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. 2019 May 20;6:66. doi: 10.1038/s41597-019-0082-x

Transcriptomic profiles of 33 opium poppy samples in different tissues, growth phases, and cultivars

Yucheng Zhao 1, Zhaoping Zhang 2, Mingzhi Li 3, Jun Luo 1, Fang Chen 2, Yongfu Gong 2, Yanrong Li 2, Yujie Wei 2, Yujie Su 2, Lingyi Kong 1,✉
PMCID: PMC6527585  PMID: 31110243

Abstract

Opium poppy is one of the most important medicinal plants and remains the only commercial resource of morphinan-based painkillers. However, little is known about the regulatory mechanisms involved in benzylisoquinoline alkaloids (BIAs) biosynthesis in opium poppy. Herein, the full-length transcriptome dataset of opium poppy was constructed for the first time in accompanied with the 33 samples of Illumina transcriptome data from different tissues, growth phases and cultivars. The long-read sequencing produced 902,140 raw reads with 55,114 high-quality transcripts, and short-read sequencing produced 1,923,679,864 clean reads with an average Q30 rate of 93%. The high-quality transcripts were subsequently quantified using the short reads, and the expression of each unigene among different samples was calculated as reads per kilobase per million mapped reads (RPKM). These data provide a foundation for opium poppy transcriptomic analysis, which may aid in capturing splice variants and some non-coding RNAs involved in the regulation of BIAs biosynthesis. It can also be used for genome assembly and annotation which will favor in new transcript identification.

Subject terms: Secondary metabolism, Plant genetics

Design Type(s) transcription profiling design • sequence annotation objective • organism part comparison design
Measurement Type(s) transcription profiling assay
Technology Type(s) RNA sequencing
Factor Type(s) cultivar • developmental stage • organism part
Sample Characteristic(s) Papaver somniferum • root • stem • leaf
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Machine-accessible metadata file describing the reported data (ISA-Tab format)

Background & Summary

Opium poppy (Papaver somniferum) is one of the most important medicinal plants in the world and remains the only commercial resource of morphinan-based painkillers1. Its main active ingredient, BIAs, also displays potential pharmacological activity in relieving cough, muscle relaxation, anticancer, and so on2,3. Although approximately 100,000 hectares (ha) of opium poppy are cultivated annually worldwide, this insufficient to meet the demand for managing moderate or severe pain1,4. Engineered microbes could be used to produce BIAs such as opiates and noscapine5–7, however, major hurdles, such as low yield and unclear biosynthetic pathways, make it different to scale-up this method of production for most BIAs. Hence, how to guarantee the source of BIAs to meet the medical applications, such as in pain relief or palliative care, has been recognized as a major issue that needs to be resolved.

Investigation into the BIA biosynthesis mechanism began in the 1960s with radiotracer technology3. The emergence of transcriptomics, proteomics, and metabolomics, coupled with recent genome analysis tools, accelerated the discovery of new BIA biosynthetic genes that could facilitate metabolic engineering reconstitution of commercial source of valuable BIAs in microbes1,5,7–11. However, some key steps in the BIA biosynthesis pathway are yet to be identified, and there has been a lack of investigation into the molecular mechanisms of gene regulation in the pathway3. In addition, there are few reports major in the processes of BIAs biosynthesis, such as compound dynamic accumulation, tissue-specific distribution, enzyme interactions and metabolism, compartmentalization and transport3. Therefore, investigation into the regulation mechanism and understanding the compound accumulation process is a key way to improve the yield of BIAs.

Of all the strategies in metabolic regulation, over-expression and silencing of genes involved in the metabolism of target compounds are the most widely used method. However, non-coding RNAs, alternative splicing/translation/polyadenylation (AS/AT/APA), formation of heterodimers, and gene fusion have also been shown to increase the flexibility of the transcriptome, functional complexity of plants, and the trend of metabolic flow, tissue-specific accumulation or product yield8,9,12,13. However, little research has been conducted on the regulatory mechanisms in opium poppy despite the recently release of its genome1. The reason for this oversight may be the lack of the transcriptional information on opium poppy in different growth periods/status or tissues. In addition, the short sequences created by third-generation sequencing and the gene information in DNA standard could not capture the AS/AT/APA of transcripts1,3.

