Genome skimming - Knowledge (XXG)

163:, or plastome, has been used extensively in identification and evolutionary studies using genome skimming due to its high abundance within plants (~3-5% of cell DNA), small size, simple structure, greater conservation of gene structure than nuclear or mitochondrial genes. Plastids studies have previously been limited by the number of regions that could be assessed in traditional approaches. Using genome skimming, the sequencing of the entire plastid genome, or plastome, can be done at a fraction of the cost and time required for typical sequencing approaches like 998:

rare or extinct species. The preservation processes in ethanol often damage the genomic DNA, which hinders the success of standard PCR protocols and other amplicon-based approaches. This presents an opportunity to sequence samples with very low DNA concentrations, without the need for DNA enrichment or amplification. Library preparation for specific to genome skimming has been shown to work with as low as 37 ng of DNA (0.2 ng/ul), 135-fold less than recommended by Illumina.

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2016). Additionally, most library preparation protocols have not been fully automated with robotics yet. On the bioinformatics side, large complex databases and automated workflows need to be designed to handle the large amounts of data resulting from genome skimming. The automation of the following processes need to be implemented:

211:, and high mutation rate. It is often used for phylogenetic studies as it is very uniform across metazoan groups, with a circular, double-stranded DNA molecule structure, about 15 to 20 kilobases, with 37 ribosomal RNA genes, 13 protein-coding genes, and 22 transfer RNA genes. Mitochondrial barcode sequences, such as COI, 863:, referred to as a “baits pool”, which dynamically increases in size with each iteration. Due to the low sequencing coverage of genome skims, non-target reads, even those with high sequence similarity to target reads, are largely not recruited. Using the final recruited organellar-associated reads, GetOrganelle conducts a 269:, single‐copy conserved orthologous gene, and shared copy genes. Another method is looking for novel probes that target low-copy genes using transcriptomics via Hyb-Seq. While nuclear genomes assembled using genome skims are extremely fragmented, some low-copy single-copy nuclear genes can be successfully assembled. 248:

Nuclear repeats in the genome are an underused source of phylogenetic data. When the nuclear genome is sequenced at 5% of the genome, thousands of copies of the nuclear repeats will be present. Although the repeats sequenced will only be representative of those in the entire genome, it has been shown

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Skmer is an assembly-free and alignment-free tool to compute genomic distances between the query and reference genome skims. Skmer uses a 2 stage approach to compute these distances. First, it generates k-mer frequency profiling using a tool called JellyFish and then these k-mers are converted into

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at a lower cost and larger scale than traditional methods. Due to the small amount of DNA required for genome skimming, its methodology can be applied in other fields other than genomics. Tasks like this include determining the traceability of products in the food industry, enforcing international

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Genome skimming is an especially advantageous approach regarding cases where the genomic DNA may be old and degraded from chemical treatments, such as specimens from herbarium and museum collections, a largely untapped genomic resource. Genome skimming allows for the molecular characterization of

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In field studies, invertebrates are stored in ethanol which is usually discarded during DNA-based studies. Genome skimming has been shown to detect the low quantity of DNA from this ethanol-fraction and provide information about the biomass of the specimens in a fraction, the microbiota of outer

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Malé, Pierre-Jean G.; Bardon, Léa; Besnard, Guillaume; Coissac, Eric; Delsuc, Frédéric; Engel, Julien; Lhuillier, Emeline; Scotti-Saintagne, Caroline; Tinaut, Alexandra; Chave, Jérôme (April 2014). "Genome skimming by shotgun sequencing helps resolve the phylogeny of a pantropical tree family".

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Both the wet-lab and the bioinformatics parts of genome skimming have certain challenges with scalability. Although the cost of sequencing in genome skimming is affordable at $ 80 for 1 Gb in 2016, the library preparation for sequencing is still very expensive, at least ~$ 200 per sample (as of

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Although genome skimming is mostly used to extract high-copy plastomes and mitogenomes, it can also provide partial sequences of low-copy nuclear sequences. These sequences may not be sufficiently complete for phylogenomic analysis, but can be sufficient for designing PCR primers and probes for

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When targeting mitogenomes, there are no specific suggestions for minimum final sequencing depth, as mitogenomes are more variable in size and more variable in complexity in plant species, increasing the difficulty of assembling repeated sequences. However, highly conserved coding sequences and

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Hyb-Seq is a new protocol for capturing low-copy nuclear genes that combines target enrichment and genome skimming. Target enrichment of the low-copy loci is achieved through designed enrichment probes for specific single-copy exons, but requires a nuclear draft genome and transcriptome of the

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barcode genes. Compared to the typical DNA barcode, genome skimming produces plastomes at a tenth of the cost per base. Recent uses of genome skims of plastomes have allowed greater resolution of phylogenies, higher differentiation of specific groups within taxa, and more accurate estimates of

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allows for inference of robust phylogenies across many taxonomic groups, and it can capture events such as gene rearrangements and positioning of mobile genetic elements. Using genome skimming to assemble complete mitogenomes, the phylogenetic history and biodiversity of many organisms can be

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Grandjean, Frederic; Tan, Mun Hua; Gan, Han Ming; Lee, Yin Peng; Kawai, Tadashi; Distefano, Robert J.; Blaha, Martin; Roles, Angela J.; Austin, Christopher M. (November 2017). "Rapid recovery of nuclear and mitochondrial genes by genome skimming from Northern Hemisphere freshwater crayfish".

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Genome skimming is a cost-effective, rapid and reliable method to generate large shallow datasets, since several datasets (plastid, mitochondrial, nuclear) are generated per run. It is very simple to implement, requires less lab work and optimization, and does not require

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protocols will depend on a variety of factors: organism, tissue type, etc. In the cases of preserved specimens, specific library preparation protocols modifications may have to be made. The following library preparation protocols have been used in genome skimming:

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Other than the current uses listed above, genome skimming has also been applied to other tasks, such as quantifying pollen mixtures, monitoring and conservation of certain populations. Genome skimming can also be used for variant calling, to examine

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is used to identify genes in the genome assemblies. The annotation tool chosen will depend on the target genome and the target features of that genome. The following annotation tools have been used in genome skimming to annotate organellar genomes:

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and museums, where the DNA is often very degraded, and very little remains. Studies in plants show that DNA as old as 80 years and with as little as 500 pg of degraded DNA, can be used with genome skimming to infer genomic information. In

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Lin, Diana; Coombe, Lauren; Jackman, Shaun D.; Gagalova, Kristina K.; Warren, René L.; Hammond, S. Austin; McDonald, Helen; Kirk, Heather; Pandoh, Pawan; Zhao, Yongjun; Moore, Richard A. (2019-06-13). Stajich, Jason E. (ed.).

