ycf1bF | AAKCAAGAGACAACTTACCTTGA |
| m-ycf1bR | GGATCAGATGCACAAAACCAAGGAA |
Figure 4Genetic distance-based discrimination power changes along with the increase of sequence lengths. Pm: maximum percentage of samples discriminated.