Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae

Lloyd, John P.; Bowman, Megan J.; Azodi, Christina B.; Sowers, Rosalie P.; Moghe, Gaurav D.; Childs, Kevin L.; Shiu, Shin-Han

doi:10.1038/s41598-019-47797-y

Download PDF

Article
Open access
Published: 20 August 2019

Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae

Scientific Reports volume 9, Article number: 12122 (2019) Cite this article

2800 Accesses
3 Citations
2 Altmetric
Metrics details

Subjects

Abstract

Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Among 43,301 ITRs across the four species, 34,460 (80%) are species-specific. ITRs found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established in Oryza sativa (rice) that could accurately distinguish between phenotype genes and pseudogenes (area under curve-receiver operating characteristic = 0.94). Based on the models, 584 (8%) and 4391 (61%) rice ITRs are classified as likely functional and nonfunctional with high confidence, respectively. ITRs with conserved expression and ancient retained duplicates, features that were not part of the model, are frequently classified as likely-functional, suggesting these characteristics could serve as pragmatic rules of thumb for identifying candidate sequences likely to be under selection. This study also provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems.

The genome and population genomics of allopolyploid Coffea arabica reveal the diversification history of modern coffee cultivars

Article Open access 15 April 2024

A pan-genome of 69 Arabidopsis thaliana accessions reveals a conserved genome structure throughout the global species range

Article Open access 11 April 2024

Long non-coding RNAs: definitions, functions, challenges and recommendations

Article 03 January 2023

Introduction

Transcriptome sequencing has led to the discovery of pervasive transcription in unannotated, intergenic space in eukaryotes, including metazoan^1,2,3,4, fungal⁵, and plant species^6,7,8,9,10. While intergenic transcription may be associated with nearby genes¹¹, intergenic transcripts can represent the activity of novel genes. These intergenic transcripts may play roles as competitive endogenous RNAs¹², cis-acting regulatory transcripts¹³, and/or small protein-coding regions that can be missed by gene finding programs¹⁴. In addition to these possible functions, intergenic transcribed regions (ITRs) may be products of noisy transcription, resulting from imperfect regulation of the gene expression machinery^8,15. Although some of these ITRs may ultimately provide the raw materials from which novel genes may evolve de novo¹⁶, they are not expected to be under selection. Thus, identifying the sequence features that distinguish between functional intergenic transcripts and those that are the products of noisy transcription represents a critical task in genome biology.

Here, we adopt the “selected effect” definition of function, which stipulates that a measurable activity, e.g. transcription, is considered functional if such activity is under natural selection^17,18,19. This definition stands in contrast to the “causal role” definition that considers any reproducible biochemical activity, such as intergenic transcription, to be functional^2,17. However, pseudogenes that are remnants of once functional genes can be expressed^20,21, indicating that sequences that are not under selection could be considered functional based on the causal role definition due to biochemical activities. Given these considerations, the causal role definition of function is inadequate for distinguishing functional ITRs from noisy ones.

To establish selected effect functionality in a sequence, significant conservation in sequences or activities provides strong evidence. However, lack of significant conservation does not necessarily indicate a lack of selective pressure, which may be due to selection that is significant but too weak to be detected based on sequence conservation or because only a small stretch of a sequence is under selection^22,23. Instead of relying on a single line of evidence such as sequence conservation, an approach that integrates genetic, evolutionary, and biochemical evidence has been suggested²⁴. Based on this framework, predictive models were established that were highly effective at identifying sequences with significant fitness cost when mutated²⁵ and distinguishing human and Arabidopsis thaliana protein coding and RNA genes from pseudogenes^26,27. Thus, integration of evolutionary and biochemical signatures could provide valuable insight in distinguishing functional and noisy ITRs.

In this study, we investigate the extent of sequence and expression conservation as well as potential functionality of ITRs using data from four Poaceae (grass) species: Oryza sativa (rice), Brachypodium distachyon, Sorghum bicolor (sorghum), and Zea mays (maize). Phylogenetically, these four species fall into two clades where one clade consists of maize and sorghum diverged ~15 million years ago (MYA)^28,29, and the other contains rice and B. distachyon diverged ~47 MYA³⁰. All four species share ancient whole genome duplications (WGDs)^31,32 while the maize lineage experienced a more recent WGD ~12 MYA³³. Thus, studies of ITRs in these species are well-suited to assess not only sequence and expression conservation of ITRs but also ITR duplicate retention post duplication. In addition, using rice as an example, we generated function prediction models by integrating rice mutant phenotype, sequence conservation, transcriptome, histone modification, DNA methylation, and nucleosome occupancy data to predict functional ITRs genome-wide.

Results and Discussion

Identification and classification of poaceae transcribed regions

To investigate the properties and potential functionality of polyadenylated intergenic transcripts, we focused on four Poaceae species (Fig. 1A). In each of the four species, we identified transcribed regions and classified them as parts of exons, introns, or pseudogenes (see Methods). Transcriptome datasets were developed using polyadenylated (PolyA+) transcripts from developmentally-matched leaf, seed, and reproductive tissues^34,35 (Table S1). Transcript fragments were assembled at the exon level, although neighboring transcribed regions may organize into larger transcriptional units. Larger transcriptional units were considered in later sections. Transcribed regions that did not overlap with gene or pseudogene annotation were considered as intergenic. Intergenic transcribed regions (ITRs, Supplemental Dataset 1) accounted for only 4–7% of mapped reads and 6–12% of transcribed regions (defined by continuous read mapping) in each species (Fig. 1B). In contrast, 92–96% of mapped reads overlapped with annotated exons and/or introns (Fig. 1B). We also found that only 0.4–2.6% of intergenic space was covered by transcribed regions in each species (Fig. 1C). Thus, among PolyA+ transcripts, intergenic transcription in Poaceae is rare relative to genic expression, consistent with previous findings in A. thaliana²⁷.