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Lin, Diana; Coombe, Lauren; Jackman, Shaun D.; Gagalova, Kristina K.; Warren, René L.; Hammond, S. Austin; Kirk, Heather; Pandoh, Pawan; Zhao, Yongjun; Moore, Richard A.; Mungall, Andrew J. (2019-06-06). Rokas, Antonis (ed.).

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to the target genome, using Bowtie2, are referred to as “seed reads”. The seed reads are used as “baits” to recruit more organelle-associated reads via multiple iterations of extension. The read extension algorithm uses a

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Genome skimming scratches the surface of the genome, so it will not suffice for biological questions that require gene prediction and annotation. These downstream steps are required for deep and more meaningful analyses.

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A combination of sequencing depth and read type, as well as genomic target (plastome, mitogenome, etc.), will influence the success of single-end and paired-end assemblies, so these parameters must be carefully chosen.

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Straub, Shannon C. K.; Parks, Matthew; Weitemier, Kevin; Fishbein, Mark; Cronn, Richard C.; Liston, Aaron (February 2012). "Navigating the tip of the genomic iceberg: Next-generation sequencing for plant systematics".

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Bankevich, Anton; Nurk, Sergey; Antipov, Dmitry; Gurevich, Alexey A.; Dvorkin, Mikhail; Kulikov, Alexander S.; Lesin, Valery M.; Nikolenko, Sergey I.; Pham, Son; Prjibelski, Andrey D.; Pyshkin, Alexey V. (May 2012).

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and relied on large intact DNA templates and were affected by contamination and method of preservation. Genome skimming, on the other hand, can be used to extract genetic information from preserved species in

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and their abundance is estimated. The distribution and occurrence of these repeat types can be phylogenetically informative and provide information about the evolutionary history of various species.

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Dodsworth, Steven; Guignard, Maïté S.; Christenhusz, Maarten J. M.; Cowan, Robyn S.; Knapp, Sandra; Maurin, Olivier; Struebig, Monika; Leitch, Andrew R.; Chase, Mark W.; Forest, Félix (2018-10-29).

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GetOrganelle is a toolkit that assembles organellar genomes uses genome skimming reads. Organelle-associated reads are recruited using a modified “baiting and iterative mapping” approach. The reads

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if (deep) whole-genome sequencing data has already been obtained. Genome skimming has been demonstrated to simplify organellar genome assembly by subsampling the reads of the nuclear genome via

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Although plastid genomic sequences are abundant in genome skims, the presence of mitochondrial and nuclear pseudogenes of plastid origin can potentially pose issues for plastome assemblies.

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are non-coding regions within the 18-5.8-28S rDNA in eukaryotes and are one feature of rDNA that has been used in genome skimming studies. ITS are used to detect different species within a

2274:"Genome skimming provides well resolved plastid and nuclear phylogenies, showing patterns of deep reticulate evolution in the tropical carnivorous plant genus Nepenthes (Caryophyllales)" 291:, even with low yield and low-quality DNA, one study was still able to produce "high-quality complete chloroplast and ribosomal DNA sequences" at a large scale for downstream analyses. 136:, due to their high inter-species variability. These have low individual variability, preventing the identification of distinct strains or individuals. They are also present in all 3797: 2719:"Exploring the potential of nuclear and mitochondrial sequencing data generated through genome‐skimming for plant phylogenetics: A case study from a clade of neotropical lianas" 964:

genome skimming essentially filters out nuclear sequences, leaving a higher organellar to nuclear sequence ratio for assembly, reducing the complexity of the assembly paradigm.

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Berger, Brent A.; Han, Jiahong; Sessa, Emily B.; Gardner, Andrew G.; Shepherd, Kelly A.; Ricigliano, Vincent A.; Jabaily, Rachel S.; Howarth, Dianella G. (October 2017).

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Nevill, Paul G.; Zhong, Xiao; Tonti-Filippini, Julian; Byrne, Margaret; Hislop, Michael; Thiele, Kevin; van Leeuwen, Stephen; Boykin, Laura M.; Small, Ian (2020-01-04).

474:. Assemblers chosen will depend on the target genome and whether short or long reads are used. The following tools have been used to assemble genomes from genome skims: 994:

knowledge of the organism nor its genome size. This provides a low-risk avenue for biological inquiry and hypothesis generation without a huge commitment of resources.

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Low-copy DNA can prove useful for evolution developmental and phylogenetic studies. It can be mined from high-copy fractions in a number of ways such as developing

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Berger, Brent A.; Han, Jiahong; Sessa, Emily B.; Gardner, Andrew G.; Shepherd, Kelly A.; Ricigliano, Vincent A.; Jabaily, Rachel S.; Howarth, Dianella G. (2017).

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protocols will vary depending on the source of the sample (i.e. plants, animals, etc.). The following DNA extraction protocols have been used in genome skimming:

1618:"Chloroplast genome analyses and genomic resource development for epilithic sister genera Oresitrophe and Mukdenia (Saxifragaceae), using genome skimming data" 183:

When targeting plastomes, it is suggested that a minimum final sequencing depth of 30X is achieved for single-copy regions to ensure high-quality assemblies.

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Johri, Shaili; Solanki, Jitesh; Cantu, Vito Adrian; Fellows, Sam R.; Edwards, Robert A.; Moreno, Isabel; Vyas, Asit; Dinsdale, Elizabeth A. (December 2019).

