DNA条形码参考数据集构建和序列分析相关的新兴技术

doi:10.17520/biods.2018209

摘要/Abstract

摘要：

近年来DNA条形码技术迅速发展, 产生的条形码的数量及其应用范围都呈指数性增长, 现已广泛用于物种鉴定、食性分析、生物多样性评估等方面。本文重点总结并讨论了构建条形码参考数据库和序列聚类相关的信息分析的技术和方法, 包括: 基于高通量测序(high throughput sequencing, HTS)平台以高效并较低的成本获取条形码序列的方法; 同时还介绍了从原始测序序列到分类操作单元(operational taxonomic units, OTUs)过程中的一些计算逻辑以及被广泛采用的软件和技术。这是一个较新并快速发展的领域, 我们希望本文能为读者提供一个梗概, 了解DNA条形码技术在生物多样性研究应用中的方法和手段。

关键词: DNA条形码, 可操作物种单元, 聚类, 宏基因条形码, 高通量测序

Abstract

DNA barcoding has been growing exponentially in terms of the number of barcode generated as well as its applications, e.g. as conservation tools in: species identification for damaged specimens, diet analysis from gut content and feces, biodiversity assessment from environmental DNA (eDNA), bulk arthropod samples or invertebrate-derived DNA (iDNA). These applications often require coupling with high throughput sequencing (HTS) technologies, and when done so are referred to as metabarcoding. Here, we discuss the methods used to generate reference barcodes using cost-efficient HTS platforms, and introduce several rules-of-thumb and some widely-used tools to conduct data quality control, denoising, and Operational Taxonomic Units (OTUs) clustering. We hope this review will help readers better understand how these emerging technologies can be implemented alongside existing technologies to accelerate biodiversity assessments in an accurate and efficient way.

Key words: DNA barcoding, OTUs, clustering, metabarcoding, high throughput sequencing

刘山林 (2019) DNA条形码参考数据集构建和序列分析相关的新兴技术. 生物多样性, 27, 526-533. DOI: 10.17520/biods.2018209.

Liu Shanlin (2019) DNA barcoding and emerging reference construction and data analysis technologies. Biodiversity Science, 27, 526-533. DOI: 10.17520/biods.2018209.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2018209