Despite the relative scarcity of intergenic transcripts, we identified 7,000 to 16,000 ITRs in these four species (Fig. 1B, right panel, orange). We asked whether ITRs resemble known annotated sequences and found that 16–23% of ITRs in the four species have significant translated sequence similarity to plant proteins and/or contain a known protein domain (Fig. 1D). In addition, only 56–94 ITRs (<1% in each species) contain known RNA gene domains. ITRs resembling annotated genes may be unannotated exons, novel genes, or pseudogenes that are still expressed. Only 7–23% of ITRs are highly repetitive (Fig. 1D), indicating that expression of abundant transposable element families does not account for the majority of intergenic expression in these species. Overall, 54–77% of ITRs among the four species do not resemble annotated features and are not highly repetitive.

Expression properties of ITRs and their associations with neighboring genes

We next contrasted four expression properties – transcript fragment length, expression level and breadth, and reproducibility across experiments – between ITRs and transcribed exons. Compared to transcribed exons, ITRs in all four species are significantly shorter (Mann Whitney U tests, all p < 7 × 10⁻¹⁰⁵), expressed at lower levels (all p < 8 × 10⁻³¹¹), and expressed in fewer tissues, (all p < 2 × 10⁻³⁰⁶) (Fig. S1A–C). We found that 76–88% of ITRs were reproducible across leaf transcriptome biological replicates (Fig. S1D). Although these proportions were significantly lower than those of transcribed exons (93–95%; Fisher’s Exact Tests (FETs), all p < 3 × 10⁻¹⁰), they far exceeded randomly expected percentages (2–21%; FETs, all p < 7 × 10⁻¹⁰). This is indicative of the presence of hotspots in intergenic space where reproducible transcription originates, either due to the presence of truly functional sequences or noisy transcription driven by true regulatory signals with neutral effects.

In mammalian systems, intergenic transcripts have been suggested to be associated with nearby genes¹¹, either as unannotated exon extensions or products of run-on transcription. Interestingly, the expression correlations between ITRs and proximal annotated gene neighbors (<500 bp) were significantly higher than expression correlation between ITRs and distal gene neighbors (>500 bp) or between proximal gene-gene neighbors (Fig. 2A). There are two potential explanations for this pattern: (1) ITRs proximal to genes may be unannotated gene extensions (2) gene regulation may influence expression patterns of nearby unrelated transcripts. Consistent with the first explanation, we found that 90% of rice ITRs within 500 bp of a gene have junction reads bridging ITRs and neighboring genes identified with a multi-exon transcript identification pipeline (see Methods). Similarly, 93% of expressed pseudogenes in rice were also overlapped by exon-derived transcripts from the alternative pipeline. Thus, as in mammalian systems, in plants much of the intergenic and pseudogenic transcription occurring near genes may be gene-associated, either due to run on transcription, alternative genic transcriptional start sites, or unannotated exon extensions of genes.

We identified sets of gene-distal ITRs >10 kbp from a neighboring gene that are unlikely to be associated with genic transcription (n = 500, 411, 1,355, and 5,581 in rice, B. distachyon, sorghum, and maize, respectively). Compared to ITRs ≤10 kbp from genes, gene-distal ITRs were expressed at higher levels in rice, sorghum, and maize (≥13% median expression level increase; U tests, all p < 0.02), but were expressed in fewer datasets in B. distachyon, sorghum, and maize (≥4% decrease in mean expression breadth; U tests, all p < 0.04). Taken together, the expression characteristics of ITRs are consistent with noisy transcripts that are expected to be short in length, and weakly and narrowly expressed¹⁵.

Cross-species sequence and expression conservation of ITRs

Sequence conservation due to selective pressure is a hallmark of functional genome regions. We considered a sequence as conserved if it exhibited cross-species sequence similarity significantly greater than that observed for random, unexpressed intergenic sequences (see Methods). Under this framework, we found that only 11–15% of ITRs among the four species were conserved across species (Fig. 3A). Significantly fewer ITRs were conserved across species compared to transcribed exons (74–79% conserved), introns (16–31%), or pseudogenes (28–49%; FET, all p < 2 × 10⁻⁹). Gene-distal ITRs were less likely to be conserved compared to those ≤10 kbp from genes, with a >30% reduction in conservation among gene-distal ITRs in all species (FET; all p < 9 × 10⁻³). Thus, ITRs primarily represent species-specific sequences.

We next evaluated the expression conservation of ITRs by two measures. First, we identified syntenic orthologs (see Methods) and determined whether regions in other species orthologous to ITRs were also expressed. We found that 18–44% of conserved ITRs and their orthologs were both expressed, significantly lower than the percentages for transcribed exons and their orthologs (91–97%, FET, all p < 2 × 10⁻¹⁰; Fig. 3B). Thus, species-specific gain of expression may explain ITR expression. Alternatively, if the ancestral ITR sequence was expressed, maintenance of ITR expression across species is rare. However, significantly fewer unexpressed intergenic sequences had an ortholog that was expressed (0–7%) compared to ITRs (FET, all p < 0.003), indicating that, when both an ITR and its ortholog are expressed, this expression conservation likely represents non-random events that are under selection. ITRs with significant similarities to protein coding genes (Fig. 1D) are more likely to have conserved expression across species, as 32% of ITRs with similarity to protein-coding sequences were associated with an expressed ortholog compared to 11% of ITRs without protein similarity (FET, p < 3 × 10⁻⁶). We also identified sequence blocks conserved across all four species (see Methods). Among 639 blocks that were intergenic in all species, we found that 15% were expressed in at least one species (Fig. S2A), compared to >99% of blocks composed of exons in all four species (FET, p < 6 × 10⁻¹¹; Fig. S2B). When an intergenic conserved sequence block was expressed, the expression was limited to one species in 74% of cases. While expression in two, three, or four species was documented in only 18%, 6%, and 1.3% (n = 1) cases, respectively (Fig. S2A), compared to 74% of exon blocks that were expressed in all four species (Fig. S2B). Overall, we find that the two analyses lead to the same conclusion: the few ITRs with sequence conservation rarely exhibit conserved expression.