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targeted organism. The target-enriched libraries are then sequenced, and the resulting reads processed, assembled, and identified. Using off-target reads,

216: 3822: 1331:"Genome skimming is a low-cost and robust strategy to assemble complete mitochondrial genomes from ethanol preserved specimens in biodiversity studies" 180:

biodiversity. Additionally, the plastome has been used to compare species within a genus to look at evolutionary changes and diversity within a group.

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tissue layers and the gut contents (like prey) released by the vomit reflex. Thus, genome skimming can provide an additional method of understanding

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Weitemier, Kevin; Straub, Shannon C. K.; Cronn, Richard C.; Fishbein, Mark; Schmickl, Roswitha; McDonnell, Angela; Liston, Aaron (September 2014).

3944: 859:, where the reads are cut into substrings of certain lengths, referred to as “words”. At each extension iteration, these “words” are added to a 3802: 878:

is filtered and untangled, producing all possible paths of the graph, and therefore all configurations of the circular organellar genomes.

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Ondov, Brian D.; Treangen, Todd J.; Melsted, Páll; Mallonee, Adam B.; Bergman, Nicholas H.; Koren, Sergey; Phillippy, Adam M. (Dec 2016).

42:. These genome skims contain information about the high-copy fraction of the genome. The high-copy fraction of the genome consists of the 887:

hashes. A random subset of these hashes are selected to form a so-called "sketch". For its second stage, Skmer uses Mash to estimate the

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Various protocols, pipelines, and bioinformatic tools have been developed to help automate the downstream processes of genome skimming.

3817: 3611: 2647:"Microsatellite development from genome skimming and transcriptome sequencing: comparison of strategies and lessons from frog species" 968:

genome skimming was first done as a proof-of-concept, optimizing the parameters for read type, read length, and sequencing coverage.

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are inferred using phylogenetic reconstruction software. The software chosen for phylogeny reconstruction will depend on whether a

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Although genome skimming is usually chosen as a cost-effective method to sequence organellar genomes, genome skimming can be done

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Zeng, Chun-Xia; Hollingsworth, Peter M.; Yang, Jing; He, Zheng-Shan; Zhang, Zhi-Rong; Li, De-Zhu; Yang, Jun-Bo (2018-06-05).

1109: 978: 184: 2819:"The Utility of Genome Skimming for Phylogenomic Analyses as Demonstrated for Glycerid Relationships (Annelida, Glyceridae)" 2338:"Geneious! Simplified Genome Skimming Methods for Phylogenetic Systematic Studies: A Case Study in Oreocarya (Boraginaceae)" 1702:"Plastome of Quercus xanthoclada and comparison of genomic diversity amongst selected Quercus species using genome skimming" 2767:"Genome skimming reveals the origin of the Jerusalem Artichoke tuber crop species: neither from Jerusalem nor an artichoke" 3456:"Complete Chloroplast Genome Sequence of an Engelmann Spruce ( Picea engelmannii, Genotype Se404-851) from Western Canada" 926: 717: 1059:

Some of these scalability challenges have already been implemented, as shown above in the "Tools and Pipelines" section.

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and complete plastomes can also be assembled. Through this process, Hyb-Seq is able to produce genome-scale datasets for

3903: 71: 1774:"'Genome skimming' with the MinION hand-held sequencer identifies CITES-listed shark species in India's exports market" 1472:

Denver, Dee R.; Brown, Amanda M. V.; Howe, Dana K.; Peetz, Amy B.; Zasada, Inga A. (2016-08-04). Round, June L. (ed.).

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Trevisan, Bruna; Alcantara, Daniel M.C.; Machado, Denis Jacob; Marques, Fernando P.L.; Lahr, Daniel J.G. (2019-09-13).

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Jin, Jian-Jun; Yu, Wen-Bin; Yang, Jun-Bo; Song, Yu; dePamphilis, Claude W.; Yi, Ting-Shuang; Li, De-Zhu (2018-03-09).

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In addition to the assembly of the smaller organellar genomes, genome skimming can also be used to uncover conserved

3651: 2218:"Genome skimming provides new insight into the relationships in Ludwigia section Macrocarpon, a polyploid complex" 3807: 3792: 902:

is an integrative software platform that allows users to perform various steps in bioinformatic analysis such as

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Sarmashghi, Shahab; Bohmann, Kristine; P. Gilbert, M. Thomas; Bafna, Vineet; Mirarab, Siavash (December 2019).

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in nuclear repeats require longer reads. The following sequencing platforms have been used in genome skimming:

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method is appropriate. The following phylogenetic reconstruction programs have been used in genome skimming:

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The Illumina MiSeq platform has been chosen by certain researchers for its long read length for short reads.

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Liu, Luxian; Wang, Yuewen; He, Peizi; Li, Pan; Lee, Joongku; Soltis, Douglas E.; Fu, Chengxin (2018-04-04).

3513:"Taking Advantage of the Genomics Revolution for Monitoring and Conservation of Chondrichthyan Populations" 1844:"The unexpected depths of genome-skimming data: A case study examining Goodeniaceae floral symmetry genes1" 891:

of two of these sketches. The combination of these 2 stages is used to estimate the evolutionary distance.

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Genome skimming allows for assembly of high-copy fractions of the genome into contiguous, complete genomes.

3604: 1901:"The Unexpected Depths of Genome-Skimming Data: A Case Study Examining Goodeniaceae Floral Symmetry Genes" 2817:

Richter, Sandy; Schwarz, Francine; Hering, Lars; Böggemann, Markus; Bleidorn, Christoph (December 2015).

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Nauheimer, Lars; Cui, Lujing; Clarke, Charles; Crayn, Darren M.; Bourke, Greg; Nargar, Katharina (2019).

2053:"Large scale genome skimming from herbarium material for accurate plant identification and phylogenomics" 1554:"A Simple Method to Decode the Complete 18-5.8-28S rRNA Repeated Units of Green Algae by Genome Skimming" 3939: 3773: 3738: 3032: 1005:

Genome skimming is not dependent on any specific primers and remains unaffected by gene rearrangements.

