Skip to main content
NCBI home page
Applications in Plant Sciences logoLink to Applications in Plant Sciences
. 2021 Feb 14;9(2):e11409. doi: 10.1002/aps3.11409

Pilot RNA‐seq data from 24 species of vascular plants at Harvard Forest

Hannah E Marx 1,2, Stacy A Jorgensen 1, Eldridge Wisely 3, Zheng Li 1, Katrina M Dlugosch 1, Michael S Barker 1,✉
PMCID: PMC7910807  PMID: 33680580

Abstract

Premise

Large‐scale projects such as the National Ecological Observatory Network (NEON) collect ecological data on entire biomes to track climate change. NEON provides an opportunity to launch community transcriptomic projects that ask integrative questions in ecology and evolution. We conducted a pilot study to investigate the challenges of collecting RNA‐seq data from diverse plant communities.

Methods

We generated >650 Gbp of RNA‐seq for 24 vascular plant species representing 12 genera and nine families at the Harvard Forest NEON site. Each species was sampled twice in 2016 (July and August). We assessed transcriptome quality and content with TransRate, BUSCO, and Gene Ontology annotations.

Results

Only modest differences in assembly quality were observed across multiple k‐mers. On average, transcriptomes contained hits to >70% of loci in the BUSCO database. We found no significant difference in the number of assembled and annotated transcripts between diploid and polyploid transcriptomes.

Discussion

We provide new RNA‐seq data sets for 24 species of vascular plants in Harvard Forest. Challenges associated with this type of study included recovery of high‐quality RNA from diverse species and access to NEON sites for genomic sampling. Overcoming these challenges offers opportunities for large‐scale studies at the intersection of ecology and genomics.

Keywords: community transcriptomics, NEON, polyploidy, RNA‐seq, transcriptome assembly

Many questions in ecology and evolutionary biology increasingly require combining data from these fields at large scales. In particular, integrated, large‐scale analyses of multispecies ecological and phylogenetic data sets have become critical to understanding plant distributions and responses to climate change (Zanne et al., 2014; Swenson and Jones, 2017; Maitner et al., 2018; Enquist et al., 2019; McFadden et al., 2019; Rice et al., 2019; Baniaga et al., 2020; Gallagher et al., 2020; Román‐Palacios and Wiens, 2020). Recognizing this need, the National Science Foundation (NSF) recently launched the National Ecological Observatory Network (NEON) to generate large‐scale data in areas including species occurrence, phenology, and climate, for ecological communities across the United States (Collinge, 2018; Knapp and Collins, 2019). Metagenomic and genomic sampling are also being used to identify and estimate changes in abundance and composition of some taxa, especially microbial communities (https://www.neonscience.org/data). Although these data and analyses will be crucial for understanding ecosystem‐scale processes, the collection of genomic data from a broader array of species across NEON sites would allow researchers to further integrate ecological and evolutionary processes in the analyses of communities.

Genomic analyses of single species, although important, do not capture the larger patterns occurring within an interacting community of plants. Transcriptome profiling or genome sequencing of multiple species and individuals within a community will open new, integrative avenues of analysis and allow us to address existing questions that require sampling of floras and communities (Bragg et al., 2015; Fitzpatrick and Keller, 2015; Bowsher et al., 2017; Han et al., 2017; Swenson and Jones, 2017; Zambrano et al., 2017; Matthews et al., 2018; Subrahmaniam et al., 2018; Breed et al., 2019). This is especially true for understanding responses to climate change where community‐level analyses are needed to capture the interacting dynamics of different species responses (Liu et al., 2018; Komatsu et al., 2019; Snell et al., 2019). The integration of community‐level genomic data from non‐model species with ecological and trait data will improve our understanding of plant responses to climate change. Collecting genomic data at the community level with repeated sampling that mirrors other trait data collection will permit assessments of the genetic diversity of entire plant communities and how they change over time, estimates of gene flow and hybridization, measurement of in situ gene expression variation across species in response to shared climate events, and a genomic perspective on functional diversity within and between plant communities. Metagenomics analyses of microbiomes have transformed our understanding of and approaches for studying microbial biology (Fierer et al., 2012a, 2012b; Turner et al., 2013; Delgado‐Baquerizo et al., 2018; Jansson and Hofmockel, 2020). Similar plant community transcriptomics and genomics studies could open new avenues of research and provide the crucial data to understand plant responses to climate change.

To explore the potential and challenges of plant community transcriptomics, we conducted a pilot RNA‐seq study at the Harvard Forest NEON site. Whereas many RNA‐seq studies are focused on collections of related species, an approach that simplifies collection and RNA extraction, a major challenge of community‐level transcriptomics is that a diverse range of plant species need to be sampled for RNA extraction in the field. In this pilot study, we evaluated RNA‐seq results generated following a protocol that we developed (Field Setup 2 of Yang et al., 2017) for collecting material at distant field sites and returning samples by shipping. Harvard Forest was selected for this pilot study because of access to a field station that simplified the logistics of working with liquid nitrogen. At Harvard Forest, we sampled 24 species of vascular plants from sites adjacent to the NEON plot. Each species was sampled on two different dates one month apart (in July and August 2016), as close to the same time of day as possible. Species were selected from a phylogenetically diverse range of plants that included ferns, trees, and herbaceous annuals. These plants were selected because they represented the diversity of form and habit that is present in the deciduous forest community at Harvard Forest. Another potential challenge for plant transcriptomics is the abundance of polyploid species and cytotypes (Barker et al., 2016a). With potentially twice as many (or more) genes in a polyploid genome, these species could require more sequencing reads than related diploids to obtain reference transcriptomes of similar quality. To explore the impacts of polyploidy on transcriptome surveys, we made an effort to select sets of related polyploid and diploid species. Here, we give an overview of our data collection, present new reference transcriptomes and translated protein collections for each species, and evaluate the quality of these assemblies using multiple approaches.

METHODS

Taxon selection and sampling

The Harvard Forest Flora (Jenkins et al., 2008) was used to guide our taxonomic selections and find species to represent each category (diploid/polyploid). Putative diploids and neo‐polyploid species were identified from chromosome counts obtained from the Chromosome Counts Database (Rice et al., 2015). Congeneric species pairs were selected based on their phylogenetic relatedness. Our sampling included nine polyploid and 11 diploid species (Table 1). We could not determine the ploidal level of four species. The Harvard Forest Flora Database (Jenkins et al., 2008) was used to locate sampling sites.

TABLE 1.

Summary statistics for RNA‐seq data sets, assemblies with a k‐mer = 57, and translations.

Species Ploidy a Chromosome no. July SRA Aug SRA Total Gbp (July + Aug) No. of scaffolds Mean scaffold length (bp)

Scaffold

N50 (bp)