For the second measure of expression conservation, we evaluated whether ITRs and their expressed orthologs were expressed in similar tissues. For a pair of orthologs, we calculated percentage of tissues commonly expressed (% commonality, see Methods). Orthologous pairs of ITRs had an average of 48% commonality, which not statistically different than the average for orthologous transcribed exons (53%; U test, p = 0.22), and significantly higher than the average for random sequence pairs (36%, p < 6 × 10⁻⁶; Fig. 3C). By comparison, expressed tissue % commonality between transcribed introns and their orthologs (mean % commonality = 27%) was no different than random (mean = 26%; U test, p = 0.33; Fig. 3D). These findings suggest that ITRs with cross-species expression conservation, particularly those with higher % tissue commonality, may be enriched for sequences under selection.

Sequence and expression conservation of ITR paralogs

In the previous section, we examined cross-species sequence and expression conservation. Considering that plant genomes harbor a rich history of large- and small-scale duplication events, we next asked whether ITR duplicates tend to be retained over time, and if so, whether the paralogous ITRs have similar expression patterns. We excluded highly-repetitive sequences (Fig. 1D) from the analyses because they were duplicated by definition. We found that 47–50% of rice, B. distachyon, and sorghum ITRs were duplicated, compared to 45–48% of transcribed exons and 31–66% of random, unexpressed intergenic sequences (Fig. S3). In maize, 82% of ITRs, 74% of transcribed exons, and 95% of random, unexpressed intergenic sequences were duplicated (Fig. S3). The high percentage of duplicated sequences from random, unexpressed intergenic regions in maize is due to an overall greater number of paralogs identified among maize exons and repetitive sequences, which were used to define thresholds to call a sequence as repetitive (see Methods). Based on the estimated base substitution rates (K) as a proxy for time (Fig. 4A), ITR duplicates in all species were generated from more recent duplication events compared to transcribed exons (U tests, all p < 6 × 10⁻¹²⁷), instead more closely resembling introns and pseudogenes that experience weaker or no selection. Together with the rarity of ITR duplicates at larger K (>0.06, Fig. 4A) compared to exons in annotated genes, most ITR duplicates are likely not maintained.

To more definitively determine the relationship between the timing of duplication events and ITR duplicate retention, we identified ITRs present in duplicated genome blocks derived from whole genome duplication (WGD) events (referred as syntenic blocks, Figs 4B; S4). We first looked at duplicate sequences derived from a WGD event 12 MYA in maize³³ (see Methods). Among the 4,906 non-repetitive maize ITRs in these syntenic blocks, 463 (9%) had syntenic duplicates (Fig. 4C), significantly more than that of random, unexpressed intergenic regions (2%; FET, p < 4 × 10⁻¹⁰) but significantly fewer than protein-coding genes (36%, FET, p < 3 × 10⁻¹⁶). Thus, as many as 91% of ITRs arose following the WGD or have duplicates that were deleted, mutated beyond recognition, or translocated within a span of 12 million years. When considering syntenic blocks from the much older ρ and σ WGD events >70 MYA in all four species, only 1.0% (49 of 4,859) of ITRs had syntenic duplicates, compared to 28–31% of protein-coding genes, indicating an overall lack of ancient duplicates. Nonetheless, a greater proportion of ITRs have ρ or σ WGD-derived duplicates compared to random, unexpressed intergenic regions (0.2%; FET, p < 0.002; right panel, Fig. 4C), suggesting that a subset of these ITR duplicates have been retained for over 70 MY.

We next assessed whether expression was conserved among duplicate ITRs and found that 18–34% ITR duplicates (most similar paralogs) were also expressed depending on the species, compared to 79–84% of paralogs of transcribed exons (FET, all p < 6 × 10⁻¹⁰). In addition, ITR paralogs generated from recent duplication events are more likely to be expressed than those from older events (U tests, all p < 3 × 10⁻⁵; Fig. 4D). This suggests that the expression of ITRs may be initially maintained following duplication, but frequently lost over time. By contrast, older duplicates of transcribed exons are more frequently expressed than recent duplicates (U tests, all p < 8 × 10⁻¹⁰⁹; Fig. 4D). Compared to ITR duplicates, fewer duplicates of random unexpressed intergenic sequences were expressed (2–7%; FET, all p < 4 × 10⁻¹⁰). Overall, ITRs were duplicated nearly as often as annotated genes but significantly fewer ITR paralogs were maintained over time or expressed compared to paralogs of annotated genes. In addition, the negative relationship between timing of duplication and percent expressed ITR paralogs suggest transcription of ITR duplicates tend to be lost over time, characteristics that could be indicative of sequences that are not under selection.