1474:"Genome Skimming: A Rapid Approach to Gaining Diverse Biological Insights into Multicellular Pathogens" 66:

technology to generate these skims. Although these skims are merely 'the tip of the genomic iceberg',

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Identification of the different organisms from shotgun sequencing of environmental DNA (metagenomics)

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Liu, Shih-Hui; Edwards, Christine E.; Hoch, Peter C.; Raven, Peter H.; Barber, Janet C. (May 2018).

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that these sequenced fractions accurately reflect genomic abundance. These repeats can be clustered

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Dodsworth, Steven (September 2015). "Genome skimming for next-generation biodiversity analysis".

1089: 907: 851: 104: 2936: 3015:"GetOrganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes" 3983: 3962: 3893: 3883: 3873: 3707: 3597: 3575: 3534: 3493: 3475: 3435: 3417: 3340: 3322: 3281: 3263: 3224: 3206: 3156: 3138: 3098: 3080: 2995: 2977: 2856: 2838: 2788: 2740: 2686: 2668: 2622: 2604: 2556: 2494: 2447: 2429: 2375: 2357: 2293: 2239: 2173: 2165: 2092: 2074: 2012: 1994: 1938: 1920: 1881: 1863: 1819: 1801: 1737: 1719: 1657: 1639: 1585: 1513: 1495: 1429: 1370: 1352: 1253: 1217: 1177: 1079: 1074: 929:. It uses built-in database or user specified reference to extract orthologous sequences from 903: 871: 867: 713: 463: 376: 278: 266: 233: 224: 196: 164: 112: 92: 51: 3923: 3898: 3565: 3552:

Coissac, Eric; Hollingsworth, Peter M.; Lavergne, Sébastien; Taberlet, Pierre (April 2016).

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Stoughton, Thomas R.; Kriebel, Ricardo; Jolles, Diana D.; O'Quinn, Robin L. (March 2018).

1277: 875: 47: 236:. Sequences should be masked similarly to targeting plastomes and nuclear ribosomal DNA. 3360:"PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome skimming data" 3119:"SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing" 1789: 3913: 3853: 3753: 3488: 3455: 3430: 3397: 3380: 3359: 3335: 3300: 3276: 3243: 3219: 3184: 3151: 3118: 3093: 3060: 2990: 2957: 2851: 2818: 2681: 2646: 2617: 2582: 2442: 2409: 2370: 2337: 2087: 2052: 2007: 1972: 1933: 1900: 1876: 1843: 1814: 1773: 1732: 1701: 1652: 1617: 1580: 1553: 1508: 1473: 1365: 1330: 1069: 583: 420: 416: 312: 223:, can also be used for taxonomic identification. The increased publishing of complete 63: 55: 31: 1052:

Identification of unknown specimen from a small shotgun sequencing or any DNA fragment

3977: 3244:"A fast, lock-free approach for efficient parallel counting of occurrences of k-mers" 2765:

Bock, Dan G.; Kane, Nolan C.; Ebert, Daniel P.; Rieseberg, Loren H. (February 2014).

2305: 938: 934: 911: 888: 856: 839: 835: 168: 141: 96: 67: 43: 3259: 2919: 2185: 1441: 3863: 3743: 108: 75: 3185:"Skmer: assembly-free and alignment-free sample identification using genome skims" 2958:"Hyb-Seq: Combining Target Enrichment and Genome Skimming for Plant Phylogenomics" 1213: 1552:

Lin, Geng-Ming; Lai, Yu-Heng; Audira, Gilbert; Hsiao, Chung-Der (November 2017).

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Johri, Shaili; Doane, Michael; Allen, Lauren; Dinsdale, Elizabeth (2019-03-29).

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Linard, B.; Arribas, P.; Andújar, C.; Crampton‐Platt, A.; Vogler, A. P. (2016).

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Ripma, Lee A.; Simpson, Michael G.; Hasenstab-Lehman, Kristen (December 2014).

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Xia, Yun; Luo, Wei; Yuan, Siqi; Zheng, Yuchi; Zeng, Xiaomao (December 2018).

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genome skimming. Since the organellar genomes will be high-copy in the cell,

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with short reads or long reads will depend on the target genome or genes.

3888: 3858: 3702: 3692: 3620: 3471: 3413: 2973: 2834: 2583:"Genome skimming identifies polymorphism in tern populations and species" 2353: 1973:"Genome skimming herbarium specimens for DNA barcoding and phylogenomics" 1916: 1859: 1570: 220: 100: 2910: 2551: 1173: 1037:

Assembly of organellar DNA (as well as nuclear ribosomal tandem repeats)

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of 100X is achieved, and sequences with less than 5X depth are masked.

3570: 3553: 3529: 3512: 2901: 2783: 2766: 2735: 2718: 2525:"Potential of Herbariomics for Studying Repetitive DNA in Angiosperms" 2489: 2473: 2410:"Genome‐skimming provides accurate quantification for pollen mixtures" 2234: 2217: 3712: 3687: 922: 35: 3301:"Mash: fast genome and metagenome distance estimation using MinHash" 2289: 3023: 3014: 167:. Plastomes have been suggested as a method to replace traditional 3554:"From barcodes to genomes: extending the concept of DNA barcoding" 147:

When targeting nuclear rDNA, it is suggested that a minimum final

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Previous methods of trying to recover degraded DNA were based on

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regulations regarding biodiversity and biological resources, and

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Lang, Dandan; Tang, Min; Hu, Jiahui; Zhou, Xin (November 2019).

3593: 3682: 3677: 2133:"Lessons from genome skimming of arthropod-preserving ethanol" 2717:

Fonseca, Luiz Henrique M.; Lohmann, Lúcia G. (January 2020).

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Jackson, David; Emslie, Steven D; van Tuinen, Marcel (2012).

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Hinsinger, Damien Daniel; Strijk, Joeri Sergej (2019-01-10).

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by incorporating other tools within a GUI based platform.