 No. of translated proteins Mean translated length (nucleic acids, bp) N50 (trans. nucleic acids, bp)
Dryopteris carthusiana P 82 SAMN08277176 SAMN08277204 26 643,129 264.5 710 24,851 473.8 543
Dryopteris intermedia D 41 SAMN08277187 SAMN08277216 22 529,510 267.1 822 22,595 587.1 702
Dryopteris marginalis D 41 SAMN08277188 SAMN08277217 21 550,548 260.1 917 34,121 633.9 789
Galium mollugo D 11 SAMN08277173 SAMN08277201 40 608,764 179.2 1028 25,040 692.9 822
Galium tinctorium D 12 SAMN08277181 SAMN08277210 32 78,487 400.6 1091 16,610 811.2 1023
Galium triflorum P 33 SAMN08277189 SAMN08277219 37 574,562 246 899 38,650 664.9 798
Hypericum perforatum P 16 SAMN08277171 SAMN08277199 25 335,837 233.4 867 34,670 698.6 858
Juglans cinerea D 16 SAMN08277174 SAMN08277202 18.2 569,859 359.5 1151 66,595 712.4 918
Lonicera tatarica var. morrowii NA 9 SAMN08277167 SAMN08277195 10.4 386,927 324.9 1216 28,147 710.9 864
Lysimachia ciliata P 48 SAMN08277190 SAMN08277220 24 1,422,451 207 828 32,005 631.6 777
Lysimachia nummularia D 17 SAMN08277194 SAMN08277223 25.3 428,232 320.9 1102 46,343 716.7 894
Lysimachia quadrifolia P 42 SAMN08277183 SAMN08277212 30 340,491 290.4 944 37,737 674.9 813
Persicaria arifolia NA NA SAMN08277180 SAMN08277209 30 528,292 245 787 28,741 558.8 639
Persicaria hydropiperoides NA NA SAMN08277172 SAMN08277200 21.3 347,558 344 913 46,058 658.8 795
Persicaria sagittata NA 20 SAMN08277179 SAMN08277208 26 398,304 319.6 1148 33,118 725.1 885
Plantago lanceolata D 6 SAMN08277178 SAMN08277206 31 213,834 293.1 1073 20,470 742.7 906
Plantago major D 6 SAMN08277169 SAMN08277197 23 217,041 308.6 1032 24,715 782.6 993
Plantago rugelii P 12 SAMN08277170 SAMN08277198 30 378,418 276.1 1161 30,673 760.5 930
Polygonum cilinode D 11 SAMN08277184 SAMN08277213 17.3 1,065,186 186.8 415 6088 471.6 498
Potentilla argentea P 21 SAMN08277177 SAMN08277207 29 245,734 425.6 1268 16,306 525.5 687
Potentilla canadensis D 14 SAMN08277192 SAMN08277222 16.6 433,249 216.5 667 37,503 526.5 594
Prunus serotina P 16 SAMN08277186 SAMN08277215 31 350,572 267.5 1017 30,812 658.3 774
Prunus virginiana D 8 SAMN08277185 SAMN08277214 38 536,216 235 1110 38,773 678.8 813
Reynoutria japonica P 33 SAMN08277193 SAMN08277224 44 410,810 279.6 870 34,662 549.3 609
Open in a new tab

NA = not available; SRA = Sequence Read Archive.

a

P and D are polyploid and diploid species, respectively.

Field collection for plant RNA‐seq followed the approach described in Yang et al. (2017). The only difference was here we sampled tissue from mature leaves of an apparently healthy individual (e.g., lacking herbivore or pathogen damage) rather than young flower or leaf buds to maintain developmental consistency as much as possible over time. Each target species was sampled from the same population on two different dates about one month apart (July and August) during the 2016 growing season (Fig. 1). We attempted to sample as close to the same time of day as possible on both dates by sampling species in the same order on both trips, but this was not always achievable due to challenges of fieldwork, such as weather and time to relocate sample populations. Leaf tissues were flash‐frozen in liquid nitrogen in the field and shipped on dry ice to the University of Arizona for RNA extraction. After leaf tissue collection, additional leaf tissue was preserved on silica for DNA backup, and the remaining plant material was pressed for a herbarium specimen (see Appendix 1 for voucher information and collection details).

FIGURE 1.

FIGURE 1

Open in a new tab

Workflow of our RNA‐seq collection and assembly.

RNA extraction and RNA‐seq

Total RNA was extracted from leaf tissue collected on each sampling date for all species using the Spectrum Plant Total RNA Kit (Sigma‐Aldrich Co., St. Louis, Missouri, USA) following the manufacturer’s Protocol A. RNA was used to prepare cDNA using the Ovation RNA‐Seq System (catalogue no. 7102‐A01; NuGEN, Redwood, California, USA) via single primer isothermal amplification and automated on the Apollo 324 liquid handler (TaKaRa Bio, Kusatsu, Shiga, Japan). cDNA was quantified on the NanoDrop (Thermo Fisher Scientific, Waltham, Massachusetts, USA) and was sheared to approximately 300‐bp fragments using the Covaris M220 ultrasonicator (Covaris, Woburn, Massachusetts, USA). Libraries were generated using Kapa Biosystem’s library preparation kit for Illumina (KK8201; Roche, Wilmington, Massachusetts, USA). Fragments were end‐repaired and A‐tailed, and individual indexes and adapters (catalogue no. 520999; Bioo Scientific, Austin, Texas, USA) were ligated on each separate sample. The adapter‐ligated molecules were cleaned using AMPure beads (A63883; Agencourt Bioscience/Beckman Coulter, Indianapolis, Indiana, USA) and amplified with the KAPA HiFi enzyme (KK2502; Roche). Each library was then analyzed for fragment size on an Agilent TapeStation 4200 (Agilent Technologies, Santa Clara, California, USA) and quantified by qPCR (KAPA Library Quantification Kit, KK4835) on the QuantStudio 5 Real‐Time PCR System (Thermo Fisher Scientific). Multiplex pooling (13–16 samples per lane) and paired‐end sequencing at 2 × 150 bp were then performed on the Illumina NextSeq500 platform at Arizona State University’s CLAS Genomics Core facility. Raw read quality was assessed using FastQC (Andrews, 2010).

De novo transcriptome assembly, protein translation, and quality assessment

Raw sequence reads were processed using the SnoWhite pipeline (Barker et al., 2010; Dlugosch et al., 2013), which included trimming adapter sequences and bases with a quality score below 20 from the 3′ ends of all reads, removing reads that are entirely primer and/or adapter fragments using TagDust (Lassmann et al., 2009), and removing poly(A/T) tails with SeqClean (https://sourceforge.net/projects/seqclean/). The cleaned reads from each sampling date were merged and cleaned to synchronize read pairs using fastq‐pair (Edwards and Edwards, 2019) and pooled to assemble a reference de novo transcriptome for each species.

Due to the significant time involved in running and evaluating multiple assemblies for each species, we chose five species that represent the phylogenetic diversity of our samples (Dryopteris intermedia (Muhl. ex Willd.) A. Gray, Galium mollugo L., Juglans cinerea L., Plantago major L., and Persicaria sagittata (L.) H. Gross) to identify the optimal k‐mer to use for assembling all 24 species. For these five exemplar taxa, we examined the quality of assemblies generated by SOAPdenovo‐Trans version 1.03 (Xie et al., 2014) across a range of k‐mers (37, 47, 57, 67, 77, 87, 97, 107, 117, and 127). Assembly quality across the different k‐mers was assessed by mapping the raw reads to each assembly with TransRate version 1.0.3 (Smith‐Unna et al., 2016) and evaluating the optimal assembly scores. TransRate calculates assembly scores by remapping the reads back to the assembly and combining a variety of metrics for each contig, including estimates of whether a base pair was called correctly, whether a base should be a part of the final transcript, the probability that a contig was derived from a single transcript, and the probability that a contig is structurally complete. We selected a k‐mer that produced the average highest optimal assembly score across the five species. This k‐mer (57, see Results) was used to assemble reference transcriptomes for the entire collection of species.

We used TransPipe (Barker et al., 2010) to identify plant proteins from the assembled transcripts for each reference transcriptome and provide protein and in‐frame nucleic acid sequences for each species. The reading frame and protein translation for each sequence was identified by comparison to protein sequences from 25 sequenced and annotated plant genomes from Phytozome (Goodstein et al., 2012). Using BLASTX (Wheeler et al., 2008), best‐hit proteins were paired with each transcript at a minimum cutoff of 30% sequence similarity over at least 150 sites. Transcripts that did not have a best‐hit protein at this level were removed. To determine the reading frame and generate estimated amino acid sequences, each transcript was aligned against its best‐hit protein by GeneWise 2.2.2 (Birney et al., 2004). Based on the highest‐scoring GeneWise DNA–protein alignments, stop and “N”‐containing codons were removed to produce estimated amino acid sequences for each transcript. Output included translated protein sequences and their corresponding nucleic acid sequences.

To assess the quality of the assembled transcriptomes for the full set of 24 species, we analyzed each with TransRate and BUSCO. Summary statistics, including the number of scaffolds, mean scaffold lengths, and N50, were calculated by TransRate version 1.0.3 for all scaffolds as well as for the subset of sequences that were identified as plant proteins and translated. We evaluated the completeness of our transcriptome coverage with BUSCO version 4.0.5 (Seppey et al., 2019). BUSCO compares sequences to a collection of universal single‐copy orthologs for the Viridiplantae (Viridiplantae Odb10) and the eukaryotes (Eukaryote Odb10). We also used the TransRate and BUSCO statistics to compare differences in the assemblies of diploid and polyploid species.