Developing a prediction model of rice sequences under selection

To further evaluate the selected effect functionality of ITRs, we first examined which evolutionary and biochemical characteristics can distinguish whether a transcribed region resembled a phenotype gene or pseudogene. Due to data availability, we focused solely on rice. For benchmark functional sequences, we identified 513 transcribed regions that overlap exons of genes with documented loss-of-function phenotypes (referred to as phenotype exons, Supplemental Dataset 2). Benchmark nonfunctional sequences consisted of 262 transcribed regions that overlapped pseudogenes but not annotated genes (transcribed pseudogenes, Supplemental Dataset 2). To distinguish between the benchmark functional and nonfunctional sequences, 44 evolutionary and biochemical features in five categories were examined including transcription activity, sequence conservation, DNA methylation, histone modifications, and nucleosome occupancy (Table S2; Supplemental Dataset 2; see Methods). Among them, 38 features had significantly different value distributions between phenotype exons and transcribed pseudogenes (all p < 0.03) but the effect sizes (differences in values) tend to be small. That is, if we used any single feature to build a naïve classifier, the median Area Under Curve-Receiver Operating Characteristic (AUC-ROC) = 0.62 (Table S2).

Although these naïve classifiers perform better than random guessing (AUC-ROC = 0.5), they were far from perfect (AUC-ROC = 1). Therefore, we considered all 44 features in combination using a machine learning approach to generate a function prediction model (see Methods). The resulting model could distinguish between phenotype exons and transcribed pseudogenes with high AUC-ROC (0.94; Fig. 5A) and precision/recall (Fig. 5B). The model provides a score between 0 and 1 (referred to as functional likelihood) where higher and lower scores indicate similarity to phenotype exons and transcribed pseudogenes, respectively (Fig. 5C–H, Supplemental Dataset 3). Using a stringent functional likelihood threshold (T₁ = 0.60) where only 5% of pseudogenes are misclassified as functional, 75% of phenotype genes are correctly predicted as functional (Fig. 5C). We also defined a second threshold (T₂ = 0.29) where 5% of phenotype exons are misclassified as pseudogenes and the majority of pseudogenes (70%) remain correctly predicted (Fig. 5D). These findings indicate that the functional prediction model can distinguish between functional and nonfunctional sequences.

Given most of the benchmark phenotype exons were protein-coding, we applied the functional prediction model to 22 transcribed miRNAs. Most transcribed miRNAs (64%) had functional likelihood values between T₁ and T₂ and cannot be confidently classified as either phenotype exon-like or pseudogene-like (Fig. 5E), indicating that the idiosyncrasies of miRNA sequences are not completely captured by the model. To better capture the characteristics of non-coding RNAs, we developed a three-class model with random forest using phenotype exons, transcribed pseudogenes, and transcribed miRNAs as benchmark sequences (see Methods). Under cross-validation, 89% and 77% of phenotype exons and miRNAs, respectively, were classified as functional (predicted as phenotype exon or miRNA; Fig. S5). Most miRNAs (64%) were predicted as miRNA-like, consistent with the notion that ncRNAs represent a distinct sequence class. The increased ability to predict non-coding sequences as functional comes at the cost of false positives, with 35% of pseudogenes classified as functional. The high false positive rate among pseudogenes indicates the three-class model provides a liberal estimate of functionality.

Overall, the prediction models integrate heterogeneous sequence and functional genomics data to predict sequence functionality based on a stringently-defined benchmark sequence set. The use of non-ambiguous sequences to build prediction models approach may limit our ability to detect functionality beyond the sequence classes defined here. In particular, it would be informative to explore additional functional prediction models trained while considering high-confidence RNA genes based on loss-of-function phenotype evidence, including additional ncRNA classes such as long ncRNAs. However, examples of high-confidence RNA genes are currently rare, and this remains a major outstanding challenge.

Predicting the functionality of rice transcribed regions

We applied the function prediction model to all rice transcribed regions that were not used to establish the models, including transcribed exons, transcribed introns, and ITRs. Among transcribed exons, 67% were predicted as functional based on T₁ and 11% were pseudogene-like based on T₂ (Fig. 5F). Among transcribed introns that are not annotated as alternatively spliced exons, only 3% were predicted as functional based on T₁ and the majority (57%) were predicted as pseudogene-like based on T₂ (Fig. 5G), indicating that the functional prediction model can effectively distinguish between exon and intron sequences.

We applied the prediction models to ITRs and found that 8% were predicted as functional based on T₁ and 61% were pseudogene-like based on T₂ (Fig. 5H). We found that there was a significant negative correlation between ITR functional likelihoods and their distance to neighboring genes (Spearman’s rank correlation, ρ = −0.17, p < 3 × 10⁻⁴⁷), with notably higher likelihoods among ITRs < 500 bp from a gene (Fig. S6). It remains unclear whether functional predictions among ITRs are influenced by the chromatin state of neighboring genes or these ITRs represent truly functional sequences.

Earlier we discussed the presence of multi-exon ITRs and it remains an open question whether multi-exon ITRs may tend to be functional compared to single exon ones. We found a significant, positive correlation between the number of exons in an assembled transcript and functional likelihood among ITRs (Spearman’s ρ = 0.12, p < 3 × 10⁻¹⁶). In addition, 7% of single exon ITRs were predicted as functional, while 13% of ITRs with >1 exon and 28% of ITRs with 5–10 exons that were predicted functional. By comparison, 70% of exon transcribed regions associated overlapping annotated genes with 5–10 exon transcripts were predicted as functional, significantly higher than ITRs with 5–10 exons (FET, p < 6 × 10⁻¹⁵). This suggests that while association with a multi-exon transcript can increase confidence that a transcript is functional, there is significant difference in confidence of predictions between multi-exon ITRs and annotated genes.

