Data Source & Processing

Species	BioProject	Count	Species	BioProject	Count
Aristolochia contorta	PRJNA665793	18	Litsea cubeba	PRJNA763042	24
Aristolochia fimbriata	PRJNA656149	34		PRJNA562115	32
Piper nigrum	PRJNA273782	2		PRJNA563187	1
	PRJNA314826	1		PRJNA563183	1
	PRJNA529760	24		PRJNA563180	1
	PRJNA273688	1		PRJNA563179	1
	PRJNA318916	5		PRJNA563173	1
	PRJEB38192	12		PRJNA563174	1
	PRJNA515366	2		PRJNA563176	1
	Total	47		PRJNA563177	1
Liriodendron chinense	PRJNA773130	55		PRJNA563178	1
	PRJNA677852	30		PRJNA563143	1
	PRJNA218529	2		PRJNA563140	1
	PRJNA277997	6		PRJNA563133	1
	PRJNA498177	4		PRJNA563131	1
	PRJNA508825	1		PRJNA563130	1
	PRJNA556244	8		PRJNA563128	1
	PRJNA559687	19		PRJNA563124	1
	Total	125		PRJNA563120	1
Magnolia biondii	CNP0000884	6		PRJNA563116	1
Chimonanthus praecox	PRJNA222632	3		PRJNA389660	13
	PRJNA704246	39		Total	87
	PRJNA600650	45	Annona glabra	PRJNA817364	1
	PRJNA562115	4	Chimonanthus salicifolius	PRJNA602413	30
	PRJEB28568	6	Magnolia officinalis	PRJNA492971	4
	PRJNA492170	6	Cinnamomum kanehirae	PRJNA477266	8
	PRJNA421868	1	Piper longum	PRJNA580359	3
	Total	104	Cinnamomum camphora	PRJNA613766	5
Persea americana	PRJNA541745	30		PRJNA679978	9
	PRJNA562115	4		PRJNA421868	1
	PRJNA551035	27		PRJNA380988	1
	PRJNA655460	24		PRJNA428279	4
	PRJNA660369	12		PRJNA288748	3
	PRJNA282441	1		PRJNA857520	5
	PRJNA412534	7		PRJNA761572	12
	PRJNA483022	12		PRJNA753557	18
	PRJNA803253	6		PRJNA747104	9
	PRJNA754775	36		PRJNA741581	6
	Total	159		Total	73
Saururus chinensis	PRJNA1066966	1	Lindera megaphylla	PRJNA850554	21
	PRJNA717905	3		Total	21
	PRJNA508825	1	Cinnamomum burmannii	PRJNA562115	4
	PRJNA323685	2		PRJNA951776	3
	Total	7		Total	7
Michelia alba	PRJCA008087	21	Phoebe bournei	PRJNA628065	15
	Total	21		PRJNA706207	9
Lindera glauca	PRJNA248248	5		PRJNA793342	18
	PRJNA307646	2		PRJNA802986	6
	PRJNA7339276	5		PRJNA817391	18
	PRJNA977679	9		Total	66
	Total	21
Warburgia ugandensis	PRJEB5301	1
	PRJNA279116	2
	Total	3

Species	BioProject	Count
Magnolia sinica	PRJNA774088	21
Cinnamomum burmanni	CNP0003375	1
Liriodendron chinense	PRJNA418361	14
Persea americana	PRJNA508502	10
	PRJNA758103	20
Cinnamomum kanehirae	CNP0003375	1
	PRJNA814897	1
Cinnamomum camphora	CNP0003375	1
	PRJNA1000241	74
	PRJNA814897	6

Species	File	Source
Chimonanthus salicifolius	Chimonanthus_salicifolius.txt	Proteomics and metabolomics reveal the mechanism underlying differential antioxidant activity among the organs of two base plants of Shiliang tea (Chimonanthus salicifolius and Chimonanthus zhejiangensis)
Cinnamomum camphora	Cinnamomum_camphora.txt	The photo-inhibition of camphor leaves (Cinnamomum camphora L.) by NaCl stress based on physiological, chloroplast structure and comparative proteomic analysis
Chimonanthus praecox	Chimonanthus_praecox_1.txt	Comprehensive analysis of wintersweet flower reveals key structural genes involved in flavonoid biosynthetic pathway
	Chimonanthus_praecox_2.txt	Transcriptomic and proteomic approaches to explore the differences in monoterpene and benzenoid biosynthesis between scented and unscented genotypes of wintersweet. Physiologia plantarum
Liriodendron chinense	Liriodendron_chinense.txt	Morphological and proteomic analysis reveal the role of pistil under pollination in Liriodendron chinense (Hemsl.) Sarg
Magnolia officinalis	Magnolia_officinalis.txt	Comparative transcriptome and proteome profiling of chlorophyll metabolism pathway in four types of Magnolia officinalis
Persea americana	Persea_americana_1.txt	Integration of proteomics and metabolomics data of early and middle season Hass avocados under heat treatment
	Persea_americana_2.txt	Identification of avocado (Persea americana) root proteins induced by infection with the oomycete Phytophthora cinnamomi using a proteomic approach
	Persea_americana_3.txt	Identification of avocado (Persea americana) pulp proteins by nano-LC-MS/MS via combinatorial peptide ligand libraries
	Persea_americana_4.txt	Identification of a new class of lipid droplet-associated proteins in plants
Piper nigrum	Piper_nigrum_1.txt	Transcriptome- Assisted Label-Free Quantitative Proteomics Analysis Reveals Novel Insights into Piper nigrum-Phytophthora capsici Phytopathosystem
	Piper_nigrum_2.txt	Influence of temperature on symptom expression, detection of host factors in virus infected Piper nigrum L