Gene Ontology annotation and comparison

Gene Ontology (GO) annotations of all transcriptomes were obtained through translated BLAST (BLASTX) searches against the annotated Arabidopsis thaliana (L.) Heynh. protein database from TAIR (Lamesch et al., 2012) to find the best hit with a length of at least 100 bp and an E‐value of at least 1e‐10. GO‐slim annotations based on the plant GO‐slims from TAIR were obtained for the whole transcriptome for each species and presented as a heatmap. The heatmap columns were clustered by hierarchical clustering with default parameters in R with the order of GO categories set arbitrarily by the ranking in Lysimachia ciliata L. Rankings of the GO slim categories were determined by the relative frequency of the GO term among the transcripts in each transcriptome.

RESULTS

We found relatively little variation in the optimal TransRate scores across assemblies with different k‐mers. The optimal TransRate scores ranged from ~0.1–0.15, with each of the five exemplar species peaking at different k‐mers (Fig. 2). Scores trended downward for all species at higher k‐mers, with no sharp peaks in the score apparent in most taxa. The mean k‐mer of the top‐scoring assemblies for each species was 61, and the closest k‐mer to this value (57) was used to assemble reference transcriptomes for all 24 species.

FIGURE 2.

FIGURE 2

Open in a new tab

TransRate optimal scores for assemblies of five exemplar species from Harvard Forest. A reference transcriptome for each species was assembled with different k‐mers starting at k = 37 and increasing in increments of 10 to k = 127.

Assemblies for most of the 24 species appeared to be of relatively high quality. By combining RNA‐seq libraries representing two different time points, we obtained an average of 27 Gbp of reads for each reference transcriptome (Table 1). With a k‐mer = 57, the assemblies contained an average of 483,084 scaffolds with a mean length of 281 bp and N50 of 960 bp. The translated nucleic acids for each assembly had an average of 31,470 sequences with a mean length of 652 bp and N50 of 789 bp. We observed no significant relationship between the number of scaffolds or number of translated proteins and sequencing depth (Fig. 3). The mean complete plus fragmented BUSCO percentages were 73.2% against the Viridiplantae database and 76% against the eukaryote database (Table 2). We found that the number of hits to sequences in the BUSCO databases plateaued at around 20 Gbp of sequencing effort and around 20,000 proteins (Fig. 4).

FIGURE 3.

FIGURE 3

Open in a new tab

Comparison of the number of (A) scaffolds and (B) translated proteins produced by each assembly with the total number of giga base pairs (Gbp) sequenced for each species.

TABLE 2.

BUSCO results for comparisons to the Viridiplantae and eukaryote databases.

Species BUSCO Viridiplantae database BUSCO Eukaryote database
C (S) (D) F M D/S C+F C (S) (D) F M D/S C+F
Dryopteris carthusiana 19.3 16.9 2.4 40.7 40 0.142 60 24.3 20 4.3 43.5 32.2 0.215 67.8
Dryopteris intermedia 39.8 35.1 4.7 38.4 21.8 0.134 78.2 48.2 44.3 3.9 32.9 18.9 0.088 81.1
Dryopteris marginalis 41.5 34.4 7.1 40 18.5 0.206 81.5 48.6 43.5 5.1 38.4 13 0.117 87
Galium mollugo 27.8 22.6 5.2 50.1 22.1 0.230 77.9 38 32.5 5.5 41.6 20.4 0.169 79.6
Galium tinctorium 40 37.9 2.1 40 20 0.055 80 50.2 46.3 3.9 27.1 22.7 0.084 77.3
Galium triflorum 30.8 21.9 8.9 46.4 22.8 0.406 77.2 33.4 21.6 11.8 50.2 16.4 0.546 83.6
Hypericum perforatum 29.5 22.4 7.1 48.7 21.8 0.317 78.2 38.8 29 9.8 40 21.2 0.338 78.8
Juglans cinerea 33.9 27.8 6.1 48.2 17.9 0.219 82.1 47.8 39.2 8.6 34.1 18.1 0.219 81.9
Lonicera tatarica var. morrowii 34.8 29.4 5.4 46.4 18.8 0.184 81.2 47.1 36.9 10.2 34.5 18.4 0.276 81.6
Lysimachia ciliata 34.1 28.7 5.4 46.1 19.8 0.188 80.2 49 40 9 32.2 18.8 0.225 81.2
Lysimachia nummularia 42.4 35.1 7.3 42.8 14.8 0.208 85.2 48.6 34.9 13.7 37.6 13.8 0.393 86.2
Lysimachia quadrifolia 31.8 26.4 5.4 44.2 24 0.205 76 38 32.5 5.5 39.6 22.4 0.169 77.6
Persicaria arifolia 13.9 10.6 3.3 51.5 34.6 0.311 65.4 24.7 16.9 7.8 46.3 29 0.462 71
Persicaria hydropiperoides 25.4 16.5 8.9 48.9 25.7 0.539 74.3 32.9 18.4 14.5 44.3 22.8 0.788 77.2
Persicaria sagittata 27.8 16.5 11.3 48.7 23.5 0.685 76.5 39.2 20.4 18.8 40.8 20 0.922 80
Plantago lanceolata 40.5 36 4.5 40.5 19 0.125 81 45.9 39.2 6.7 36.1 18 0.171 82
Plantago major 51.3 48 3.3 33.4 15.3 0.069 84.7 54.1 49.8 4.3 29.4 16.5 0.086 83.5
Plantago rugelii 34.5 20.9 13.6 46.6 18.9 0.651 81.1 45.8 27.8 18 39.6 14.6 0.647 85.4
Polygonum cilinode 11.5 9.4 2.1 8.5 80 0.223 20 5.5 4.7 0.8 19.6 74.9 0.170 25.1
Potentilla argentea 11.7 10.8 0.9 33.2 55.1 0.083 44.9 14.1 12.9 1.2 38.4 47.5 0.093 52.5
Potentilla canadensis 12.9 9.6 3.3 47.8 39.3 0.344 60.7 17.3 12.2 5.1 52.9 29.8 0.418 70.2
Prunus serotina 23.5 18.1 5.4 50.4 26.1 0.298 73.9 28.6 20 8.6 47.1 24.3 0.430 75.7
Prunus virginiana 27 18.1 8.9 54.4 18.6 0.492 81.4 37.7 25.5 12.2 44.7 17.6 0.478 82.4
Reynoutria japonica 30.1 22.8 7.3 45.2 24.7 0.320 75.3 30.2 23.1 7.1 45.5 24.3 0.307 75.7
Open in a new tab

C = percentage of all complete BUSCO matches in the respective database (C = S + D where S = percentage of complete and single‐copy BUSCO matches in the respective database and D = percentage of complete and duplicated BUSCO matches in the respective database); F = percentage of fragmented BUSCO matches in the respective database; M = percentage of missing BUSCO matches in the respective database; D/S = ratio of duplicated to single‐copy complete sequences BUSCO matches in the respective database; C+F = percentage of complete and fragmented BUSCO matches in the respective database.

FIGURE 4.

FIGURE 4

Open in a new tab

The percentage of BUSCO complete (C) plus fragmented (F) matches compared to the (A) total giga base pairs (Gbp) sequenced and (B) number of translated proteins in each assembly. Green diamonds represent BUSCO matches to the Viridiplantae database, whereas purple circles represent matches to the eukaryote database.

Polyploid species did not have significantly more translated proteins than diploid species, with 31,152 average proteins translated compared to 30,804 (Fig. 5A; two‐tailed t‐test: P = 0.95). Similarly, polyploid species did not have a significantly higher proportion of duplicated BUSCO matches than diploids (Fig. 5B; two‐tailed t‐test: P = 0.11). In some cases, the number of proteins or duplicated BUSCO proportion was lower when comparing a polyploid species with its related diploids (e.g., Dryopteris Adans.). This may be due to variation in read and/or assembly quality rather than differences in the biology of these species. However, it is not clear that this is due to differences in data quality because in most cases, including Dryopteris, all of the species have similarly high read depth (>20 Gbp).