We also applied the three-class prediction model to ITR sequences, as they may represent non-coding RNA genes. When applied to ITRs, the three-class model predicts 60% ITRs as non-functional (pseudogene-like; Fig. S5). Given that <25% of miRNA sequences were predicted as non-functional, the three-class model indicates that ITRs are not likely to be primarily composed of functional non-coding sequences. These results are consistent with the results from the two-class prediction model where 61% of ITRs are predicted as non-functional based on T₂ and with a prior study showing that the majority of A. thaliana ITRs are dissimilar to benchmark non-coding RNAs and more similar to pseudogenes or unexpressed intergenic sequences²⁷. These findings suggest that PolyA + ITRs are generally dissimilar to both protein-coding and non-coding sequences and thus may primarily result from transcriptional noise, although a small percentage are likely novel genes.

Identification of evolutionary characteristics that closely associate with functional sequences

With subsets of ITRs predicted as functional and nonfunctional, we next sought out how well sequence and expression conservation characteristics correlated with functional predictions. Among rice ITRs with cross-species homologs in B. distachyon, sorghum, or maize (defined based on sequence similarity, not synteny), 36% were predicted as functional based on T₁, significantly higher than 3% for ITRs without putative homologs (Fisher’s Exact Test, p < 2.2 × 10⁻¹⁶, Fig. 6A, left column), although lower than that for exons (80%; FET, p < 2.2 × 10⁻¹⁶, Fig. 6A, right column). We next tested whether rice ITRs with expressed orthologs (defined based on synteny) in B. distachyon (diverged ~47 MYA) would primarily represent functional sequences and found that 50% of ITRs with expressed orthologs were predicted as functional based on T₁, significantly higher than ITRs with non-expressed orthologs (12%; FET, p < 0.05; Fig. 6B, left column). Similarly, 81% of transcribed exons with an expressed ortholog were predicted as functional based on T₁ (Fig. 6B, right column). Since cross-species homology was used as a feature for model building, the correlation is not surprising. But expression of an ortholog was not used as a feature, indicating that our findings provide strong support for ITR functionality as defined by the machine learning model. We next investigated the relationship between functional likelihood of ITRs/exons and duplication status. We found that single-copy ITRs exhibited significantly higher functional likelihood (median = 0.26) compared to ITR duplicates (median = 0.21; U test, p < 3 × 10⁻¹⁶; Fig. 6C, left column), but lower than those of singleton transcribed exons (median = 0.78, U test, p < 3 × 10⁻¹⁶, Fig. 6C, right column). We also found that ITRs with expressed paralogs tend to have higher functional likelihood (median = 0.25) compared to those with unexpressed paralogs (median = 0.18; U test, p < 3 × 10⁻¹⁶; Fig. 6D). Despite significant correlation with functional likelihood, only 9% and 10% of single-copy ITRs and ITRs with an expressed paralog, respectively, were predicted as functional based on T₁.

Among duplicated ITRs, younger duplicates tend to have lower functional likelihood scores (p < 0.02; Fig. 6E), but the correlation is exceedingly weak (ρ = 0.05), similar to those of pseudogenes (ρ = 0.11, p = 0.15; Fig. 6E). By comparison, transcribed exons and introns show stronger positive correlations between timing of duplication and functional prediction scores (ρ = 0.36 and 0.21, respectively; both p < 9 × 10⁻²¹; Fig. 6E). To address the question if the time scale examined may be too short (K ≤ 0.08), we assessed functional predictions of the 11 rice ITRs with retained duplicates from the >70 MYA ρ or σ WGD events. Five of these sequences (46%) were predicted as functional based on the stringent T₁, while the other six of the 11 ρ/σ ITR duplicates failed to be predicted as non-functional based on T₂. Together with cross-species sequence and expression conservation patterns, ancient duplicate retention provides strong support for the utility of our computational models for assessing sequence functionality and reaffirm our assertion that the majority of ITRs have pseudogene-like properties and likely represent noisy transcription. Meanwhile, a relatively small subset of ITRs with high functional likelihood may represent novel genes that warrant further study.

Conclusion

In this study, we investigated the cross-species and post-duplication evolutionary characteristics of intergenic transcribed regions (ITRs) in four grass species. ITR sequences are primarily species-specific and, when ITR orthologs can be identified, the majority are not expressed. Similarly, ITR paralogs, which tend to be from recent duplication events, are usually not expressed. Thus, expression of an orthologous or duplicated ITR either evolved post-speciation/duplication or ITR orthologs/paralogs tend to quickly lose ancestral expression states. Either case suggests that expression among ITRs and related sequences may evolve more rapidly compared to the significantly higher degrees of cross-species and duplicate expression conservation seen among exons. We also found only rare examples of ITRs with paralogs retained from whole genome duplication events occurring ~12–70 MYA, suggesting there may be little selective pressure on most ITRs exerted through, for example, neofunctionalization or that the majority of ITRs have evolved recently. In addition to evolutionary characteristics, we evaluated the transcriptional features and found that ITRs tend to exhibit weak and narrow expression compared to annotated protein-coding exons. Overall, the evolutionary and expression characteristics explored here are consistent with the notion that most PolyA+ intergenic expression is not subjected to strong selection and primarily represents transcriptional noise.

We also established robust functional prediction models for rice transcribed sequences using 44 evolutionary and biochemical characteristics of benchmark phenotype genes and expressed pseudogenes. These models distinguished between phenotype genes and pseudogene sequences with high accuracy, indicating that the model can identify sequences under selection (i.e. selected effect functionality). Based on model predictions, 583 ITRs in rice (8%) were classified as likely functional based on a conservative estimate of functionality (FPR of 5%). A previous study in Arabidopsis thaliana that employed selection within and between species as a criterion for defining likely functionality also found ~5% of ITRs functional⁸. In addition, in A. thaliana, a similar data integration framework and conservative functional estimate found that 7% of A. thaliana ITRs in were predicted as functional²⁷, suggesting that this proportion may represent a reasonable expectation for the proportion of potentially functional ITR sequences in plants.