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Estimation of sequencing coverage for single-copy genes

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After genome skimming, high-copy organellar DNA can be

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Extraction of reads corresponding to single-copy genes

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nonrepetitive flanking regions can be assembled using

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is a sequencing approach that uses low-pass, shallow

3932: 3831: 3782: 3726: 3665: 38:(up to 5%), to generate fragments of DNA, known as 3242:Marçais, Guillaume; Kingsford, Carl (2011-03-15). 140:, have a high evolution rate and has been used in 1040:Annotation of the different assembled fragments 3466:(24): e00382–19, /mra/8/24/MRA.00382–19.atom. 3408:(23): e00381–19, /mra/8/23/MRA.00381–19.atom. 3059:Langmead, Ben; Salzberg, Steven L (Mar 2012). 3605: 470:. High-copy nuclear repeats can be clustered 353:Cetyl Trimethylammonium Bromide (CTAB) method 203:in a great variety of studies because of its 8: 2331: 2329: 2327: 2325: 2323: 2321: 2319: 2317: 2315: 1558:International Journal of Molecular Sciences 187:with less than 20X depth should be masked. 99:. In phylogenomic studies of multicellular 3612: 3598: 3590: 3061:"Fast gapped-read alignment with Bowtie 2" 1043:Removal of potential contaminant sequences 403:TruSeq Nano DNA LT Library Preparation kit 385:Illumina TruSeq DNA Sample Preparation kit 3569: 3528: 3487: 3429: 3379: 3358:Cai, L., Zhang, H., Davis, C. C. (2022). 3334: 3316: 3275: 3218: 3200: 3150: 3092: 3022: 2989: 2909: 2850: 2782: 2734: 2680: 2662: 2616: 2598: 2550: 2540: 2488: 2441: 2369: 2233: 2159: 2086: 2068: 2006: 1988: 1932: 1875: 1813: 1731: 1713: 1651: 1633: 1579: 1569: 1507: 1489: 1364: 1346: 1283:CS1 maint: multiple names: authors list ( 449:Oxford Nanopore Technologies (ONT) MinION 622:Dual Organellar GenoMe Annotator (DOGMA) 604:Dual Organellar GenoMe Annotator (DOGMA) 207:, high copy-number in the cell, lack of 18: 3945:List of genetics research organizations 2126: 2046: 1122: 3178: 3176: 3174: 3172: 3170: 3041: 3030: 2931: 2929: 2882: 2880: 2878: 2876: 2874: 2872: 2870: 2812: 2810: 2808: 2806: 2804: 2802: 2760: 2758: 2756: 2754: 2712: 2710: 2708: 2706: 2704: 2702: 2700: 2640: 2638: 2636: 2576: 2574: 2572: 2570: 2518: 2516: 2514: 2512: 2510: 2508: 2467: 2465: 2463: 2461: 2403: 2401: 2399: 2397: 2395: 2393: 2391: 2389: 2267: 2265: 2263: 2261: 2259: 2257: 2255: 2253: 2211: 2209: 2207: 2205: 2203: 2201: 2199: 2197: 2195: 2124: 2122: 2120: 2118: 2116: 2114: 2112: 2110: 2108: 2106: 2044: 2042: 2040: 2038: 2036: 2034: 2032: 2030: 2028: 2026: 1273: 1263: 925:is a bioinformatic pipeline write in 400:Nextera XT DNA Library Preparation kit 338:Invitrogen ChargeSwitch gDNA Plant kit 265:from databases that contain conserved 185:Single nucleotide polymorphisms (SNPs) 103:, genome skimming can be used to find 70:of them can still provide insights on 1966: 1964: 1962: 1960: 1958: 1956: 1954: 1952: 1837: 1835: 1833: 1767: 1765: 1763: 1761: 1759: 1757: 1755: 1753: 1751: 1695: 1693: 1691: 1611: 1609: 1607: 1605: 1603: 1601: 1599: 1547: 1406: 1404: 1324: 1322: 1320: 1318: 1316: 1314: 1195: 1193: 1191: 1154: 1152: 1150: 1148: 1146: 7: 2723:Journal of Systematics and Evolution 1689: 1687: 1685: 1683: 1681: 1679: 1677: 1675: 1673: 1671: 1545: 1543: 1541: 1539: 1537: 1535: 1533: 1531: 1529: 1527: 1467: 1465: 1463: 1461: 1459: 1457: 1455: 1453: 1451: 1402: 1400: 1398: 1396: 1394: 1392: 1390: 1388: 1386: 1384: 1312: 1310: 1308: 1306: 1304: 1302: 1300: 1298: 1296: 1294: 1243: 1241: 1239: 1237: 1235: 1233: 1231: 1144: 1142: 1140: 1138: 1136: 1134: 1132: 1130: 1128: 1126: 466:with a reference guide or assembled 3460:Microbiology Resource Announcements 3402:Microbiology Resource Announcements 356:Qiagen DNeasy Tissue Extraction kit 2529:Frontiers in Ecology and Evolution 1248:Dodsworth, Steven Andrew, author. 359:Qiagen DNeasy Blood and Tissue kit 130:Internal transcribed spacers (ITS) 14: 1250:Genome skimming for phylogenomics 1034:Assembly of the standard barcodes 3958: 3957: 3123:Journal of Computational Biology 1095:Alignment-free sequence analysis 1002:hybridization-based approaches. 