https://www.biodiversity-science.net/CN/Y2019/V27/I5/526

图/表 3

参考文献 57

[1]	Armstrong K, Ball S ( 2005) DNA barcodes for biosecurity: Invasive species identification. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 360, 1813-1823. DOI URL
[2]	Baird DJ, Hajibabaei M ( 2012) Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Molecular Ecology, 21, 2039-2044. DOI URL
[3]	Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW ( 2012) GenBank. Nucleic Acids Research, 41, D36-D42. DOI URL
[4]	Bohmann K, Evans A, Gilbert MTP, Carvalho GR, Creer S, Knapp M, Douglas WY, De Bruyn M ( 2014) Environmental DNA for wildlife biology and biodiversity monitoring. Trends in Ecology & Evolution, 29, 358-367.
[5]	Bohmann K, Schnell IB, Gilbert MTP ( 2013) When bugs reveal biodiversity. Molecular Ecology, 22, 909-911. DOI URL
[6]	Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP ( 2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13, 581-583. DOI
[7]	Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI ( 2010) QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335-336. DOI
[8]	Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R ( 2011) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences, USA, 108, 4516-4522. DOI URL
[9]	Chen Y, Chen Y, Shi C, Huang Z, Zhang Y, Li S, Li Y, Ye J, Yu C, Li Z ( 2017) SOAPnuke: A MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. GigaScience, 7, gix120.
[10]	Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, Kulam-Syed-Mohideen A, McGarrell DM, Marsh T, Garrity GM ( 2008) The Ribosomal Database Project: Improved alignments and new tools for rRNA analysis. Nucleic Acids Research, 37, D141-D145.
[11]	Cruaud P, Rasplus J-Y, Rodriguez LJ, Cruaud A ( 2017) High-throughput sequencing of multiple amplicons for barcoding and integrative taxonomy. Scientific Reports, 7, 41948. DOI
[12]	Deiner K, Bik HM, Mächler E, Seymour M, Lacoursière-Roussel A, Altermatt F, Creer S, Bista I, Lodge DM, Vere N ( 2017) Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Molecular Ecology, 26, 5872-5895. DOI URL
[13]	DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL ( 2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and Environmental Microbiology 72, 5069-5072.
[14]	Edgar RC ( 2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460-2461. DOI URL
[15]	Edgar RC ( 2013) UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10, 996-998. DOI
[16]	Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R ( 2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27, 2194-2200. DOI URL
[17]	Eid J, Fehr A, Gray J, Luong K, Lyle J, Otto G, Peluso P, Rank D, Baybayan P, Bettman B ( 2008) Real-time DNA sequencing from single polymerase molecules. Science, 323, 133-138.
[18]	Eren AM, Morrison HG, Lescault PJ, Reveillaud J, Vineis JH, Sogin ML ( 2015) Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. The ISME Journal, 9, 968-979. DOI
[19]	Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Brunbjerg AK, Pietroni C, Hansen AJ ( 2017) Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nature Communications, 8, 1188. DOI URL
[20]	Group CPB, Li DZ, Gao LM, Li HT, Wang H, Ge XJ, Liu JQ, Chen ZD, Zhou SL, Chen SL ( 2011) Comparative analysis of a large dataset indicates that internal transcribed spacer (ITS) should be incorporated into the core barcode for seed plants. Proceedings of the National Academy of Sciences, USA, 108, 19641-19646. DOI URL
[21]	Hajibabaei M, Baird DJ, Fahner NA, Beiko R, Golding GB ( 2016) A new way to contemplate Darwin’s tangled bank: How DNA barcodes are reconnecting biodiversity science and biomonitoring. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 371, 20150330. DOI URL
[22]	Hao X, Jiang R, Chen T ( 2011) Clustering 16S rRNA for OTU prediction: A method of unsupervised Bayesian clustering. Bioinformatics, 27, 611-618. DOI URL
[23]	Hebert PD, Braukmann TW, Prosser SW, Ratnasingham S, Ivanova NV, Janzen DH, Hallwachs W, Naik S, Sones JE, Zakharov EV ( 2018) A Sequel to Sanger: Amplicon sequencing that scales. BMC Genomics, 19, 219. DOI
[24]	Hebert PD, Cywinska A, Ball SL ( 2003) Biological identifications through DNA barcodes. Proceedings of the Royal Society of London B: Biological Sciences, 270, 313-321. DOI URL
[25]	Hebert PD, Hollingsworth PM, Hajibabaei M ( 2016) From writing to reading the encyclopedia of life. Proceedings of the Royal Society of London B: Biological Sciences, 371, 20150321. DOI URL
[26]	Hollingsworth PM, Forrest LL, Spouge JL, Hajibabaei M, Ratnasingham S, van der Bank M, Chase MW, Cowan RS, Erickson DL ( 2009) A DNA barcode for land plants. Proceedings of the National Academy of Sciences, USA, 106, 12794-12797. DOI URL
[27]	Jiang H, Lei R, Ding SW, Zhu S ( 2014) Skewer: A fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics, 15, 182. DOI
[28]	Kõljalg U, Larsson KH, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, Erland S, Høiland K, Kjøller R, Larsson E ( 2005) UNITE: A database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytologist, 166, 1063-1068. DOI URL
[29]	Kress WJ, Erickson DL ( 2012) DNA barcodes: Methods and protocols. In: DNA Barcodes (eds Kress WJ, Erickson DL), pp. 3-8.Humana Press, Totowa.
[30]	Kunz TH, Whitaker JO Jr ( 1983) An evaluation of fecal analysis for determining food habits of insectivorous bats. Canadian Journal of Zoology, 61, 1317-1321. DOI URL