Third-generation single-molecule real-time (SMRT, Pacific Biosciences) sequencing has increasingly been used to detect AS/AT/APA, to identify novel isoforms, to predict non-coding RNA, and for gene fusion studies due to its long reads14,15. However, to date, there has been no report on a full-length transcriptome dataset of opium poppy, despite the recent release of its genome1. Herein, we construct the full-length transcriptome dataset of opium poppy using twenty-one pooled RNA samples from three different tissues and five different growth phases (Fig. 1). This produced 902,140 post-filter polymerase reads and 660,418 circular consensus sequence (CCS) reads (Table 1 and Fig. 2). After data processing, 566,746 full-length (FL) reads, 180,511 non-redundant isoforms and 61,856 unigenes (59,144 protein-coding unigenes and 2,712 non-coding unigenes) were obtained for functional annotation (Fig. 2). In addition, a total of 1,923,679,864 clean, paired-end short reads were produced (Fig. 3 and Table 2). Gene expression levels were then determined using RSEM and converted into fragments per kb per million fragments (FPKM) value (Fig. 4)16,17. The dataset reported here, provides an overview of the gene expression levels, AS/AT/APA, and full-length transcript/unigene/mRNA of the opium poppy during key statuses. It can also be used to analyze the regulation mechanisms of BIAs biosynthesis according to its tissue-specific distribution, dynamic accumulation in different growth phases as well as BIAs diversity in different germplasm resources.

Fig. 1.

Fig. 1

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Overview of the experimental design and the data processing pipeline. This study consisted of experimental design, RNA isolation, sequencing and data processing, annotation, and analysis, all of which are marked with different colors. Samples were divided into different growth phases, tissues, and cultivars. The numbers 1–5 indicate the five growth phases of opium poppy. Samples in the filling stage were used for either growth phases or tissues and cultivars. All 33 samples were subject to Illumina sequencing, and only samples from B1 were used for PacBio Sequel sequencing.

Table 1.

Summary of post-filter polymerase reads of long-read sequencing.

Cell Name Polymerase Bases Polymerase reads Mean polymerase reads length N50 Mean insert length Subreads Bases Subreads Mean subreads length Subreads N50
Cell 1 7,134,964,542 290,143 24,591 42,250 3,192 6,896,883,917 3,684,400 1,871.92 2,604
Cell 2 5,637,070,764 271,411 20,770 38,750 3,047 5,481,138,738 2,865,188 1,913.01 2,663
Cell 3 6,993,388,572 340,586 20,533 36,250 3,036 6,700,617,834 3,825,257 1,751.68 2,450
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Fig. 2.

Fig. 2

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Output and quality assessment of the SMRT data. (a) Passes vs. read length. (b) Read length of inserts in three cells. (c) Read number of each type of read in three cells. (d) Density of full-length non-chimeric reads in three cells. (e) Length and number of mRNA without redundancy. (f) Venn graph of annotation.

Fig. 3.

Fig. 3

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Correlation analysis of repeated samples and saturation curve of transcripts and genes. (a) Correlation analysis of repeated samples. Heatmap displaying the similarities among all samples based on Poisson distances. (b) Saturation curve of transcripts and genes. The saturation curve shows with the incense of FL reads, the number of genes tends to remain flat, while the number of transcripts rises rapidly.

Table 2.

Statistics of Illumina-based RNA-seq data and quantification of gene expression.

