Tracheophyta

To evaluate the working efficiency and assembly success, we selected 50 datasets of vascular plants with raw reads from the GenBank Sequence Reads Archive (SRA) (Additional file 2: Table S1). The 50 vascular plants represented 42 species of angiosperms (from eight major clades, 21 orders and 29 families), four species of gymnosperms, three species of ferns, and one species of lycophytes. Notably, the raw reads of these 50 samples are associated with published plastomes [56 (link)–59 (link)], allowing comparison with newly reassembled plastome using GetOrganelle. Since 2018, NOVOPlasty has received more than 400 citations for assembly chloroplast genome in Google Scholar (accessed 31 Dec 2019) and became one of the most widely used tools for plastome assembly. We thus reassembled 50 samples using NOVOPlasty for comparisons.
The data resources are paired-end reads. The read length varied from 100 to 300 bp (Additional file 2: Table S1). In all tests, if the tested data included fewer than 10,000,000 reads for each end, we used all the reads; if the data included more than 10,000,000 reads of each end, we only select the first 10,000,000 reads for each end. We set up four testing groups, i.e., three groups with different word size values (w = 0.6, 0.7, 0.8) (i.e., GetOrganelle-W0.6, GetOrganelle-W0.7, GetOrganelle-W0.8) and an auto-estimated word size group (i.e., GetOrganelle-auto). The extension rounds of all tests were set to 10. All other options including the seed were set to default. Because incomplete assemblies are unsuitable for comparing mapping qualities in the next part, we additionally added extra runs for eight samples, in which GetOrganelle-auto could not achieve complete plastomes, with customized options (GetOrganelle-customized) for mapping quality comparison. A detailed record of commands, as well as the final results and log files recording the memory usage and time cost of all the tests are available at https://github.com/Kinggerm/GetOrganelleComparison (version 1.1.1).
Plastomes from the same 50 datasets were also reassembled by NOVOPlasty using four k-mer values, i.e., 23, 31, 39, and 47. The config file of NOVOPlasty was downloaded from the NOVOPlasty GitHub repository (https://github.com/ndierckx/NOVOPlasty/blob/master/config.txt), with “Type” as “chloro,” “Genome Range” as 15,000–180,000, “Save assembled reads” as “yes,” “Seed Input” as the same seed as running GetOrganelle, and “Read Length” as the mean read length of each sample (seed Additional file 2), with all other parameters unchanged.