[31]	Liu S, Li Y, Lu J, Su X, Tang M, Zhang R, Zhou L, Zhou C, Yang Q, Ji Y ( 2013) SOAPBarcode: Revealing arthropod biodiversity through assembly of Illumina shotgun sequences of PCR amplicons. Methods in Ecology and Evolution, 4, 1142-1150. DOI URL
[32]	Liu S, Wang X, Xie L, Tan M, Li Z, Su X, Zhang H, Misof B, Kjer KM, Tang M ( 2016) Mitochondrial capture enriches mito-DNA 100 fold, enabling PCR-free mitogenomics biodiversity analysis. Molecular Ecology Resources, 16, 470-479. DOI URL
[33]	Liu S, Yang C, Zhou C, Zhou X ( 2017) Filling reference gaps via assembling DNA barcodes using high-throughput sequencing—Moving toward barcoding the world. GigaScience, 6, 1-8.
[34]	Mahon AR, Jerde CL, Galaska M, Bergner JL, Chadderton WL, Lodge DM, Hunter ME, Nico LG ( 2013) Validation of eDNA surveillance sensitivity for detection of Asian carps in controlled and field experiments. PLoS ONE, 8, e58316. DOI URL
[35]	Matias Rodrigues JF, von Mering C ( 2013) HPC-CLUST: Distributed hierarchical clustering for large sets of nucleotide sequences. Bioinformatics, 30, 287-288.
[36]	Meier R, Wong W, Srivathsan A, Foo M ( 2016) $1 DNA barcodes for reconstructing complex phenomes and finding rare species in specimen-rich samples. Cladistics, 32, 100-110. DOI URL
[37]	Nilsson RH, Ryberg M, Abarenkov K, Sjökvist E, Kristiansson E ( 2009) The ITS region as a target for characterization of fungal communities using emerging sequencing technologies. FEMS Microbiology Letters, 296, 97-101. DOI URL
[38]	Pawlowski J, Audic S, Adl S, Bass D, Belbahri L, Berney C, Bowser SS, Cepicka I, Decelle J, Dunthorn M ( 2012) CBOL protist working group: Barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLoS Biology, 10, e1001419. DOI URL
[39]	Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glöckner FO ( 2007) SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Research, 35, 7188-7196. DOI URL
[40]	Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO ( 2012) The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41, D590-D596. DOI URL
[41]	Ratnasingham S, Hebert PD (2007)BOLD: The Barcode of Life Data System (.Molecular Ecology Notes, 7, 355-364.
[42]	Rognes T, Flouri T, Nichols B, Quince C, Mahé F ( 2016) VSEARCH: A versatile open source tool for metagenomics. PeerJ, 4, e2584. DOI URL
[43]	Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ ( 2009) Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75, 7537-7541. DOI URL
[44]	Schnell IB, Thomsen PF, Wilkinson N, Rasmussen M, Jensen LRD, Willerslev E, Bertelsen MF, Gilbert MTP ( 2012) Screening mammal biodiversity using DNA from leeches. Current Biology, 22, R262-R263. DOI URL
[45]	Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, Chen W, Bolchacova E, Voigt K, Crous PW ( 2012) Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for fungi. Proceedings of the National Academy of Sciences, USA, 109, 6241-6246. DOI URL
[46]	Schubert M, Lindgreen S, Orlando L ( 2016) AdapterRemoval v2: Rapid adapter trimming, identification, and read merging. BMC Research Notes, 9, 88. DOI URL
[47]	Shi ZY, Yang CQ, Hao MD, Wang XY, Ward RD, Zhang AB ( 2018) FuzzyID2: A software package for large data set species identification via barcoding and metabarcoding using hidden Markov models and fuzzy set methods. Molecular Ecology Resources, 18, 666-675. DOI URL
[48]	Shokralla S, Gibson JF, Nikbakht H, Janzen DH, Hallwachs W, Hajibabaei M ( 2014) Next-generation DNA barcoding: Using next-generation sequencing to enhance and accelerate DNA barcode capture from single specimens. Molecular Ecology Resources, 14, 892-901.
[49]	Shokralla S, Porter TM, Gibson JF, Dobosz R, Janzen DH, Hallwachs W, Golding GB, Hajibabaei M ( 2015) Massively parallel multiplex DNA sequencing for specimen identification using an Illumina MiSeq platform. Scientific Reports, 5, 9687. DOI
[50]	Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, Arrieta JM, Herndl GJ ( 2006) Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proceedings of the National Academy of Sciences, USA, 103, 12115-12120. DOI URL
[51]	Taberlet P, Coissac E, Hajibabaei M, Rieseberg LH ( 2012) Environmental DNA. Molecular Ecology, 21, 1789-1793. DOI URL
[52]	Tang M, Hardman CJ, Ji Y, Meng G, Liu S, Tan M, Yang S, Moss ED, Wang J, Yang C ( 2015) High-throughput monitoring of wild bee diversity and abundance via mitogenomics. Methods in Ecology and Evolution, 6, 1034-1043. DOI URL
[53]	Turner CR, Miller DJ, Coyne KJ, Corush J ( 2014) Improved methods for capture, extraction, and quantitative assay of environmental DNA from Asian bigheaded carp (Hypophthalmichthys spp.). PLoS ONE, 9, e114329. DOI URL
[54]	Yang C, Tan S, Meng G, Bourne DG, O’Brien PA, Xu J, Liao S, Chen A, Chen X, Liu S ( 2018) Access COI barcode efficiently using high throughput Single End 400 bp sequencing. bioRxiv, doi: 10.1101/498618 .
[55]	Yu DW, Ji YQ, Emerson BC, Wang XY, Ye CX, Yang CY, Ding ZL ( 2012) Biodiversity soup: Metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods in Ecology and Evolution, 3, 613-623. DOI URL
[56]	Zhang J, Kapli P, Pavlidis P, Stamatakis A ( 2013) A general species delimitation method with applications to phylogenetic placements. Bioinformatics, 29, 2869-2876. DOI URL
[57]	Zhou X, Li Y, Liu S, Yang Q, Su X, Zhou L, Tang M, Fu R, Li J, Huang Q ( 2013) Ultra-deep sequencing enables high-fidelity recovery of biodiversity for bulk arthropod samples without PCR amplification. GigaScience, 2, 4. DOI URL