Sample Index Clean Bases Q30 Rate (%) RPKM
0–0.1 0.1–3.75 3.75–15 >15
131 TTAGGC 8713642719 95 16,032 29,642 9,636 6,546
132 TGACCA 9564232510 96 16,532 29,732 9,285 6,307
133 ACAGTG 8107787693 96 16,599 29,566 9,314 6,377
231 GCCAAT 9856735582 96 14,703 28,829 11,103 7,221
231 CAGATC 9135096761 96 14,664 28,591 11,303 7,298
233 ACTTGA 8796177410 96 14,738 27,346 11,713 8,059
331 GATCAG 9098543425 96 16,710 29,501 9,491 6,154
332 TAGCTT 10710694418 96 15,936 29,443 9,956 6,521
333 GGCTAC 9433383049 96 15,573 29,564 10,177 6,542
431 CTTGTA 8512385396 96 15,180 28,368 10,846 7,462
432 AGTCAA 8863916114 96 15,682 27,710 10,627 7,837
433 AGTTCC 9205308102 96 14,991 27,757 11,208 7,900
511 TGACCA 7080192660 94 15,480 26,114 12,024 8,238
512 ACAGTG 6668054022 96 15,392 26,004 11,811 8,649
513 GCCAAT 8814911815 96 15,470 28,361 10,500 7,525
521 CAGATC 8857730898 96 15,831 26,107 11,330 8,588
522 ACTTGA 8354907591 96 16,108 26,313 10,792 8,643
523 GATCAG 6919453352 96 17,249 28,572 8,458 7,577
531 TAGCTT 8282182945 96 15,611 28,124 10,408 7,713
532 GGCTAC 6896148722 95 15,771 28,693 9,966 7,426
533 CTTGTA 8631197062 95 14,860 28,393 10,683 7,920
B2-31 AGTCAA 8664717417 88 15,916 27,833 10,396 7,711
B2-32 AGTTCC 9101628243 90 16,114 27,576 10,248 7,918
B2-33 ATGTCA 7740678568 86 15,292 28,381 10,548 7,635
T31 CCGTCC 6488485876 95 16,674 27,689 10,025 7,468
T32 GTCCGC 8853003315 89 16,820 27,750 9,858 7,428
T33 GTGAAA 8213957582 90 16,707 27,532 10,081 7,536
Z31 GTGGCC 9127832844 89 16,843 27,659 9,898 7,456
Z32 GTTTCG 9417530235 87 16,940 28,042 9,500 7,374
Z33 CGTACG 9140750089 88 16,660 27,983 9,770 7,443
G31 GAGTGG 9615912781 89 16,337 27,959 10,022 7,538
G32 ACTGAT 9595710378 90 16,519 27,960 9,823 7,554
G33 ATTCCT 9773818624 90 16,086 28,398 9,997 7,375
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Fig. 4.

Fig. 4

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Example of differential gene expression analysis and the enrichment in GO between B2 and ZB. (a) GO classification map of differential expression genes. (b) Statistics of GO enrichment. (c) Volcano map of differential expression genes.

Methods

Plant material and experimental design

All the opium poppy for this study was cultured in our experimental plot. The original cultivar was named B1 and represents the original state of opium poppy, known as wild opium poppy. Other germplasm resources resulting from directed breeding in our institute were named B2, T, G and Z. Their characteristics and major BIAs content are summarized in Tables 3 and 4, respectively. Samples were divided into different tissues, cultivars, and growth phases (Fig. 1). Samples from different tissues in B1 (root, stem, and leaves) and germplasm resources (B1, B2, T, G, ZB) were collected during filling stage on Jul 4, 2017. Samples in different growth phases (seedling, jointing, bolting, florescence, and filling) were collected from March to July of 2017. For each sampling point (eleven in all), three independent samples were collected. Unless otherwise mentioned, samples collected from opium poppy are all leaves. This produced 33 samples in all and their definitions of sampling are also listed in Table 3. The leaves from B1 at the filling stage were used as a shared sample for subsequent analysis: they were used as the filling stage sample when studying the growth rhythm of opium poppy, and also as a leaf sample in different tissues or a B1 sample in different cultivars. After washing and cleaning, the samples were immediately inundated with liquid nitrogen for 10 minutes and then stored in −80 °C freezers until use.

Table 3.

Description and definition of the main characteristics of opium poppy in each germplasm lines, growth phase, and tissue.

Groups Sample Description
Germplasm lines B1 Four white petals; white seed; plant height up to 115 cm and have an average leaves of 15; white flower with only one fruit
B2 Two green sepals with 4–6 white petals; multi-branching; plant height up to 106 cm and usually have 13 leaves; white seed
T It has an average plant height of 110 cm and 12 leaves; White flower with purple spots; juice in white and filament in white; multi-branching which could produce at least three fruit
Z It has an average plant height of 105 cm and 11 leaves; White flower with purple spots; juice in light red and filament in white; multi-branching
G It has an highest plant height of 125 cm; 15 leaves; pink flower and golden anther; grey seed; juice in light red and filament in white
Growth phases Seedling Twenty days after sprouting
Jointing Sixty days after sprouting. In this time, a height of 50 cm stem could be observed
Bolting Seventy days after sprouting. In this time, a small bud was first observed at the tips of the stem
Florescence The date of opium poppy first bloom
Filling stag Fourteen days after falling flowers
Tissues Root The root in underground 5–8 cm part
Stem The middle position of the stem with a length of 3 cm
Leaf The top leaf of opium poppy
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Table 4.

The major BIAs contents in one Kilogram (mg/kg) dry leaves in different germplasm lines at filling stag.