979:single nucleotide polymorphisms 240:Nuclear repeats (satellites or 62:. It employs high-throughput, 54:), and nuclear repeats such as 3364:Applications in Plant Sciences 2962:Applications in Plant Sciences 2342:Applications in Plant Sciences 1905:Applications in Plant Sciences 1848:Applications in Plant Sciences 1110:List of phylogenetics software 199:, or mitogenome, is used as a 1: 3260:10.1093/bioinformatics/btr011 1214:10.1016/j.tplants.2015.06.012 443:Illumina NextSeq 550 platform 437:Illumina HiSeq X Ten platform 350:Quick-DNA Plus Extraction kit 3904:Missing heritability problem 2823:Genome Biology and Evolution 2278:Australian Systematic Botany 1491:10.1371/journal.ppat.1005713 708:The assembled sequences are 434:Illumina HiSeq 4000 platform 431:Illumina HiSeq 2500 platform 428:Illumina HiSeq 2000 platform 388:Illumina TruSeq PCR-free kit 332:Qiagen DNeasy Plant Mini kit 144:between and across species. 2414:Molecular Ecology Resources 2140:Molecular Ecology Resources 1715:10.3897/phytokeys.132.36365 1414:Molecular Ecology Resources 1105:List of sequenced plastomes 1100:Computational phylogenetics 704:Phylogenetic reconstruction 391:NEXTFlex DNA Sequencing kit 335:Tiangen DNAsecure Plant kit 16:Method of genome sequencing 4005: 2477:American Journal of Botany 2222:American Journal of Botany 1798:10.1038/s41598-019-40940-9 1162:American Journal of Botany 64:next generation sequencing 3953: 3627: 3318:10.1186/s13059-016-0997-x 3202:10.1186/s13059-019-1632-4 2664:10.1186/s12864-018-5329-y 2070:10.1186/s13007-019-0534-5 1990:10.1186/s13007-018-0300-0 1635:10.1186/s12864-018-4633-x 939:nuclear ribosomal regions 273:Low-quantity degraded DNA 234:reference-guided assembly 50:), mitochondrial genome ( 397:NEBNext Multiplex Oligos 2542:10.3389/fevo.2018.00174 2426:10.1111/1755-0998.13061 2152:10.1111/1755-0998.12539 1426:10.1111/1755-0998.12246 1202:Trends in Plant Science 776:Bayesian Inference (BI) 737:Maximum Likelihood (ML) 726:Bayesian Inference (BI) 718:Maximum Likelihood (ML) 446:Illumina GAIIx platform 440:Illumina MiSeq platform 171:in plants, such as the 3989:DNA sequencing methods 3040:Cite journal requires 2600:10.1186/1756-0500-5-94 941:using a BLAST search. 761:Maximum Parsimony (MP) 722:Maximum Parsimony (MP) 24: 3940:List of genetic codes 3135:10.1089/cmb.2012.0021 242:transposable elements 142:phylogenetic analysis 68:phylogenomic analysis 60:transposable elements 22: 3839:Behavioural genetics 3472:10.1128/MRA.00382-19 3414:10.1128/MRA.00381-19 2974:10.3732/apps.1400042 2354:10.3732/apps.1400062 1917:10.3732/apps.1700042 1860:10.3732/apps.1700042 1571:10.3390/ijms18112341 406:Rapid Sequencing kit 394:NEBNext Ultra II DNA 329:Plant DNAzol Reagent 205:maternal inheritance 197:mitochondrial genome 97:phylogenomic studies 72:evolutionary history 3919:Population genomics 3909:Molecular evolution 3869:Genetic engineering 1790:2019NatSR...9.4476J 1174:10.3732/ajb.1100335 1085:Coverage (genetics) 821:Tools and Pipelines 377:Library preparation 372:Library preparation 3879:Genetic monitoring 3372:10.1002/aps3.11475 3077:10.1038/nmeth.1923 2835:10.1093/gbe/evv224 2587:BMC Research Notes 1778:Scientific Reports 1348:10.7717/peerj.7543 1276:has generic name ( 1090:Taxonomy (biology) 981:across a species. 972:Other Applications 714:phylogenetic trees 299:via low copy DNA. 46:, plastid genome ( 25: 3971: 3970: 3894:He Jiankui affair 3884:Genetic genealogy 3874:Genetic diversity 3803:the British Isles 3708:Genetic variation 3571:10.1111/mec.13549 3558:Molecular Ecology 3530:10.3390/d11040049 2937:"Geneious – OSTR" 2902:10.1111/zsc.12247 2890:Zoologica Scripta 2829:(12): 3443–3462. 2784:10.1111/nph.12560 2736:10.1111/jse.12533 2490:10.1002/ajb2.1061 2235:10.1002/ajb2.1086 1080:Sequence assembly 1075:Sequence analysis 817: 816: 700: 699: 658: 657: 524: 523: 368: 367: 279:Sanger sequencing 267:orthologous genes 165:Sanger sequencing 113:genomic variation 111:and characterize 3996: 3961: 3960: 3924:Reverse genetics 3899:Medical genetics 3614: 3607: 3600: 3591: 3584: 3583: 3573: 3564:(7): 1423–1428. 3549: 3543: 3542: 3532: 3508: 3502: 3501: 3491: 3450: 3444: 3443: 3433: 3392: 3386: 3385: 3383: 3355: 3349: 3348: 3338: 3320: 3296: 3290: 3289: 3279: 3239: 3233: 3232: 3222: 3204: 3180: 3165: 3164: 3154: 3113: 3107: 3106: 3096: 3056: 3050: 3049: 3043: 3038: 3036: 3028: 3026: 3010: 3004: 3003: 2993: 2953: 2947: 2946: 2944: 2943: 2933: 2924: 2923: 2913: 2884: 2865: 2864: 2854: 2814: 2797: 2796: 2786: 2777:(3): 1021–1030. 