1.Genomic Data Analysis

The basic information on genome assembly and gene were obtained from the genome and annotation files. The transcription factors were predicted by alignment with the iTAK Database (Zheng et al., 2016). The gene functions were predicted by alignment with the PFAM database using HMMER (Mistry et al., 2013; Mistry et al., 2020). The GO and KEGG information was obtained by alignment with the KEGG, or GO databases (Ashburner et al., 2000; Galperin et al., 2021; Kanehisa and Goto, 2000).

2.RNA-seq Data Analysis

The fastp software was used to filter adaptor sequence of transcriptome raw data (Chen et al., 2018). And then clean data were mapped to the corresponding reference genome using Hisat2 (Kim et al., 2015). Subsequently, samtools was used to further used to convert SAM format into BAM format (Li et al., 2009). Finally, the FPKM value of each protein-coding gene were obtained using StringTie software (Pertea et al., 2015).

3.Metabolic Sample Processing and Data Analysis

All samples were extracted as described previously method (Chen et al., 2013). In brief, samples were first freeze-dried and then ground into powder using a mixer mill (MM400, Retsch) for 1.5 min at 30 Hz. Powder (100 mg) was weighed and incubated overnight at 4℃ with 0.8 ml 70% aqueous methanol (methanol: H2O, 70:30, v/v) and pure methanol, respectively, followed by centrifugation for 10 min at 10 000 g. The supernatants were collected separately and combined, followed by filtration (SCAA-104, 0.22 mm pore size; ANPEL Shanghai, China, www.anpel.com.cn/) before LC–MS analysis. To investigate the tissue differences in metabolites, samples were dissected into six different tissues (seed, flower, root, stem, fruit and leaf) for targeted metabolomics analysis. The samples were also equally mixed into multiple quality control samples for testing instrument stability.

The sample extracts were analyzed using an LC-electrospray ionization (ESI)-MS/MS system (high-performance liquid chromatography [HPLC], Shim-pack UFLC Shimadzu LC30AD system, www.shimadzu.com.cn/;MS, Applied Biosystems 6500 QTRAP, www.appliedbiosystems.com.cn/). The effluent and commercial standards were acquired by ESI-triple quadrupole-linear ion trap MS (QTRAP) in MRM-IDA-EPI mode to obtain the chromatographic peaks and MS2. Instrument tuning and mass calibration were performed with 10 and 100 m mol/l polypropylene glycol solutions in QQQ and LIT modes, respectively. The QQQ scans were acquired as MRM experiments with the collision gas (nitrogen) set to 5 psi. The declustering potential (DP) and collision energy (CE) for individual MRM transitions were performed with further DP and CE optimization. A specific set of MRM transitions was monitored for each period according to the metabolites that were eluted within this period. The data recorded were processed with MultiQuant 3.0.3 and Analyst 1.6.3 software.

4.Resequencing Data Analysis

Resequencing raw data were mapped to the corresponding reference genome using BWA (Li and Durbin, 2010). Samtools software was used to convert SAM format into BAM format (Li et al., 2009). The picard software was used to filter duplicate reads (https://broadinstitute.github.io/picard/). And then bcftools was performed to detect variation (Danecek et al., 2021). The SNPs was filtered using the parameters: QUAL > 20 && DP > 4 && MQ > 30) | | (GT="0/0" and the Indels was filtered using the parameters: QUAL > 20 && DP > 4 && MQ > 30) | | (GT="0/0".

Data Source & Processing

1.Species Information

2.Genome and Annotation

3.RNA-seq Data

4.Metabolome Data

5.Resequencing Data

6.Proteome Data

1.Genomic Data Analysis

2.RNA-seq Data Analysis

3.Metabolic Sample Processing and Data Analysis

4.Resequencing Data Analysis

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Data Source & Processing

1.Species Information

2.Genome and Annotation

3.RNA-seq Data

4.Metabolome Data

5.Resequencing Data

6.Proteome Data

1.Genomic Data Analysis

2.RNA-seq Data Analysis

3.Metabolic Sample Processing and Data Analysis

4.Resequencing Data Analysis