BIAs name B1 B2 G T Z
Morphine 599.9 ± 139.77 273.8 ± 17.68 700.0 ± 193.71 92.4 ± 14.64 807.8 ± 18.93
Codeine 44.7 ± 5.85 50.5 ± 34.67 266.7 ± 97.39 — 197.8 ± 79.43
Norcoclaurine 46.2 ± 13.25 14.9 ± 7.75 101.6 ± 55.20 55.1 ± 9.56 153.8 ± 89.71
Thebaine — — — 16009.2 ± 605.32 —
Scoulerine 632.3 ± 116.43 629.0 ± 183.91 408.0 ± 5.54 107.8 ± 4.53 886.4 ± 222.46
Noscapine 58.3 ± 13.80 — 2021.9 ± 480.39 — —
Papaverine 175.2 ± 44.88 118.9 ± 59.13 — 99.9 ± 12.25 —
Canadine 1150.9 ± 257.82 957.9 ± 203.02 1463. 3 ± 365.45 1641.6 ± 372.12 1021.3 ± 164.90
Sanguinarine 507.8 ± 23.82 417.4 ± 17.56 405.8 ± 9.41 — —
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— Is represented as the content of BIAs could not be detected. All data are represented as mean ± SD from three independent plants (n = 3). The unit is mg/kg dry leaves.

RNA extraction, library preparation, and sequencing

For each sample, TRIzol Reagent (Tiangen Biotech Co., Ltd., Beijing, China) was used to extract RNA following the protocol provided by the manufacturer. After cDNA synthesis, samples were subjected to phosphorylation, “A” base addition, and end-repair according to library construction protocol. Sequencing adapters were then added to both sizes of the cDNA fragments. After PCR amplification of cDNA fragments, the 150–250 bp targets were cleaned up. We then performed paired-end sequencing on an Illumina HiSeq X Ten platform (Illumina Inc, CA, USA) (Fig. 1). For PacBio Sequel sequencing, RNA from 21 samples of B1 was mixed in equal amounts for reverse transcription using the Clontech SMARTer PCR cDNA Synthesis Kit (TaKaRa, Dalian, China). In order to determine the optimal amplification cycle number for the downstream large-scale PCR reactions, PCR cycle optimization was employed (PrimeSTAR® GXL DNA polymerase). Then the optimized cycle number was used to generate double-stranded cDNA. Large-scale PCR was performed for SMRTbell library construction (Pacific Biosciences). This include DNA damage repair, end repair, ligating sequencing adapters and removing fragments that failed to connect. Finally, the SMRTbell template was annealed to the sequencing primer, bound to polymerase, and sequenced on the PacBio Sequel platform using V2.1 chemistry (Pacific Biosciences) with 10-hour movies (Fig. 1).

Data filtering, processing and yield

After sequencing, the raw reads were classified and clustered into a transcript consensus using the SMRT Link 5.1 pipeline (http://www.pacb.com/products-and-services/analytical-software/smrt-analysis/). After adaptor removal and elimination of low quality regions, we obtain 902,140 post-filter polymerase reads (19.77 GB) with an average length of 22 kb (Fig. 2 and Table 1). In order to improve the accuracy of sequencing, CCS reads were extracted from the subreads BAM file, which produced a total of 660,418 CCS reads with an average insert length of 2.61 kb (Fig. 2a,b). Briefly, CCS reads were extracted out of subreads.bam file with minimum full pass of 1 and a minimum read score of 0.8. CCS reads were then classified into full-length (FL) non-chimeric (NC), non-full-length (NFL), chimeras (C), and short reads based on cDNA primers and poly-A tail signal. Reads shorter than 50 bp were discarded. CCS reads with 5′ primer, 3′ primer and polyA tails were identified as FL reads, and 566,746 FL sequences ranging from 300 bp to 25,247 bp were obtained (Fig. 2c,d). Subsequently, the full-length non-chimeric (FLNC) reads were clustered by Iterative Clustering for Error Correction (ICE) software to generate the cluster consensus isoforms18. NFL reads were used by Arrow software to polish the obtained cluster consensus isoforms to obtain the final 55,114 FL polished high quality consensus sequences (accuracy ≥ 99%)18. Lordec was used to correct FL transcripts and CD-HIT was used to remove redundant sequences according to sequence similarity of high-quality transcripts19,20. Finally, 180,511 non-redundant isoforms and 61,856 unigenes were obtained for functional annotation (Fig. 2e,f). For Illumina paired-end RNA-seq, the low-quality reads (reads containing sequencing adaptors, reads containing sequencing primers, nucleotide with q quality score lower than 20) were removed. After that, a total of 1,923,679,864 clean, paired-end reads were produced (Table 2 and Fig. 3a). Illumina clean data was mapped onto our SMRT sequencing data using hisat2 v2.0521. RSEM was used to identify gene expression levels, which were then converted into an FPKM value16,17. DESeq R package was used to analyze differential expression22. Fold change ≥2 and adjusted P-value < 0.05 were set as threshold for significance of gene expression differences between the two samples (Fig. 4).