FIGURE 5.

FIGURE 5

Open in a new tab

Comparison of (A) the number of translated proteins and (B) BUSCO duplicated/single copy (D/S) ratio for assemblies of diploid and polyploid species. In neither case were diploids significantly different from polyploids.

GO annotations of the transcriptomes of the 24 species were largely similar (Fig. 6). Categories such as other cellular processes, other metabolic processes, and other intracellular components were the largest fraction of all transcriptomes, whereas receptor binding or activity and electron transport or energy pathways were among the smallest. The rank order of each GO‐slim category was largely consistent across most species. Species from the same genus were sometimes clustered together by the similarity of their GO‐slim representations, such as in Dryopteris and Lysimachia L., but in most cases the species were not clustered with their congeners. Polygonum cilinode Michx. was unique in having many differences in GO category rank compared to the other taxa. It was also the lowest‐scoring transcriptome assembly, with only 6088 translated proteins and nearly 80% of BUSCO genes missing (Table 2).

FIGURE 6.

FIGURE 6

Open in a new tab

Heat map of Gene Ontology (GO) slim categories present in the entire transcriptome of each species. Each column represents the annotated GO categories from each analyzed transcriptome, whereas the rows represent a particular GO category. The colors of the heat map represent the percentage of the transcriptome represented by a particular GO category, with red being highest and purple lowest. The overall ranking of GO category rows was determined by the ranking of GO annotations in the transcriptome of Lysimachia ciliata. Hierarchical clustering was used to organize the heatmap columns.

DISCUSSION

Overall, the RNA sampling approach we developed and employed (Field Setup 2 of Yang et al., 2017) allowed us to sequence and assemble RNA‐seq data from a diverse range of species at Harvard Forest. The transcriptomes we assembled for 24 species of vascular plants at Harvard Forest appear to be relatively high quality and consistent with our expectations for de novo plant transcriptome assemblies. Our assemblies were reasonably complete, with more than 70% of BUSCO genes present on average. This is a similar distribution of BUSCO scores to those in the recently published 1KP project (Carpenter et al., 2019; One Thousand Plant Transcriptomes Initiative, 2019) and other studies (Blande et al., 2017; Evkaikina et al., 2017; Pokorn et al., 2017; Weisberg et al., 2017). In our analyses, BUSCO scores and scaffold numbers appear to plateau after approximately 20 Gbp of sequencing effort for diploid and polyploid species, but previous studies indicate that reference assemblies of similar quality can be generated with substantially less sequencing effort for high‐quality RNA samples. For example, data from the 1KP project suggest that as little as 2–3 Gbp of read depth is sufficient (Carpenter et al., 2019). Larger amounts of data were collected in this project to facilitate future gene expression analyses. Notably, the few samples that had low BUSCO scores or BUSCO scores that were relatively low for the sequencing effort, such as Polygonum cilinode, also had lower numbers of translated proteins but more scaffolds than most species. The relatively poor quality of these outlier assemblies is likely related to lower RNA quality rather than to sequencing effort or ploidal level. In contrast, assemblies with higher BUSCO scores yield translated protein numbers that are more consistent with the number of genes in sequenced plant genomes (Michael, 2014; Wendel et al., 2016). For example, our transcriptomes of Prunus virginiana L. and P. serotina Ehrh. contained 38,773 and 30,812 translated proteins each. Genomes of related Prunus L. species had similar numbers of annotated genes, including 27,852 in P. persica (L.) Batsch (International Peach Genome Initiative et al., 2013), 41,294 in P. yedoensis Matsum. (Baek et al., 2018), and 43,349 in P. avium (L.) L. (Shirasawa et al., 2017). However, these comparisons should be interpreted cautiously because transcriptome assemblies can contain multiple isoforms of protein‐coding genes. Like many transcriptome assemblies (Johnson et al., 2012; Carpenter et al., 2019; Patterson et al., 2019), our assemblies also contain a large number of small scaffolds (<300 bp). Small scaffolds are likely artifacts of library amplification and sequencing, considering that most did not translate to a known plant protein sequence.

We found no significant difference in the number of translated transcripts between the diploid and polyploid transcriptome assemblies. Although this could be due to the modest sample size or variation in the age and fractionation level of polyploids, it may also reflect biological differences in expressed transcriptome size and diversity that impact the number of assembled transcripts. Under a simple null model of polyploid transcriptome size, one may expect to observe an approximate doubling of the diploid transcriptome size that may translate to doubling the number of assembled transcripts. However, recent research indicates polyploid transcriptomes may be smaller than expected. Research in Glycine Willd. has found that the expressed transcriptome size of polyploid species is less than 2× the diploid size (Coate and Doyle, 2015; Doyle and Coate, 2018; Visger et al., 2019). For example, the transcriptome of the allotetraploid G. dolichocarpa Tateishi & H. Ohashi was 1.4× the size of its diploid progenitors (Coate and Doyle, 2010). The apparent lower‐than‐expected level of the quantity of gene expression in polyploids may be an artifact of comparing diploids and polyploids without accounting for differences in cell numbers or biomass (Coate and Doyle, 2015; Doyle and Coate, 2018; Visger et al., 2019). However, smaller transcriptome sizes in polyploids may also be related to which genes are expressed at a given time or in a particular tissue. This is likely relevant when comparing the assembled gene space for diploid and polyploid transcriptomes, as we do here. Our non‐model reference transcriptomes are built from the expressed genes in each sample rather than being based on a reference genome collection. Thus, only genes and alleles that are expressed will be captured in our assemblies and observed in our comparisons. Not all genes or alleles in a polyploid need to be expressed at one time and the overall diversity of the transcriptome at any given time may look more like a diploid, with other alleles being expressed at different times or tissues. Indeed, differential homoeolog silencing is well characterized in polyploid plants (Adams et al., 2003; Coate and Doyle, 2010) and may reduce the sampled transcript diversity of a polyploid genome. If this is the case, we would expect that sampling across more tissues, development times, and environments would lead to greater sampling of the polyploid gene space. Although RNA spike‐ins and cell counting may improve differential expression analyses (Visger et al., 2019), capturing the full genome diversity of non‐model polyploid species from RNA‐seq assemblies remains an additional challenge.

Our pilot study of RNA‐seq sampling of diverse species in the field demonstrated some familiar challenges. Building on our past experience with extracting RNA from diverse species (Barker et al., 2008; Dempewolf et al., 2010; Der et al., 2011; Lai et al., 2012; Dlugosch et al., 2013; Hodgins et al., 2014; Barker et al., 2016b; Mandáková et al., 2017; Qi et al., 2017; Yang et al., 2017; An et al., 2019; Carpenter et al., 2019), we developed an approach for this study to obtain high‐quality RNA from field samples (Field Setup 2 of Yang et al., 2017). We found that flash‐freezing leaves in liquid nitrogen in situ for later RNA extraction worked well for our diverse samples. A few samples, especially Polygonum cilinode, yielded lower‐quality RNA, which could potentially be related to leaf age at the time of sampling. Different RNA extraction methods will be needed to deal with the secondary compounds (e.g., polyphenolics) that are present in mature and senescing tissues. Recovering high‐quality RNA in the field, across a range of time points and from leaves of different ages, will be a challenge for future studies.

Other challenges that will need to be overcome are associated with sampling at NEON sites. Sampling within NEON permanent plots is generally not allowed for collections outside of NEON’s own standard protocol, and therefore our sampling was limited to sites adjacent to NEON plots. This limitation raises some significant issues for researchers who wish to leverage data being collected within NEON sites (https://data.neonscience.org/). First, many NEON sites are located in areas where there is no similar adjacent field site available for sampling, due to land restrictions or ecological variation. We ultimately selected Harvard Forest because we could sample at sites outside of the NEON plot itself. The second major issue is that sampling outside of the NEON plot means that there is no guarantee of continued access to plant populations in the future. There is a great opportunity for ecologists and evolutionary biologists to leverage the wealth of data that NEON is generating for our community. However, access for researchers that wish to conduct RNA and DNA sampling of plants (and other organisms) within NEON sites is an essential issue that requires further development across the network. Sequencing costs will continue to decline over the planned 30‐year life span of NEON, and strategies to accommodate sequencing for plants and other eukaryotes will offer opportunities to greatly expand large‐scale studies at the intersection of ecology and evolution.