Results from function prediction models indicated that ITRs with two characteristics: (1) cross-species expression conservation and (2) ancient retained duplicates primarily resemble phenotype genes. More importantly, prediction models also classified 203 (3%) of rice ITRs that lack sequence conservation as functional. This finding indicates that the data integration framework applied here can identify ITRs that may function in species-specific roles or when sequence conservation is not readily detectable²³. These two possibilities are not mutually exclusive. Ultimately, the majority of ITRs lack similarity to benchmark functional sequences. Considering that non-functionality cannot be tested but functionality can, we reiterate our argument that non-functionality should be treated as null hypothesis that is rejected when evidence suggests otherwise^8,27. Based on this logic, for most ITRs we cannot reject the null hypothesis that intergenic transcription primarily represents non-functional, transcriptional noise.

Methods

Gene, pseudogene, and random intergenic annotation

The MAKER-P (r1065) genome annotation pipeline was used to reannotate the Oryza sativa (rice) Nipponbare (IGRSP-1.0 v7), Brachypodium distachyon (v1.2) and Sorghum bicolor (sorghum; v2.1) genome assemblies as previously described^36,37. Repeats were masked using default parameters in RepeatMasker (v 4.0.3) for the sorghum and B. distachyon genomes, and a custom repeat library was created for rice using a method described previously³⁸. RepeatMasker was run within the MAKER pipeline to mask repetitive elements. To aid in gene prediction, EST evidence was provided by transcriptome assemblies generated from selected publicly available data from the National Center for Biotechnology Information Sequence Read Archive (NCBI-SRA) (Table S3) using Trinity (version 2014) with a minimum contig length of 150 bp and the Jaccard clip option³⁹. Protein evidence was provided by the SwissProt plant protein dataset (ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/taxonomic_divisions/uniprot_sprot_plants.dat.gz), with the rice, sorghum, or B. distachyon protein sequences removed. For Zea mays (maize; v. 3.21), we utilized the MAKER-P gene annotation described by Law et al.⁴⁰. Pseudogenes were identified in each species using a pipeline similar to that described by Zou et al.²⁰. We considered genome regions with at least 40 contiguous ambiguous nucleotides (Ns) as likely-unmappable, a length that represented the size of reads in RNA-sequencing (RNA-seq) datasets used in our analyses (see below). Likely-unmappable regions were excluded when calculating the size of total exon, intron, pseudogene, and intergenic space in Fig. 1B. Random sets of intergenic coordinates with equal length distributions as intergenic transcribed regions (ITRs, see below) were identified in each species. Random intergenic coordinates were sampled so that they did not overlap one another, transcribed regions, or likely-unmappable genome regions (as described above). We further filtered random intergenic coordinates to remove those that contained an ambiguous nucleotide, a step that removed 4–9% of random coordinates in each species.

Identification and classification of transcribed regions

Multiple developmentally-matched RNA-seq datasets from rice, B. distachyon, sorghum, and maize (n = 11, 11, 10, and 14 respectively) were retrieved from NCBI-SRA (Table S1)^34,35. Reads were trimmed of low-scoring ends and Illumina adapter sequences using Trimmomatic v.0.33 (LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20). Reads ≥20 nucleotides in length and with an average Phred score ≥20 were mapped to associated genomes using TopHat v.2.1.0 (-i 5000; -I 50000; all other parameters default)⁴¹. Transcribed region coordinates were identified by assembling unique-mapped reads with Cufflinks v.2.2.1 (-min-intron-length 5000; -max-intron-length 50000; -m 150; -no-effective-length-correction)⁴² while providing an associated genome sequence via the -b flag to correct for sequence-specific biases. To identify multi-exon transcripts in the rice datasets, read mapping was also performed using STAR v2.6.0c (non-default parameters included: -outSAMstrandField intronMotif -outSAMattrIHstart 0 -alignSoftClipAtReferenceEnds No), and mapped reads were assembled with Cufflinks as described above except that no intron size parameters were provided. Transcribed regions from across datasets that overlapped with one another by at least 1 nucleotide were merged. Merged transcribed regions were classified based on overlap with annotated exon, intron, and pseudogene sequences through a priority system: exon > intron > pseudogene (e.g. a transcribed region that overlapped both an exon and an intron was classified as exon). Transcribed regions that did not overlap with any gene or pseudogene annotation were classified as intergenic.

Transcribed regions were further categorized as likely protein coding or repetitive. Likely protein-coding transcribed regions were defined as those that contained a protein domain from Pfam v. 31.0⁴³ in any translated frame or had significant translated sequence similarity to a plant protein annotated in Phytozome v.10 (BLAST v. 2.2.26; BLASTX E-value < 1 × 10⁻⁵)⁴⁴. Additionally, canonical RNA gene domains were identified within ITRs by searching for significant matches (E < 1 × 10⁻⁵) with Rfam domain covariance models (v.12)⁴⁵ using Infernal v.1.1⁴⁶.