Major BIAs content measurement

To analysis the major BIAs content, some 0.5 g dry leaves in each sample of different germplasm lines at filling stag were employed according to our previous publicized method23. For details, the sample was sequentially extracted three times in methanol, with the help of ultrasonication, for 30 min at room temperature. Then, methanol extracts were combined and concentrated under reduced pressure conditions to a volume of 2 mL. At last, 10 μL concentrated extracts was subject to analysis using HPLC HPLC equipped with a reversed phase C18 column (XDB-C18, 5 mm; Agilent, USA) according to our publicized method. The BIAs content was show as weight (mg) in one Kilogram dry leaves (mg/kg) and listed in Table 4, and its raw data of individual measurements are available at Figshare24.

Functional annotation

Functional annotations of the novel genes were performed using BLAST searching against public databases such as Swiss-Prot, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomics)25,26.

Data Records

The Illumina HiSeq X Ten data (different growth phases, cultivars and tissues) and PacBio Sequel sequencing data have been submitted to the Sequence Read Archive (SRA) of NCBI under accession numbers SRP17355127, SRP17356528, SRP17354629, and SRP17372830, respectively (Table 5). The functional annotation and gene expression (RPKM) information of high-quality transcripts and unigenes are deposited in Figshare and Gene Expression Omnibus (GEO) in NCBI24,31. The differential gene expression data among different samples was also deposited in Figshare24.

Table 5.

Metadata and description of each of the 33 samples that were sequenced.

Groups Study Biosample Sample title Accession Description
Growth phases SRP173551 SAMN10600731 131 SRR8325944 Seedling leaf from B1
132 SRR8325943 Seedling leaf from B1
133 SRR8325942 Seedling leaf from B1
231 SRR8325941 Jointing leaf from B1
231 SRR8325940 Jointing leaf from B1
233 SRR8325939 Jointing leaf from B1
331 SRR8325938 Bolting leaf from B1
332 SRR8325937 Bolting leaf from B1
333 SRR8325946 Bolting leaf from B1
431 SRR8325945 Florescence leaf from B1
432 SRR8325936 Florescence leaf from B1
433 SRR8325935 Florescence leaf from B1
Tissues SRP173546 SAMN10600614 511 SRR8325831 Filling stag root from B1
512 SRR8325832 Filling stag root from B1
513 SRR8325829 Filling stag root from B1
521 SRR8325830 Filling stag stem from B1
522 SRR8325827 Filling stag stem from B1
523 SRR8325828 Filling stag stem from B1
531 SRR8325825 Filling stag leaf from B1
532 SRR8325826 Filling stag leaf from B1
533 SRR8325833 Filling stag leaf from B1
Cultivars SRP173565 SAMN10601491 B2-31 SRR8327183 Filling stag leaf from B2
B2-32 SRR8327182 Filling stag leaf from B2
B2-33 SRR8327181 Filling stag leaf from B2
SAMN11104145 T31 SRR8327180 Filling stag leaf from T
T32 SRR8327187 Filling stag leaf from T
T33 SRR8327186 Filling stag leaf from T
SAMN11104146 Z31 SRR8327185 Filling stag leaf from Z
Z32 SRR8327184 Filling stag leaf from Z
Z33 SRR8327178 Filling stag leaf from Z
SAMN11104144 G31 SRR8327177 Filling stag leaf from G
G32 SRR8327176 Filling stag leaf from G
G33 SRR8327179 Filling stag leaf from G
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Technical Validation

RNA quality control

The integrity of RNA sample was determined using the Agilent 2100 Bioanalyzer (Agilent Technologies, USA) and agarose gel electrophoresis. The purity and concentration of RNA samples were determined with the Nanodrop microspectrophotometer (Thermo Fisher Scientific, USA). For Illumina sequencing, 33 high-quality RNA samples (OD260/280 = 1.7~2.3, OD260/230 ≥ 2.0, RIN ≥ 7) were used to construct the sequencing library. The OD260/280, OD260/230, and RIN values for all RNA samples are listed in Table 6.