AUTHOR CONTRIBUTIONS

H.E.M., K.M.D., and M.S.B. conceived and designed the experiments. H.E.M. and S.A.J collected the samples, extracted the RNA, and collected the vouchers. H.E.M., E.W., Z.L., K.M.D., and M.S.B. analyzed the data. H.E.M., Z.L., K.M.D., and M.S.B. drafted the manuscript, and all authors approved the final manuscript.

Acknowledgments

The authors thank Manisha V. Patel at Harvard Forest for sampling logistics, George M. Ferguson at the University of Arizona Herbarium (ARIZ) for help with identifications and voucher deposits, and Jason Steel at the Biodesign Institute at Arizona State University for RNA‐seq preparation and sequencing. This project was supported by the National Science Foundation (NSF 1550838 to M.S.B. and K.M.D and NSF 1750280 to K.M.D.).

APPENDIX 1. Sample collection number, sampling date, time of day, and locality information, with catalog numbers for vouchers deposited at the University of Arizona Herbarium (ARIZ). RIN (RNA integrity number) scores for each sample are also included.

Family Species Collection no. Sampling date Sampling time Sampling locality information Latitude Longitude ARIZ catalog no.
Dryopteridaceae Dryopteris carthusiana 2016‐015 16‐Jul‐16 19:51 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. Rd. to Lyford House, ~50 m east of State Route 32. Along bank on south side (south facing), in Acer saccharum dominated forest. Scattered in leaf litter. 42.52798 −72.18665 435313
Dryopteridaceae Dryopteris carthusiana 2016‐050 16‐Aug‐16 12:19 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. South‐facing slope on the roadway to Lyford House. 42.52789 −72.18668 435265
Dryopteridaceae Dryopteris intermedia 2016‐028 17‐Jul‐16 17:30 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Simes Tract, Compartment Simes. Along path north of gate #29 (north of Dugway Rd.). 42.46747 −72.21249 435364
Dryopteridaceae Dryopteris intermedia 2016‐066 17‐Aug‐16 13:01 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Simes Tract, Compartment Simes. Along path north of gate #29 (north of Dugway Rd.). 42.46762 −72.21259 435293
Dryopteridaceae Dryopteris marginalis 2016‐029 17‐Jul‐16 17:30 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Simes Tract, Compartment Simes. Along path north of gate #29 (north of Dugway Rd.). 42.46747 −72.21249 435363
Dryopteridaceae Dryopteris marginalis 2016‐067 17‐Aug‐16 13:06 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Simes Tract, Compartment Simes. Along path north of gate #29 (north of Dugway Rd.). 42.46762 −72.21259 435294
Rubiaceae Galium mollugo 2016‐008 16‐Jul‐16 10:23 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. Shed SE of Torrey Lab, along forest edge. 42.5319 −72.18893 435348
Rubiaceae Galium mollugo 2016‐044 15‐Aug‐16 17:31 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. Around wood shed and generator, along forest margin east of Torrey Hall. South of dumpster and parking. 42.53189 −72.18892 435259
Rubiaceae Galium tinctorium 2016‐022 17‐Jul‐16 9:40 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. Along trail SE of gate #26, to Connor Pond marsh margin. Followed margin back north ~10 m to opening in forest (mixed Thuja, Acer, Pinus). Collected at forest/marsh margin. 42.46639 −72.16238 435357
Rubiaceae Galium tinctorium 2016‐059 16‐Aug‐16 17:37 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. Along trail SE of gate #26, to Connor Pond marsh margin. Followed margin back north ~10 m to opening in forest (mixed Thuja, Acer, Pinus). Collected at forest/marsh margin. 42.46631 −72.16244 435288
Rubiaceae Galium triflorum 2016‐030 17‐Jul‐16 17:57 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Simes Tract, Compartment Simes. At gate #26. 42.46616 −72.21214 435379
Rubiaceae Galium triflorum 2016‐069 17‐Aug‐16 13:42 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Simes Tract, Compartment Simes. At gate #26. 42.46608 −72.21198 435296
Hypericaceae Hypericum perforatum 2016‐005 16‐Jul‐16 9:47 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. West of gas shed that is SE of Torrey Lab. Sandy parking lot near forest margin. 42.53202 −72.18911 435355
Hypericaceae Hypericum perforatum 2016‐041 15‐Aug‐16 17:20 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. West of gas shed that is SE of Torrey Lab. Sandy parking lot near forest margin. 42.53197 −72.18904 435224
Juglandaceae Juglans cinerea 2016‐009 16‐Jul‐16 11:36 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. Along south side of trail (Locust Opening Rd.) that heads east from Torrey Lab. ~100 m past gate, next to phone pole (#135‐5). 42.53244 −72.18853 435347
Juglandaceae Juglans cinerea 2016‐045 16‐Aug‐16 10:09 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. Along south side of trail (Locust Opening Rd.) that heads east from Torrey Lab. ~100 m past gate, next to phone pole (#135‐5). 42.53265 −72.18901 435260
Caprifoliaceae Lonicera tatarica var. morrowii 2016‐001 16‐Jul‐16 9:34 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. John H. Torrey Laboratory. North parking lot, along fence margin. 42.54171 −72.18997 435195
Caprifoliaceae Lonicera tatarica var. morrowii 2016‐037 15‐Aug‐16 15:12 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. John H. Torrey Laboratory. North parking lot, along fence margin. 42.53246 −72.18967 435228
Primulaceae Lysimachia ciliata 2016‐031 17‐Jul‐16 18:01 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Simes Tract, Compartment Simes. At gate #26. 42.46616 −72.21214 435366
Primulaceae Lysimachia ciliata 2016‐070 17‐Aug‐16 13:47 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Simes Tract, Compartment Simes. At gate #26. 42.46608 −72.21198 435297
Primulaceae Lysimachia nummularia 2016‐035 18‐Jul‐16 10:00 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P8. Harvard Farm. East of State Route 32 on Pierce Rd., just west of gate. 42.52539 −72.18459 435351
Primulaceae Lysimachia nummularia 2016‐074 17‐Aug‐16 15:15 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P8. Harvard Farm. East of State Route 32 on Pierce Rd., just west of gate. 42.52341 −72.18465 435299