Genotypic data was simulated consisting of 511 SNP markers in 40 inbred lines belonging to two heterotic groups (20 in each). Phenotypic data was simulated consisting of grain yield (GY) and plant height (PH) for the 40 parents and 100 out the 400 possible hybrids produced from the single-cross of both heterotic groups allowing for heterosis. Genotypes of the 40 parents were used to estimate the genomic relationship matrices as K = ZZ’/2Σp_i(1-p_i) [27 (link)] for both heterotic groups (K₁ and K₂), and the genomic relationship matrix for the 400 possible hybrids was obtained as the Kronecker product of the parental genomic relationship matrices K₁ ⊗ K₂ (K₃). Given that the phenotypic data for the possible crosses was not masked, the hybrids were predicted by estimating the BLUPs for general combining abilities in males and females (GCA_female, GCA_male) and specific combining abilities (SCA) of crosses along with their variance components (σ²_GCA1, σ²_GCA2, σ²_SCA). The model has the form:
$y=\mathit{X}\beta +{\mathit{Z}}_{\mathbf{1}}{u}_{GCA1}+{\mathit{Z}}_{\mathbf{2}}{u}_{GCA2}+{\mathit{Z}}_{\mathbf{3}}{u}_{SCA}+\epsilon$
The mixed model equations for this model are:
${\left[\begin{array}{cccc}\mathit{X}\prime {\mathit{R}}^{-1}\mathit{X}& \mathit{X}\prime {\mathit{R}}^{-1}{\mathit{Z}}_1& \mathit{X}\prime {\mathit{R}}^{-1}{\mathit{Z}}_2& \mathit{X}\prime {\mathit{R}}^{-1}{\mathit{Z}}_3\\ {\mathit{Z}}_1\prime {\mathit{R}}^{-1}\mathit{X}& {\mathit{Z}}_1\prime {\mathit{R}}^{-1}{\mathit{Z}}_1+{\mathit{G}}_1^{-1}& {\mathit{Z}}_1\prime {\mathit{R}}^{-1}{\mathit{Z}}_2& {\mathit{Z}}_1\prime {\mathit{R}}^{-1}{\mathit{Z}}_3\\ {\mathit{Z}}_2\prime {\mathit{R}}^{-1}\mathit{X}& {\mathit{Z}}_2\prime {\mathit{R}}^{-1}{\mathit{Z}}_1& {\mathit{Z}}_2\prime {\mathit{R}}^{-1}{\mathit{Z}}_2+{\mathit{G}}_2^{-1}& {\mathit{Z}}_2\prime {\mathit{R}}^{-1}{\mathit{Z}}_3\\ {\mathit{Z}}_3\prime {\mathit{R}}^{-1}\mathit{X}& {\mathit{Z}}_3\prime {\mathit{R}}^{-1}{\mathit{Z}}_1& {\mathit{Z}}_3\prime {\mathit{R}}^{-1}{\mathit{Z}}_2& {\mathit{Z}}_3\prime {\mathit{R}}^{-1}{\mathit{Z}}_3+{\mathit{G}}_3^{-1}\end{array}\right]}^{-1}\left[\begin{array}{c}\mathit{X}\prime {\mathit{R}}^{-1}y\\ {\mathit{Z}}_1\prime {\mathit{R}}^{-1}y\\ {\mathit{Z}}_2\prime {\mathit{R}}^{-1}y\\ {\mathit{Z}}_3\prime {\mathit{R}}^{-1}y\end{array}\right]=\left[\begin{array}{c}\beta \\ u_{GCA1}\\ u_{GCA2}\\ u_{SCA}\end{array}\right]$
where β is the vector of fixed effects, u_GCA1, u_GCA2, u_SCA are the BLUPs for GCA_female, GCA_male, and SCA effects, X and Zs are incidence matrices for fixed and random effects respectively, R is the matrix for residuals (here Iσ²_e), and G^-1₁, G^-1₂, G^-1₃ are the inverse of the variance-covariance matrices for random effects. The BLUPs u_GCA1, u_GCA2, u_SCA were used to predict the rest of the single-crosses as the sum of their respective GCA and SCA effects.
We fitted this model using the sommer package by specifying the incidence and variance-covariance matrices and using the AI and EM algorithms, given that EMMA method cannot estimate more than one variance component. The model could not be implemented in rrBLUP which is also limited to a single variance component. In the BGLR package the Reproducing kernel Hilbert space [RKHS] kernel was used, in ASReml and MCMCglmm the ‘ginverse’ argument was used to specify the variance-covariance structures, and in the regress package the multiple random effects model using the ZKZ’ kernel was fitted. EMMREML uses a similar syntax than sommer. Results from other software were compared with sommer. In addition, a five-fold cross validation was performed to calculate the prediction accuracy for plant height and grain yield in this population.
In order to show the advantage of fitting a model including dominance (SCA) compared to a pure additive models (GCA) with respect to the prediction ability for species displaying heterotic effects, two additional models were fitted including only GCA effects; 1) both parents having the same variance component and 2) each parent from a different heterotic group having a different variance component:
$\mathit{G}=\left[\begin{array}{c}{\mathit{K}\sigma }_u^2\end{array}\right]\mathrm{a}\mathrm{n}\mathrm{d}\mathit{G}=\left[\begin{array}{cc}{\mathit{K}}_1{\sigma }_{u1}^2& 0\\ 0& {\mathit{K}}_2{\sigma }_{u2}^2\end{array}\right]$
Models were compared with respect to their prediction ability after 500 runs of a five-fold cross validation for plant height and grain yield. Models were fitted using sommer with the default AI algorithm. In addition, heritability for both trait was estimated as; h² = (σ²_GCA1 + σ²_GCA2) / (σ²_GCA1 + σ²_GCA2 + σ²_e).

This study selected five key traits related to aspects of the ecological strategies of riparian plants and provided information on functional characteristics that environmental filters could potentially select (Brice et al., 2017 (link)). These functional traits include dispersal type, growth form, life cycle, shoot height, and flowering phenology (Yi et al., 2020 (link); Table S2). These traits have proven to be ideal indicators for revealing plant functional characteristics in the TGRR (Yi et al., 2020 (link)). This research established a list of 166 species belonging to 43 families (Table S3). We then used the latest mega-phylogeny of seed plants as a backbone to construct species-level phylogenetic trees using the “V.PhyloMaker” package, which comprises 74,533 vascular plants (Smith and Brown, 2018 (link); Jin and Qian, 2019 (link)), comprising 166 species from the present investigation.

We followed extraction procedures for LC-MS originally established by Böttcher et al. (Böttcher et al., 2009 (link)) for vascular plants and modified slightly by Peters et al. for bryophytes (Peters, Gorzolka, et al., 2018 (link)). This method was observed to give robust results regarding our targeted compound classes (Lu et al., 2021 ). Frozen plants were homogenized and extracted with 1 mL of cold 80:20 MeOH:H₂O supplemented with 5 µM Kinetin (Sigma), 5 µM Biochanin A (Sigma), and 5 µM N-(3-Indolylacetyl)-L-alanine (Sigma). For LC-MS analysis, samples were reconstituted to 10 mg fresh weight/100 µL with 80:20 MeOH:H₂O.