Table 6.

RNA sample quality used in this study.

Sample 260/280 260/230 RIN 28 s/18 s
131 2.20 2.45 7.8 1.5
132 2.20 2.45 7.9 1.5
133 2.16 2.40 8.0 1.5
231 2.18 2.44 8.7 1.2
231 2.19 2.42 8.7 1.4
233 2.17 2.40 9.4 1.5
331 2.18 2.41 7.8 1.4
332 2.18 2.39 8.4 1.5
333 2.19 1.80 7.3 1.3
431 2.18 2.22 7.5 1.4
432 2.19 2.19 6.9 1.4
433 2.18 2.02 7.2 1.5
511 2.16 2.41 10 2.0
512 2.14 2.13 10 2.1
513 2.12 2.08 10 2.4
521 2.13 2.26 9.9 1.9
522 2.12 1.61 10 3.0
523 2.12 2.35 9.8 2.0
531 2.18 2.23 8.4 1.7
532 2.19 2.17 8 1.6
533 2.21 2.12 7.3 1.4
B2-31 2.18 2.45 7.2 1.4
B2-32 2.17 2.33 7.7 1.5
B2-33 2.18 2.35 7 1.3
T31 2.19 2.48 7.9 1.6
T32 2.18 2.46 7.4 1.5
T33 2.19 2.44 8 1.5
Z31 2.19 2.40 7.9 1.5
Z32 2.18 1.79 7.5 1.6
Z33 2.19 2.47 7.4 1.5
G31 2.17 2.40 8 1.7
G32 2.14 2.35 8.1 1.8
G33 2.14 2.45 7.9 1.7
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Quality evaluation of raw data

A high quality region finder was used to identify the longest region of a singly-loaded enzyme using a signal-to-noise ratio of 0.8 to filter out low-quality areas. In order to improve the accuracy of sequencing, the same polymerase reads were read multiple times in a closed loop, and the random error correction was then performed on the sequence read multiple times by the same insert fragment. This produced 660,418 CCS reads with a mean of 12.62 passes per read (Fig. 2a,b). FL reads were classified based on the location of and relationships between the 5′ primer, 3′ primer and polyA tail (Fig. 2c,d).

Assessment of sample composition

For Illumina paired-end RNA-seq data, we measured the correlation coefficient and quantitative saturation of gene expression among 33 samples (biological repetition and biological variation, Fig. 3). Correlation of expression levels correlation among samples is an important index to test the reliability of experiments and the rationality of sample selection. Correlation coefficients close to 1 (red) indicate that the samples have a high similarity of expression patterns. If there is biological repetition in the sample, the correlation coefficient of biological repetition is usually higher. By calculating the relationship between the number of different full-length transcripts and the number of genes, we can observe whether the total number of genes has been measured up to the saturation level (Fig. 3b).

ISA-Tab metadata file

Download metadata file (4.1KB, zip)

Acknowledgements

This Project was supported by China Postdoctoral Science Foundation (2016M601922, 2018T110577), the National Natural Science Foundation of China (81703637) and Natural Science Fund in Jiangsu Province (BK20170736). This research was also supported by the Program for Changjiang Scholars and Innovative Research Team in University (IRT_15R63), 111 Project from Ministry of Education of China and the State Administration of Foreign Export Affairs of China (B18056).

Author Contributions

Y.Z. and L.K. conceived the project and designed the experiment. Z.Z., M.L., J.L., F.C. and Y.G. performed the majority of the experiment work. Y.L. and Y.W. performed the data analyses. Y.Z., Y.S. and L.K. wrote the manuscript. All authors read the final manuscript.

Code Availability

SMRT Link 5.1 pipeline: http://www.pacb.com/products-and-services/analytical-software/smrt-analysis/. CD-HIT: http://www.bioinformatics.org/cd-hit/ (version 4.6.6). Blast: ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ (version 2.2.31).

Competing Interests

The authors declare no competing interests.

Footnotes

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

ISA-Tab metadata

is available for this paper at 10.1038/s41597-019-0082-x.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Download metadata file (4.1KB, zip)

Data Availability Statement

SMRT Link 5.1 pipeline: http://www.pacb.com/products-and-services/analytical-software/smrt-analysis/. CD-HIT: http://www.bioinformatics.org/cd-hit/ (version 4.6.6). Blast: ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ (version 2.2.31).

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