A set of likely repetitive transcribed regions were defined based on the presence of repeat-associated Pfam protein domains or high numbers of duplicate sequences. To identify repeat-associated protein domains and set duplicate thresholds to call sequences as highly repetitive, a set of benchmark repetitive elements were identified in each unmasked genome using RepeatMasker v.4.0.5 (-nolow -norna -qq) with the default RepeatMasker repeat library and a custom library of rice repeats generated using previously described methods³⁸. Transcribed regions that contained a Pfam v.31.0 protein domain that was significantly enriched among interspersed repeats relative to exon transcribed regions (Fisher’s exact test; adjusted p < 0.05; Benjamini-Hochberg procedure)⁴⁷ were considered likely-repetitive. We also identified a threshold based on the number of duplicate sequences required to call a sequence as highly-repetitive based on the duplicate counts of long terminal repeats (a subset of the interspersed repetitive elements identified by RepeatMasker) and transcribed regions that overlapped exons, through F-measure maximization. Resulting duplicate count thresholds to consider a transcribed region as repetitive were 10, 15, 33, and 202 for rice, B. distachyon, sorghum, and maize, respectively. For all duplication-related analysis, we excluded sequences that were classified as repetitive, as they were considered duplicated by definition.

Sequence and expression conservation

Cross-species sequence matches were identified using BLAST searches (BLAST v. 2.2.26; BLASTN E-value < 1 × 10⁻⁵) by searching nucleotide sequences of transcribed regions against repeat-masked whole genome sequences. The 95^th percentile E-values for random unexpressed sequences that lacked similarity to protein-coding or repetitive sequences were determined for all species: 1 × 10⁻⁵, 1 × 10⁻⁵, 1 × 10⁻²², and 1 × 10⁻²⁴ for rice, B. distachyon, sorghum, and maize, respectively. A sequence was considered conserved if it had a cross-species match that was more significant than a 95^th percentile E-value. We subtracted 5% from final proportions of conserved sequences to adjust for the 5% expected false positive rate. BLAST searches were performed within-species to identify duplicates and cross-species to identify putative homologs. Per base substitution rates (K) between within-species query and match sequences were calculated with PHAST⁴⁸ using a generalized time reversible model after aligning matched regions with EMBOSS-Needle v.6.5.7.0.

Expression conservation across species and following duplication was assessed by determining whether a cross-species or within-species match was overlapped by a transcribed region (as described above). Reciprocal matches between exon, intron, and intergenic transcribed regions for cross-species syntenic gene blocks (see below) were included in percent commonality calculations (Fig. 3B,C), which was calculated as the number of expressed tissues in common between a pair of transcribed regions divided by the total number of expressed tissues. The five tissues that were in common among all four species were considered: embryo, endosperm, seed, leaf, and anther. When replicate datasets were available, transcription evidence in a single dataset was required to consider a sequence expressed in the tissue. Similarly, expression in either seed datasets – 5 days after pollination (DAP) or 10 DAP – was required.

Expected percent commonality is affected by the expression breadth of two transcripts. For example, two orthologous transcribed regions that are both expressed in four out of five tissues have a minimum % commonality of 60%, while two orthologous transcribed regions both expressed in a single tissue have 20 possibilities for 0% commonality. As transcribed exons are expressed in more tissues than ITRs or transcribed introns (Fig. S1C), their % commonality cannot be directly compared. Instead, a subset of orthologous transcribed exons with expression breadth that matched ITR and transcribed intron pairs was selected to provide a direct comparison. For each orthologous ITR pair, pairs of orthologous transcribed exons that showed the same expression breadth as the ITR pair were identified. For example, in an orthologous ITR pair, if one sequence was expressed in one tissue and the second sequence in was expressed in two tissues, all orthologous transcribed exon pairs where one sequence was expressed in one tissue and the second was expressed in two tissues were identified. From this expression breadth-matched set of orthologous transcribed exon pairs, two pairs were randomly selected and used for % commonality calculations (Fig. 3C). The same framework was used to generate direct transcribed exon comparisons for orthologous transcribed intron pairs (Fig. 3D). Note that because the exon comparison was tailored specifically to the expression breadth distributions observed in pairs of orthologous ITRs or transcribed introns, the distribution of % commonality among transcribed exons is distinct in Fig. 3C,D. Random expectations of percent commonality (Fig. 3C,D, Rn) were calculated by randomly selecting tissues to match the expression breadth of intergenic and intron transcribed region pairs. For each orthologous ITR and transcribed intron pairs, 25 random pairs of tissues were selected that matched the observed expression breadth. Similar to the comparisons generated among orthologous transcribed exon pairs, the random pairs were generated specific for the expression observed in ITR or intron pairs, and so the distribution of random % commonality is distinct for the two sequence types. The probability that a tissue would be selected for a random set was proportional to how often it appeared among transcribed regions with expression conservation.

Identification of syntenic gene blocks

We identified cross-species and within-species syntenic blocks by identifying of sets of collinear genes using MCScanX v.2⁴⁹. Multiple minimum gene pair and maximum gap parameters were tested when generating collinear blocks (Table S4) and we utilized values of 10 and 10, respectively, for within-species collinear blocks and 10 and 2, respectively, for cross-species blocks. Cross-species blocks were identified between the more-closely-related species pairs: rice/B. distachyon and maize/sorghum. The rates of synonymous substitutions (Ks) between homologous protein-coding genes anchoring within-species syntenic blocks were calculated using the yn00 package in the Phylogenetic Analysis by Maximum Likelihood software⁵⁰. Syntenic blocks with a median Ks ≥ 0.7 across all anchor genes in the syntenic block in all four species were considered associated with the ρ/σ whole genome duplication (WGD) events^31,32, while syntenic blocks in maize with a median Ks < 0.7 were associated with the more recent 12 MYO maize WGD (Fig. S4)³³. An additional 254 gene-pair syntenic block in rice was identified with a median Ks of 0.13, suggesting recent duplication. However, due to the uncertain nature of the origin and timing of the duplication event⁵¹, this block was excluded from further analysis. For exon and intron transcribed regions, syntenic duplicates and orthologs were identified as BLASTN matches (E-value < 1 × 10⁻⁵) that overlapped a homologous anchor gene. For ITRs, and random intergenic sequences, syntenic duplicates and orthologs were BLASTN matches that were present within corresponding block regions circumscribed by homologous anchor genes (see Fig. 4B).