Primulaceae Lysimachia quadrifolia 2016‐024 17‐Jul‐16 10:50 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. East of State Route 122, along trail leading south to gate #26. Forest margin / roadside clearing along fence. Weedy. 42.46713 −72.16381 435359
Primulaceae Lysimachia quadrifolia 2016‐062 17‐Aug‐16 10:20 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. East of State Route 122, along trail leading south to gate #26. Forest margin / roadside clearing along fence. Weedy. 42.26717 −72.16374 435290
Polygonaceae Persicaria arifolia 2016‐021 17‐Jul‐16 9:33 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. Along trail SE of gate #26, to Connor Pond marsh margin. Followed margin back north ~10 m to opening in forest (mixed Thuja, Acer, Pinus). Collected at forest/marsh margin. 42.46639 −72.16238 435356
Polygonaceae Persicaria arifolia 2016‐058 16‐Aug‐16 17:30 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. Along trail SE of gate #26, to Connor Pond marsh margin. Followed margin back north ~10 m to opening in forest (mixed Thuja, Acer, Pinus). Collected at forest/marsh margin. 42.46631 −72.16244 435289
Polygonaceae Persicaria hydropiperoides 2016‐006 16‐Jul‐16 9:50 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. West of dumpster SE of Torrey Lab, in sandy lot at forest edge, weedy patch. 42.53198 −72.18908 435350
Polygonaceae Persicaria hydropiperoides 2016‐042 15‐Aug‐16 17:23 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. Around dumpster east of Torrey Hall and south of shed. 42.53197 −72.18904 435223
Polygonaceae Persicaria sagittata 2016‐020 17‐Jul‐16 9:31 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. Along trail SE of gate #26, to Connor Pond marsh margin. Followed margin back north ~10 m to opening in forest (mixed Thuja, Acer, Pinus). 42.46639 −72.16238 435308
Polygonaceae Persicaria sagittata 2016‐057 16‐Aug‐16 17:24 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. Along trail SE of gate #26, to Connor Pond marsh margin. Followed margin back north ~10 m to opening in forest. 42.46631 −72.16244 435257
Plantaginaceae Plantago lanceolata 2016‐018 16‐Jul‐16 20:06 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. Lawn west of Lyford House. 42.5282 −72.18615 435310
Plantaginaceae Plantago lanceolata 2016‐053 16‐Aug‐16 13:43 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. Lawn west of Lyford House (before fence). 42.52807 −72.18624 435268
Plantaginaceae Plantago major 2016‐003 16‐Jul‐16 9:27 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. East of Torrey Lab, in sandy parking lot along forest margin. 42.53218 −72.18916 435354
Plantaginaceae Plantago major 2016‐039 15‐Aug‐16 16:09 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. East of Torrey Lab, in sandy parking lot along forest margin. 42.53216 −72.18912 435226
Plantaginaceae Plantago rugelii 2016‐004 16‐Jul‐16 9:30 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. East of Torrey Lab, in sandy parking lot along forest margin. 42.53218 ‐−72.18916 435352
Plantaginaceae Plantago rugelii 2016‐040 15‐Aug‐16 16:11 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. East of Torrey Lab, in sandy parking lot along forest margin. 42.53216 −72.18912 435225
Polygonaceae Polygonum cilinode 2016‐025 17‐Jul‐16 11:13 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. West side of State Route 122, just north of gate #27 along guard rail. Disturbed soils. 42.45533 −72.16677 435360
Polygonaceae Polygonum cilinode 2016‐063 17‐Aug‐16 10:47 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Slab City Tract, Compartment S6. West side of State Route 122, just north of gate #27 along guard rail. Disturbed soils. 42.45564 −72.16701 435287
Rosaceae Potentilla argentea 2016‐017 16‐Jul‐16 19:53 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. Bordering garage south of Lyford House. 42.52819 −72.18606 435311
Rosaceae Potentilla argentea 2016‐055 16‐Aug‐16 1:51 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P1. In sandy soil of parking area south of Lyford House. 42.52821 −72.18581 435255
Rosaceae Potentilla canadensis 2016‐033 17‐Jul‐16 16:55 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P10. French Rd., east of gate #1. On north side of trail at forest edge, just east of old foundation. With Acer, Fraxinus, relatively open clearing. 42.53904 −72.19297 435362
Rosaceae Potentilla canadensis 2016‐072 17‐Aug‐16 14:36 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P10. French Road, east of gate #1. On north side of trail at forest edge, just east of old foundation. With Acer, Fraxinus, relatively open clearing. 42.53907 −72.193 435306
Rosaceae Prunus serotina 2016‐027 17‐Jul‐16 16:38 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Tom Swam Tract, Compartment T1. Sunset Lane, west of State Route 32. About 50 m west of Riley Gate, along road‐turned‐path on left (south) side. 42.49305 −72.19437 435230
Rosaceae Prunus serotina 2016‐065 17‐Aug‐16 12:16 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Tom Swam Tract, Compartment T1. Sunset Lane, west of State Route 32. About 50 m west of Riley Gate, along road‐turned‐path on left (south) side. 42.49274 −72.19431 435292
Rosaceae Prunus virginiana 2016‐026 17‐Jul‐16 15:33 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Tom Swam Tract, Compartment T4. Road heading south from Tom Swamp Road at gate #18 (before gate #20). West side of road. 42.50949 −72.20756 435365
Rosaceae Prunus virginiana 2016‐064 17‐Aug‐16 11:26 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Tom Swam Tract, Compartment T4. Road heading south from Tom Swamp Road at gate #18 (before gate #20). West side of road. 42.5095 −72.20757 435291
Polygonaceae Reynoutria japonica 2016‐034 17‐Jul‐16 19:40 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P10. East side of State Route 32, north of Prospect Hill Rd. Along freeway. 42.53301 −72.1924 435361
Polygonaceae Reynoutria japonica 2016‐075 17‐Aug‐16 16:03 Harvard Forest, 26 Prospect Hill Rd., Petersham, MA, 0136, USA. Worcester County. Prospect Hill Tract, Compartment P10. East side of State Route 32, north of Prospect Hill Rd. Along freeway. 42.53295 −72.19247 435305
Open in a new tab