2762: 2749: 2748: 2738: 2714: 2695: 2694: 2684: 2666: 2642: 2631: 2630: 2620: 2602: 2578: 2565: 2564: 2554: 2544: 2520: 2503: 2502: 2492: 2469: 2456: 2455: 2445: 2420:(6): 1433–1446. 2405: 2384: 2383: 2373: 2333: 2310: 2309: 2269: 2248: 2247: 2237: 2213: 2190: 2189: 2163: 2146:(6): 1365–1377. 2137: 2128: 2101: 2100: 2090: 2072: 2048: 2021: 2020: 2010: 1992: 1968: 1947: 1946: 1936: 1896: 1890: 1889: 1879: 1839: 1828: 1827: 1817: 1769: 1746: 1745: 1735: 1717: 1697: 1666: 1665: 1655: 1637: 1613: 1594: 1593: 1583: 1573: 1549: 1522: 1521: 1511: 1493: 1469: 1446: 1445: 1408: 1379: 1378: 1368: 1350: 1326: 1289: 1288: 1281: 1275: 1271: 1269: 1261: 1245: 1226: 1225: 1197: 1186: 1185: 1156: 857:hashing approach 732: 710:globally aligned 667: 591: 555:SOAPdenovo-Trans 478: 319: 201:molecular marker 149:sequencing depth 4004: 4003: 3999: 3998: 3997: 3995: 3994: 3993: 3974: 3973: 3972: 3967: 3949: 3928: 3827: 3818:the Middle East 3784:Archaeogenetics 3778: 3722: 3661: 3623: 3618: 3588: 3587: 3551: 3550: 3546: 3510: 3509: 3505: 3452: 3451: 3447: 3394: 3393: 3389: 3357: 3356: 3352: 3298: 3297: 3293: 3241: 3240: 3236: 3182: 3181: 3168: 3115: 3114: 3110: 3058: 3057: 3053: 3039: 3029: 3012: 3011: 3007: 2955: 2954: 2950: 2941: 2939: 2935: 2934: 2927: 2886: 2885: 2868: 2816: 2815: 2800: 2771:New Phytologist 2764: 2763: 2752: 2716: 2715: 2698: 2644: 2643: 2634: 2580: 2579: 2568: 2522: 2521: 2506: 2471: 2470: 2459: 2407: 2406: 2387: 2348:(12): 1400062. 2335: 2334: 2313: 2290:10.1071/SB18057 2271: 2270: 2251: 2215: 2214: 2193: 2135: 2130: 2129: 2104: 2050: 2049: 2024: 1970: 1969: 1950: 1911:(10): 1700042. 1898: 1897: 1893: 1854:(10): 1700042. 1841: 1840: 1831: 1771: 1770: 1749: 1699: 1698: 1669: 1615: 1614: 1597: 1551: 1550: 1525: 1484:(8): e1005713. 1471: 1470: 1449: 1410: 1409: 1382: 1328: 1327: 1292: 1282: 1272: 1262: 1247: 1246: 1229: 1199: 1198: 1189: 1158: 1157: 1124: 1119: 1114: 1065: 1027: 1011: 987: 974: 950: 948:Genome skimming 920: 897: 884: 848: 831: 823: 818: 799: 778: 763: 739: 706: 701: 664: 659: 646: 631: 613: 598: 581: 576: 530: 525: 503: 485: 460: 452: 421:Microsatellites 414: 409: 374: 369: 347: 326: 310: 305: 275: 259: 246: 193: 157: 126: 121: 89: 56:microsatellites 28:Genome skimming 17: 12: 11: 5: 4002: 4000: 3992: 3991: 3986: 3976: 3975: 3969: 3968: 3966: 3965: 3954: 3951: 3950: 3948: 3947: 3942: 3936: 3934: 3930: 3929: 3927: 3926: 3921: 3916: 3914:Plant genetics 3911: 3906: 3901: 3896: 3891: 3886: 3881: 3876: 3871: 3866: 3861: 3856: 3854:Genome editing 3851: 3846: 3841: 3835: 3833: 3832:Related topics 3829: 3828: 3826: 3825: 3820: 3815: 3810: 3805: 3800: 3795: 3789: 3787: 3780: 3779: 3777: 3776: 3771: 3766: 3761: 3756: 3754:Immunogenetics 3751: 3746: 3741: 3736: 3730: 3728: 3724: 3723: 3721: 3720: 3715: 3710: 3705: 3700: 3695: 3690: 3685: 3680: 3675: 3669: 3667: 3666:Key components 3663: 3662: 3660: 3659: 3654: 3649: 3644: 3639: 3634: 3628: 3625: 3624: 3619: 3617: 3616: 3609: 3602: 3594: 3586: 3585: 3544: 3503: 3445: 3387: 3350: 3305:Genome Biology 3291: 3254:(6): 764–770. 3248:Bioinformatics 3234: 3189:Genome Biology 3166: 3129:(5): 455–477. 3108: 3071:(4): 357–359. 3065:Nature Methods 3051: 3042:|journal= 3024:10.1101/256479 3005: 2968:(9): 1400042. 2948: 2925: 2896:(6): 718–728. 2866: 2798: 2750: 2696: 2632: 2566: 2504: 2483:(3): 536–548. 2457: 2385: 2311: 2284:(3): 243–254. 2249: 2228:(5): 875–887. 2191: 2102: 2022: 1948: 1891: 1829: 1747: 1708:(132): 75–89. 1667: 1595: 1523: 1478:PLOS Pathogens 1447: 1380: 1290: 1227: 1208:(9): 525–527. 1187: 1168:(2): 349–364. 1121: 1120: 1118: 1115: 1113: 1112: 1107: 1102: 1097: 1092: 1087: 1082: 1077: 1072: 1070:DNA sequencing 1066: 1064: 1061: 1057: 1056: 1053: 1050: 1047: 1044: 1041: 1038: 1035: 1026: 1023: 1010: 1007: 986: 983: 973: 970: 949: 943: 919: 916: 896: 893: 883: 880: 876:assembly graph 847: 844: 830: 827: 822: 819: 815: 814: 810: 809: 806: 803: 798: 795: 793: 792: 791: 788: 785: 782: 777: 774: 772: 771: 770: 767: 762: 759: 757: 756: 755: 752: 749: 746: 743: 738: 735: 730: 705: 702: 698: 697: 693: 692: 691:EMBOSS Transeq 689: 686: 681: 680: 679: 676: 673: 665: 663: 660: 656: 655: 651: 650: 645: 642: 640: 639: 638: 635: 630: 627: 625: 624: 623: 620: 617: 612: 609: 607: 606: 605: 602: 597: 594: 589: 580: 577: 575: 574: 571: 