标记基因 Marker gene	目标物种 Targeted group	数据库 Database
16S	细菌和古细菌 Bacteria and archea (Sogin et al, 2006)	核糖体数据库项目 Ribosomal Database Project (RDP, Cole et al, 2008); Greengenes (DeSantis et al, 2006); SILVA (Pruesse et al, 2007)
ITS	真菌(Schoch et al, 2012)、植物(Group et al, 2011)、原生生物(Pawlowski et al, 2012) Fungi (Schoch et al, 2012); plant (Group et al, 2011); protist (Pawlowski et al, 2012)	UNITE (K?ljalg et al, 2005); GenBank (Benson et al, 2012)
18S	原生生物 Protist (Pawlowski et al, 2012)	SILVA (Pruesse et al, 2007)
matK + rbcL	植物 Plant (Hollingsworth et al, 2009)	生命条形码数据库 Barcode of Life Data Systems (BOLD, Ratnasingham & Hebert, 2007); GenBank (Benson et al, 2012)
COI	动物群(Hebert et al, 2003)、原生生物(Pawlowski et al, 2012) Fauna (Hebert et al, 2003) and protist (Pawlowski et al, 2012)	核糖体数据库项目 Ribosomal Database Project (RDP, Cole et al, 2008)

标记基因 Marker gene	目标物种 Targeted group	数据库 Database
16S	细菌和古细菌 Bacteria and archea (Sogin et al, 2006)	核糖体数据库项目 Ribosomal Database Project (RDP, Cole et al, 2008); Greengenes (DeSantis et al, 2006); SILVA (Pruesse et al, 2007)
ITS	真菌(Schoch et al, 2012)、植物(Group et al, 2011)、原生生物(Pawlowski et al, 2012) Fungi (Schoch et al, 2012); plant (Group et al, 2011); protist (Pawlowski et al, 2012)	UNITE (K?ljalg et al, 2005); GenBank (Benson et al, 2012)
18S	原生生物 Protist (Pawlowski et al, 2012)	SILVA (Pruesse et al, 2007)
matK + rbcL	植物 Plant (Hollingsworth et al, 2009)	生命条形码数据库 Barcode of Life Data Systems (BOLD, Ratnasingham & Hebert, 2007); GenBank (Benson et al, 2012)
COI	动物群(Hebert et al, 2003)、原生生物(Pawlowski et al, 2012) Fauna (Hebert et al, 2003) and protist (Pawlowski et al, 2012)	核糖体数据库项目 Ribosomal Database Project (RDP, Cole et al, 2008)

目标序列长度 Targeted region length (bp)	优势 Advantages	劣势 Disadvantages	参考文献 Reference
~300	-	无法处理较长的目标序列; Roche 454平台 Can not work on long fragments; Roche 454 platform	Shokralla et al, 2014
~180	简单, 易操作, 成本低 Straightforward, easy to operate, cost-efficient	目标序列偏短, 只能用于物种初筛 Short targeted region; can only be used for species pre-clustering	Meier et al, 2016
~650	标准DNA条形码全长 Standard full-length COI	普适性差; 需要多轮PCR过程 Poor universality; multiple rounds of PCR	Shokralla et al, 2015; Cruaud et al, 2017
~650	易操作, 标准DNA条形码全长 Easy to operate, standard full-length COI	相对较高的计算资源 Relatively high requirement for computational resources	Liu et al, 2017
~650	易操作, 标准DNA条形码全长 Easy to operate, standard full-length COI	SMRT平台成本高 High cost of SMRT platform	Hebert et al, 2018
~650	易操作, 标准DNA条形码全长 Easy to operate, standard full-length COI	测序平台暂时不够普及 Not a mass production	Yang et al, 2018

目标序列长度 Targeted region length (bp)	优势 Advantages	劣势 Disadvantages	参考文献 Reference
~300	-	无法处理较长的目标序列; Roche 454平台 Can not work on long fragments; Roche 454 platform	Shokralla et al, 2014
~180	简单, 易操作, 成本低 Straightforward, easy to operate, cost-efficient	目标序列偏短, 只能用于物种初筛 Short targeted region; can only be used for species pre-clustering	Meier et al, 2016
~650	标准DNA条形码全长 Standard full-length COI	普适性差; 需要多轮PCR过程 Poor universality; multiple rounds of PCR	Shokralla et al, 2015; Cruaud et al, 2017
~650	易操作, 标准DNA条形码全长 Easy to operate, standard full-length COI	相对较高的计算资源 Relatively high requirement for computational resources	Liu et al, 2017
~650	易操作, 标准DNA条形码全长 Easy to operate, standard full-length COI	SMRT平台成本高 High cost of SMRT platform	Hebert et al, 2018
~650	易操作, 标准DNA条形码全长 Easy to operate, standard full-length COI	测序平台暂时不够普及 Not a mass production	Yang et al, 2018