Marx, H. E. , Jorgensen S. A., Wisely E., Li Z., Dlugosch K. M., and Barker M. S.. 2021. Pilot RNA‐seq data from 24 species of vascular plants at Harvard Forest. Applications in Plant Sciences 9(2): e11409.

Data Availability

Raw reads for all samples for 24 species are deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRP127805: https://www.ncbi.nlm.nih.gov/sra/SRP127805; BioProject: PRJNA422719). Assembled transcriptomes for each species are archived on Zenodo and available at https://doi.org/10.5281/zenodo.3727312 (Marx et al., 2020).

LITERATURE CITED

  1. Adams, K. L. , Cronn R., Percifield R., and Wendel J. F.. 2003. Genes duplicated by polyploidy show unequal contributions to the transcriptome and organ‐specific reciprocal silencing. Proceedings of the National Academy of Sciences, USA 100: 4649–4654. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. An, H. , Qi X., Gaynor M. L., Hao Y., Gebken S. C., Mabry M. E., McAlvay A. C., et al. 2019. Transcriptome and organellar sequencing highlights the complex origin and diversification of allotetraploid Brassica napus . Nature Communications 10: 2878. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Andrews, S. 2010. FastQC: A quality control tool for high throughput sequence data [Online]. Website http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ [accessed 22 December 2020].
  4. Baek, S. , Choi K., Kim G.‐B., Yu H.‐J., Cho A., Jang H., Kim C., et al. 2018. Draft genome sequence of wild Prunus yedoensis reveals massive inter‐specific hybridization between sympatric flowering cherries. Genome Biology 19: 127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Baniaga, A. E. , Marx H. E., Arrigo N., and Barker M. S.. 2020. Polyploid plants have faster rates of multivariate niche differentiation than their diploid relatives. Ecology Letters 23: 68–78. [DOI] [PubMed] [Google Scholar]
  6. Barker, M. S. , Kane N. C., Matvienko M., Kozik A., Michelmore R. W., Knapp S. J., and Rieseberg L. H.. 2008. Multiple paleopolyploidizations during the evolution of the Compositae reveal parallel patterns of duplicate gene retention after millions of years. Molecular Biology and Evolution 25: 2445–2455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Barker, M. S. , Dlugosch K. M., Dinh L., Challa R. S., Kane N. C., King M. G., and Rieseberg L. H.. 2010. EvoPipes.net: Bioinformatic tools for ecological and evolutionary genomics. Evolutionary Bioinformatics Online 6: 143–149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Barker, M. S. , Arrigo N., Baniaga A. E., Li Z., and Levin D. A.. 2016a. On the relative abundance of autopolyploids and allopolyploids. New Phytologist 210: 391–398. [DOI] [PubMed] [Google Scholar]
  9. Barker, M. S. , Li Z., Kidder T. I., and Reardon C. R.. 2016b. Most Compositae (Asteraceae) are descendants of a paleohexaploid and all share a paleotetraploid ancestor with the Calyceraceae. American Journal of Botany 103: 1203–1211. [DOI] [PubMed] [Google Scholar]
  10. Birney, E. , Clamp M., and Durbin R.. 2004. GeneWise and Genomewise. Genome Research 14: 988–995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Blande, D. , Halimaa P., Tervahauta A. I., Aarts M. G. M., and Kärenlampi S. O.. 2017. De novo transcriptome assemblies of four accessions of the metal hyperaccumulator plant Noccaea caerulescens . Scientific Data 4: 160131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Bowsher, A. W. , Shetty P., Anacker B. L., Siefert A., Strauss S. Y., and Friesen M. L.. 2017. Transcriptomic responses to conspecific and congeneric competition in co‐occurring Trifolium . Journal of Ecology 105: 602–615. [Google Scholar]
  13. Bragg, J. G. , Supple M. A., Andrew R. L., and Borevitz J. O.. 2015. Genomic variation across landscapes: Insights and applications. New Phytologist 207: 953–967. [DOI] [PubMed] [Google Scholar]
  14. Breed, M. F. , Harrison P. A., Blyth C., Byrne M., Gaget V., Gellie N. J. C., Groom S. V. C., et al. 2019. The potential of genomics for restoring ecosystems and biodiversity. Nature Reviews Genetics 20: 615–628. [DOI] [PubMed] [Google Scholar]
  15. Carpenter, E. J. , Matasci N., Ayyampalayam S., Wu S., Sun J., Yu J., Jimenez Vieira F. R., et al. 2019. Access to RNA‐sequencing data from 1,173 plant species: The 1000 Plant transcriptomes initiative (1KP). GigaScience 8: giz126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Coate, J. E. , and Doyle J. J.. 2010. Quantifying whole transcriptome size, a prerequisite for understanding transcriptome evolution across species: An example from a plant allopolyploid. Genome Biology and Evolution 2: 534–546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Coate, J. E. , and Doyle J. J.. 2015. Variation in transcriptome size: Are we getting the message? Chromosoma 124: 27–43. [DOI] [PubMed] [Google Scholar]
  18. Collinge, S. K. 2018. NEON is your observatory. Frontiers in Ecology and the Environment 16: 371. [Google Scholar]
  19. Delgado‐Baquerizo, M. , Oliverio A. M., Brewer T. E., Benavent‐González A., Eldridge D. J., Bardgett R. D., Maestre F. T., et al. 2018. A global atlas of the dominant bacteria found in soil. Science 359: 320–325. [DOI] [PubMed] [Google Scholar]
  20. Dempewolf, H. , Kane N. C., Ostevik K. L., Geleta M., Barker M. S., Lai Z., Stewart M. L., et al. 2010. Establishing genomic tools and resources for Guizotia abyssinica (L.f.) Cass. — The development of a library of expressed sequence tags, microsatellite loci and the sequencing of its chloroplast genome. Molecular Ecology Resources 10: 1048–1058. [DOI] [PubMed] [Google Scholar]
  21. Der, J. P. , Barker M. S., Wickett N. J., dePamphilis C. W., and Wolf P. G.. 2011. De novo characterization of the gametophyte transcriptome in bracken fern, Pteridium aquilinum . BMC Genomics 12: 99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Dlugosch, K. M. , Lai Z., Bonin A., Hierro J., and Rieseberg L. H.. 2013. Allele identification for transcriptome‐based population genomics in the invasive plant Centaurea solstitialis . G3: Genes, Genomes, Genetics 3: 359–367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Doyle, J. J. , and Coate J. E.. 2018. Polyploidy, the nucleotype, and novelty: The impact of genome doubling on the biology of the cell. International Journal of Plant Sciences 180: 1–52. [Google Scholar]
  24. Edwards, J. A. , and Edwards R. A.. 2019. Fastq‐pair: Efficient synchronization of paired‐end fastq files. bioRxiv 552885 [Preprint] [published 19 February 2019]. Available at 10.1101/552885 [accessed 22 December 2020]. [DOI]
  25. Enquist, B. J. , Feng X., Boyle B., Maitner B., Newman E. A., Jørgensen P. M., Roehrdanz P. R., et al. 2019. The commonness of rarity: Global and future distribution of rarity across land plants. Science Advances 5: eaaz0414. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Evkaikina, A. I. , Berke L., Romanova M. A., Proux‐Wéra E., Ivanova A. N., Rydin C., Pawlowski K., and Voitsekhovskaja O. V.. 2017. The Huperzia selago shoot tip transcriptome sheds new light on the evolution of leaves. Genome Biology and Evolution 9: 2444–2460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Fierer, N. , Lauber C. L., Ramirez K. S., Zaneveld J., Bradford M. A., and Knight R.. 2012a. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. The ISME Journal 6: 1007–1017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Fierer, N. , Leff J. W., Adams B. J., Nielsen U. N., Bates S. T., Lauber C. L., Owens S., et al. 2012b. Cross‐biome metagenomic analyses of soil microbial communities and their functional attributes. Proceedings of the National Academy of Sciences, USA 109: 21390–21395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Fitzpatrick, M. C. , and Keller S. R.. 2015. Ecological genomics meets community‐level modelling of biodiversity: Mapping the genomic landscape of current and future environmental adaptation. Ecology Letters 18: 1–16. [DOI] [PubMed] [Google Scholar]
  30. Gallagher, R. V. , Falster D. S., Maitner B. S., Salguero‐Gómez R., Vandvik V., Pearse W. D., Schneider F. D., et al. 2020. Open Science principles for accelerating trait‐based science across the Tree of Life. Nature Ecology & Evolution 4: 294–303. [DOI] [PubMed] [Google Scholar]
  31. Goodstein, D. M. , Shu S., Howson R., Neupane R., Hayes R. D., Fazo J., Mitros T., et al. 2012. Phytozome: A comparative platform for green plant genomics. Nucleic Acids Research 40: D1178–D1186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Han, B. , Umaña M. N., Mi X., Liu X., Chen L., Wang Y., Liang Y., et al. 2017. The role of transcriptomes linked with responses to light environment on seedling mortality in a subtropical forest, China. Journal of Ecology 105: 592–601. [Google Scholar]
  33. Hodgins, K. A. , Lai Z., Oliveira L. O., Still D. W., Scascitelli M., Barker M. S., Kane N. C., et al. 2014. Genomics of Compositae crops: Reference transcriptome assemblies and evidence of hybridization with wild relatives. Molecular Ecology Resources 14: 166–177. [DOI] [PubMed] [Google Scholar]
  34. International Peach Genome Initiative , Verde I., Abbott A. G., Scalabrin S., Jung S., Shu S., Marroni F., et al. 2013. The high‐quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nature Genetics 45: 487–494. [DOI] [PubMed] [Google Scholar]
  35. Jansson, J. K. , and Hofmockel K. S.. 2020. Soil microbiomes and climate change. Nature Reviews Microbiology 18: 35–46. [DOI] [PubMed] [Google Scholar]