568: 565: 562: 559: 556: 553: 550: 547: 544: 541: 538: 535: 531: 529: 526: 522: 521: 517: 516: 513: 510: 507: 502: 499: 497: 496: 495: 492: 489: 484: 481: 476: 459: 456: 451: 450: 447: 444: 441: 438: 435: 432: 429: 425: 413: 410: 408: 407: 404: 401: 398: 395: 392: 389: 386: 382: 373: 370: 366: 365: 361: 360: 357: 354: 351: 346: 343: 341: 340: 339: 336: 333: 330: 325: 322: 317: 313:DNA extraction 309: 308:DNA extraction 306: 304: 301: 274: 271: 258: 255: 245: 238: 192: 189: 161:plastid genome 156: 153: 125: 122: 120: 117: 105:effector genes 95:sequences for 88: 85: 15: 13: 10: 9: 6: 4: 3: 2: 4001: 3990: 3987: 3985: 3982: 3981: 3979: 3964: 3956: 3955: 3952: 3946: 3943: 3941: 3938: 3937: 3935: 3931: 3925: 3922: 3920: 3917: 3915: 3912: 3910: 3907: 3905: 3902: 3900: 3897: 3895: 3892: 3890: 3887: 3885: 3882: 3880: 3877: 3875: 3872: 3870: 3867: 3865: 3862: 3860: 3857: 3855: 3852: 3850: 3847: 3845: 3842: 3840: 3837: 3836: 3834: 3830: 3824: 3821: 3819: 3816: 3814: 3811: 3809: 3806: 3804: 3801: 3799: 3796: 3794: 3791: 3790: 3788: 3785: 3781: 3775: 3772: 3770: 3767: 3765: 3762: 3760: 3757: 3755: 3752: 3750: 3747: 3745: 3742: 3740: 3737: 3735: 3732: 3731: 3729: 3725: 3719: 3716: 3714: 3711: 3709: 3706: 3704: 3701: 3699: 3696: 3694: 3691: 3689: 3686: 3684: 3681: 3679: 3676: 3674: 3671: 3670: 3668: 3664: 3658: 3655: 3653: 3650: 3648: 3645: 3643: 3640: 3638: 3635: 3633: 3630: 3629: 3626: 3622: 3615: 3610: 3608: 3603: 3601: 3596: 3595: 3592: 3581: 3577: 3572: 3567: 3563: 3559: 3555: 3548: 3545: 3540: 3536: 3531: 3526: 3522: 3518: 3514: 3507: 3504: 3499: 3495: 3490: 3485: 3481: 3477: 3473: 3469: 3465: 3461: 3457: 3449: 3446: 3441: 3437: 3432: 3427: 3423: 3419: 3415: 3411: 3407: 3403: 3399: 3391: 3388: 3382: 3377: 3373: 3369: 3365: 3361: 3354: 3351: 3346: 3342: 3337: 3332: 3328: 3324: 3319: 3314: 3310: 3306: 3302: 3295: 3292: 3287: 3283: 3278: 3273: 3269: 3265: 3261: 3257: 3253: 3249: 3245: 3238: 3235: 3230: 3226: 3221: 3216: 3212: 3208: 3203: 3198: 3194: 3190: 3186: 3179: 3177: 3175: 3173: 3171: 3167: 3162: 3158: 3153: 3148: 3144: 3140: 3136: 3132: 3128: 3124: 3120: 3112: 3109: 3104: 3100: 3095: 3090: 3086: 3082: 3078: 3074: 3070: 3066: 3062: 3055: 3052: 3047: 3034: 3025: 3020: 3016: 3009: 3006: 3001: 2997: 2992: 2987: 2983: 2979: 2975: 2971: 2967: 2963: 2959: 2952: 2949: 2938: 2932: 2930: 2926: 2921: 2917: 2912: 2907: 2903: 2899: 2895: 2891: 2883: 2881: 2879: 2877: 2875: 2873: 2871: 2867: 2862: 2858: 2853: 2848: 2844: 2840: 2836: 2832: 2828: 2824: 2820: 2813: 2811: 2809: 2807: 2805: 2803: 2799: 2794: 2790: 2785: 2780: 2776: 2772: 2768: 2761: 2759: 2757: 2755: 2751: 2746: 2742: 2737: 2732: 2728: 2724: 2720: 2713: 2711: 2709: 2707: 2705: 2703: 2701: 2697: 2692: 2688: 2683: 2678: 2674: 2670: 2665: 2660: 2656: 2652: 2648: 2641: 2639: 2637: 2633: 2628: 2624: 2619: 2614: 2610: 2606: 2601: 2596: 2592: 2588: 2584: 2577: 2575: 2573: 2571: 2567: 2562: 2558: 2553: 2548: 2543: 2538: 2534: 2530: 2526: 2519: 2517: 2515: 2513: 2511: 2509: 2505: 2500: 2496: 2491: 2486: 2482: 2478: 2475: 2468: 2466: 2464: 2462: 2458: 2453: 2449: 2444: 2439: 2435: 2431: 2427: 2423: 2419: 2415: 2411: 2404: 2402: 2400: 2398: 2396: 2394: 2392: 2390: 2386: 2381: 2377: 2372: 2367: 2363: 2359: 2355: 2351: 2347: 2343: 2339: 2332: 2330: 2328: 2326: 2324: 2322: 2320: 2318: 2316: 2312: 2307: 2303: 2299: 2295: 2291: 2287: 2283: 2279: 2275: 2268: 2266: 2264: 2262: 2260: 2258: 2256: 2254: 2250: 2245: 2241: 2236: 2231: 2227: 2223: 2219: 2212: 2210: 2208: 2206: 2204: 2202: 2200: 2198: 2196: 2192: 2187: 2183: 2179: 2175: 2171: 2167: 2162: 2161:10044/1/49937 2157: 2153: 2149: 2145: 2141: 2134: 2127: 2125: 2123: 2121: 2119: 2117: 2115: 2113: 2111: 2109: 2107: 2103: 2098: 2094: 2089: 2084: 2080: 2076: 2071: 2066: 2062: 2058: 2057:Plant Methods 2054: 2047: 2045: 2043: 2041: 2039: 2037: 2035: 2033: 2031: 2029: 2027: 2023: 2018: 2014: 2009: 2004: 2000: 1996: 1991: 1986: 1982: 1978: 1977:Plant Methods 1974: 1967: 1965: 1963: 1961: 1959: 1957: 1955: 1953: 1949: 1944: 1940: 1935: 1930: 1926: 1922: 1918: 1914: 1910: 1906: 1902: 1895: 1892: 1887: 1883: 1878: 1873: 1869: 1865: 1861: 1857: 1853: 1849: 1845: 1838: 1836: 1834: 1830: 1825: 1821: 1816: 1811: 1807: 1803: 1799: 1795: 1791: 1787: 1783: 1779: 1775: 1768: 1766: 1764: 1762: 1760: 1758: 1756: 1754: 1752: 1748: 1743: 1739: 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