  36. Jenkins, J. , Motzkin G., and Ward K.. 2008. The Harvard Forest Flora: An inventory, analysis, and ecological history. Harvard Forest Paper 28. Harvard Forest, Harvard University, Petersham, Massachusetts, USA. [Google Scholar]
  37. Johnson, M. T. J. , Carpenter E. J., Tian Z., Bruskiewich R., Burris J. N., Carrigan C. T., Chase M. W., et al. 2012. Evaluating methods for isolating total RNA and predicting the success of sequencing phylogenetically diverse plant transcriptomes. PLoS ONE 7: e50226. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Knapp, A. K. , and Collins S. L.. 2019. Reimagining NEON operations: We can do better. Bioscience 69: 956–959. [Google Scholar]
  39. Komatsu, K. J. , Avolio M. L., Lemoine N. P., Isbell F., Grman E., Houseman G. R., Koerner S. E., et al. 2019. Global change effects on plant communities are magnified by time and the number of global change factors imposed. Proceedings of the National Academy of Sciences, USA 116: 17867–17873. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lai, Z. , Kane N. C., Kozik A., Hodgins K. A., Dlugosch K. M., Barker M. S., Matvienko M., et al. 2012. Genomics of Compositae weeds: EST libraries, microarrays, and evidence of introgression. American Journal of Botany 99: 209–218. [DOI] [PubMed] [Google Scholar]
  41. Lamesch, P. , Berardini T. Z., Li D., Swarbreck D., Wilks C., Sasidharan R., Muller R., et al. 2012. The Arabidopsis Information Resource (TAIR): Improved gene annotation and new tools. Nucleic Acids Research 40: D1202–D1210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lassmann, T. , Hayashizaki Y., and Daub C. O.. 2009. TagDust—A program to eliminate artifacts from next generation sequencing data. Bioinformatics 25: 2839–2840. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Liu, H. , Mi Z., Lin L., Wang Y., Zhang Z., Zhang F., Wang H., et al. 2018. Shifting plant species composition in response to climate change stabilizes grassland primary production. Proceedings of the National Academy of Sciences, USA 115: 4051–4056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Maitner, B. S. , Boyle B., Casler N., Condit R., Donoghue J. II, Durán S. M., Guaderrama D., et al. 2018. The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods in Ecology and Evolution 9: 373–379. [Google Scholar]
  45. Mandáková, T. , Li Z., Barker M. S., and Lysak M. A.. 2017. Diverse genome organization following 13 independent mesopolyploid events in Brassicaceae contrasts with convergent patterns of gene retention. Plant Journal 91: 3–21. [DOI] [PubMed] [Google Scholar]
  46. Marx, H. E. , Jorgensen S. A., Wisely E., Li Z., Dlugosch K. M., and Barker M. S.. 2020. Progress toward plant community transcriptomics: Pilot RNA‐seq data from 24 species of vascular plants at Harvard Forest [published 25 March 2020]. Available at Zenodo repository https://zenodo.org/record/3727313#.X‐Jg5OBMHX8 [accessed 22 December 2020].
  47. Matthews, B. , Best R. J., Feulner P. G. D., Narwani A., and Limberger R.. 2018. Evolution as an ecosystem process: Insights from genomics. Genome 61: 298–309. [DOI] [PubMed] [Google Scholar]
  48. McFadden, I. R. , Sandel B., Tsirogiannis C., Morueta‐Holme N., Svenning J.‐C., Enquist B. J., and Kraft N. J. B.. 2019. Temperature shapes opposing latitudinal gradients of plant taxonomic and phylogenetic β diversity. Ecology Letters 22: 1126–1135. [DOI] [PubMed] [Google Scholar]
  49. Michael, T. P. 2014. Plant genome size variation: Bloating and purging DNA. Briefings in Functional Genomics 13: 308–317. [DOI] [PubMed] [Google Scholar]
  50. One Thousand Plant Transcriptomes Initiative . 2019. One thousand plant transcriptomes and the phylogenomics of green plants. Nature 574: 679–685. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Patterson, J. , Carpenter E. J., Zhu Z., An D., Liang X., Geng C., Drmanac R., and Wong G. K.‐S.. 2019. Impact of sequencing depth and technology on de novo RNA‐Seq assembly. BMC Genomics 20: 604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Pokorn, T. , Radišek S., Javornik B., Štajner N., and Jakše J.. 2017. Development of hop transcriptome to support research into host‐viroid interactions. PLoS ONE 12: e0184528. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Qi, X. , An H., Ragsdale A. P., Hall T. E., Gutenkunst R. N., Pires J. C., and Barker M. S.. 2017. Genomic inferences of domestication events are corroborated by written records in Brassica rapa . Molecular Ecology 26: 3373–3388. [DOI] [PubMed] [Google Scholar]
  54. Rice, A. , Glick L., Abadi S., Einhorn M., Kopelman N. M., Salman‐Minkov A., Mayzel J., et al. 2015. The Chromosome Counts Database (CCDB): A community resource of plant chromosome numbers. New Phytologist 206: 19–26. [DOI] [PubMed] [Google Scholar]
  55. Rice, A. , Šmarda P., Novosolov M., Drori M., Glick L., Sabath N., Meiri S., et al. 2019. The global biogeography of polyploid plants. Nature Ecology & Evolution 3: 265–273. [DOI] [PubMed] [Google Scholar]
  56. Román‐Palacios, C. , and Wiens J. J.. 2020. Recent responses to climate change reveal the drivers of species extinction and survival. Proceedings of the National Academy of Sciences, USA 117: 4211–4217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Seppey, M. , Manni M., and Zdobnov E. M.. 2019. BUSCO: Assessing genome assembly and annotation completeness. In Kollmar M. [ed.], Gene prediction: Methods and protocols, 227–245. Springer, New York, New York, USA. [DOI] [PubMed] [Google Scholar]
  58. Shirasawa, K. , Isuzugawa K., Ikenaga M., Saito Y., Yamamoto T., Hirakawa H., and Isobe S.. 2017. The genome sequence of sweet cherry (Prunus avium) for use in genomics‐assisted breeding. DNA Research 24: 499–508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Smith‐Unna, R. , Boursnell C., Patro R., Hibberd J. M., and Kelly S.. 2016. TransRate: Reference‐free quality assessment of de novo transcriptome assemblies. Genome Research 26: 1134–1144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Snell, R. S. , Beckman N. G., Fricke E., Loiselle B. A., Carvalho C. S., Jones L. R., Lichti N. I., et al. 2019. Consequences of intraspecific variation in seed dispersal for plant demography, communities, evolution and global change. AoB Plants 11: lz016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Subrahmaniam, H. J. , Libourel C., and Journet E. P.. 2018. The genetics underlying natural variation of plant–plant interactions, a beloved but forgotten member of the family of biotic interactions. Plant Journal 93: 747–770. [DOI] [PubMed] [Google Scholar]
  62. Swenson, N. G. , and Jones F. A.. 2017. Community transcriptomics, genomics and the problem of species co‐occurrence. Journal of Ecology 105: 563–568. [Google Scholar]
  63. Turner, T. R. , James E. K., and Poole P. S.. 2013. The plant microbiome. Genome Biology 14: 209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Visger, C. J. , Wong G. K.‐S., Zhang Y., Soltis P. S., and Soltis D. E.. 2019. Divergent gene expression levels between diploid and autotetraploid Tolmiea relative to the total transcriptome, the cell, and biomass. American Journal of Botany 106: 280–291. [DOI] [PubMed] [Google Scholar]
  65. Weisberg, A. J. , Kim G., Westwood J. H., and Jelesko J. G.. 2017. Sequencing and de novo assembly of the Toxicodendron radicans (poison ivy) transcriptome. Genes 8: 317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Wendel, J. F. , Jackson S. A., Meyers B. C., and Wing R. A.. 2016. Evolution of plant genome architecture. Genome Biology 17: 37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Wheeler, D. L. , Barrett T., Benson D. A., Bryant S. H., Canese K., Chetvernin V., Church D. M., et al. 2008. Database resources of the National Center for Biotechnology Information. Nucleic Acids Research 36: D13–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Xie, Y. , Wu G., Tang J., Luo R., Patterson J., Liu S., Huang W., et al. 2014. SOAPdenovo‐Trans: de novo transcriptome assembly with short RNA‐Seq reads. Bioinformatics 30: 1660–1666. [DOI] [PubMed] [Google Scholar]
  69. Yang, Y. , Moore M. J., Brockington S. F., Timoneda A., Feng T., Marx H. E., Walker J. F., and Smith S. A.. 2017. An efficient field and laboratory workflow for plant phylotranscriptomic projects. Applications in Plant Sciences 5: 1600128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Zambrano, J. , Iida Y., Howe R., Lin L., Umana M. N., Wolf A., Worthy S. J., and Swenson N. G.. 2017. Neighbourhood defence gene similarity effects on tree performance: A community transcriptomic approach. Journal of Ecology 105: 616–626. [Google Scholar]
  71. Zanne, A. E. , Tank D. C., Cornwell W. K., Eastman J. M., Smith S. A., FitzJohn R. G., McGlinn D. J., et al. 2014. Three keys to the radiation of angiosperms into freezing environments. Nature 506: 89–92. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Data Availability Statement

Raw reads for all samples for 24 species are deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRP127805: https://www.ncbi.nlm.nih.gov/sra/SRP127805; BioProject: PRJNA422719). Assembled transcriptomes for each species are archived on Zenodo and available at https://doi.org/10.5281/zenodo.3727312 (Marx et al., 2020).

Articles from Applications in Plant Sciences are provided here courtesy of Wiley

RESOURCES