Rice functional prediction features

Transcription activity features

Twelve transcription-related features were generated for use in rice function prediction models (Table S2). The first 9 features were FPKM values (referred to as expression level; Level in Table S2) from each of the 9 tissues represented in the RNA-seq datasets described above. For replicate datasets (leaf and endosperm), expression level was taken as the average FPKM value if a region was expressed in both replicate datasets, and the single FPKM value otherwise. If a transcribed region was not expressed in an RNA-seq dataset, expression level was set to 0. Two additional features were represented by the maximum expression level among all RNA-seq datasets and the median FPKM among datasets where a transcribed region was expressed. The final feature was the expression breadth of a sequence, represented by the total number of tissues in which a sequence was expressed. For the breadth calculations, both seed datasets (5 and 10 DAP) and inflorescence datasets (early and emerging) were considered as a single tissue.

Sequence conservation features

Three sequence conservation-related features were generated. The first feature was the −log₁₀ of the minimum BLASTN E-value to a sequence in B. distachyon, sorghum, or maize. E-values of 0 were set to 180. A threshold of 1 × 10⁻⁵ was required to consider a match significant. Sequences without a significant cross-species match where given a score of 0. The second sequence-conservation feature was based on blocks of conserved nucleotides present across all four species were identified using the LASTZ/MULTIZ paradigm with rice as the target genome^52,53,54 (http://genomewiki.ucsc.edu/index.php/Whole_genome_alignment_howto). Among all conserved nucleotide blocks (CNBs; n = 60,801), we identified those that were exonic (n = 13,616), intronic (n = 125), or intergenic (n = 525) in all four species and were overlapped only by transcribed regions of the associated type (e.g. a conserved nucleotide block that was intergenic in all four species and was only overlapped by ITRs). The second conservation feature was the proportion of a sequence that overlapped a CNB. phastCons scores were calculated for each rice nucleotide within a conserved nucleotide block⁵⁵, with conserved and non-conserved states estimated using the–estimate-trees option. The third sequence-conservation feature was the median per-base phastCons score across the proportion of a sequence that overlapped a conserved nucleotide block. If a sequence did not overlap a conserved block the phastCons score was set to 0.

Histone mark and nucleosome occupancy features

Twelve histone mark-related features among 10 histone marks were calculated based on 18 chromatin immunoprecipitation sequencing (ChIP-seq) datasets from NCBI-SRA (Table S5). Reads were trimmed as described above and mapped to the rice Michigan State University (MSU) v.7 genome with Bowtie v2.2.5 (default parameters). Spatial Clustering for Identification of ChIP-Enriched Regions v.1.1⁵⁶ was used to identify significant ChIP-seq peaks with a non-overlapping window size of 200, a gap parameter of 600, and an effective genome size of 0.68⁵⁷. For datasets with control total histone or protein datasets, a false discovery rate (FDR) ≤0.05 was utilized (see Table S5). Peaks for histone marks with multiple datasets were merged. The first 10 histone mark-related features were represented by the percent overlap of a sequence with histone mark peaks for each histone marks (‘coverage’ in Table S2). The other two features were the number of activation-associated histone marks and repression-associated histone marks with a peak that overlapped a sequence. Eight marks were considered activation-associated and two were considered repression-associated (Table S5). A single nucleosome occupancy feature was also generated from micrococcal nuclease sequencing (MNase-seq) data. MNase-seq data was generated by Wu et al.⁵⁸ and processed by Liu et al.⁵⁹. The nucleosome occupancy feature was calculated as MNase-seq average read depth across the length of a sequence.

DNA methylation features

Sixteen DNA methylation features were calculated from bisulfite-sequencing (BS-seq) datasets from four tissues: embryo (NCBI-SRA ID: SRR059000), endosperm (SRR059005), leaf (SRR618545), and panicle (SRR1520042). BS-seq reads were trimmed as described above and processed with Bismark v.3 (default parameters) to identify the number of reads that call cytosines as methylated and unmethylated in CG, CHG, and CHH (H = A, C, or T) contexts. The first 12 features were represented by the methylation level of a sequence in CG, CHG, and CHH contexts for each of the four tissues. Methylation level of a sequence was calculated as the number of reads that mapped to CG, CHG, or CHH sites that called a cytosine site as methylated divided by the total number of reads mapping to CG, CHG, or CHH sites within the sequence. A minimum of 5 reads across 5 cytosine sites were required to calculate methylation level. Multiple minimum read (range: 1–20) and site (range: 1–10) requirements were tested and found to have little effect on the ability of resulting methylation level features to distinguish between phenotype exons and transcribed pseudogene sequences (Table S6). The final four DNA methylation features were represented by whether a sequence exhibited a methylation pattern consistent with gene body methylation (GBM) in each of the four tissues. The GBM pattern is presence of CG methylation and absence of CHG and CHH methylation. Presence or absence of CG, CHG, and CHH states within a sequence were determined by binomial tests of the methylation level of a sequence (as described above) compared to the background methylation level across the whole genome for a given cytosine context. Binomial test P-values were corrected for multiple testing using the Benjamini and Hochberg method (FDR ≤ 0.05) (Benjamini and Hochberg 1995). Sequences that were significantly enriched in CG methylation relative to the genome background and not significantly enriched in CHG and CHH methylation were considered to be gene body methylated.

Machine learning approach

The machine learning approach consisted of four steps: establishing a set of benchmark functional and nonfunctional sequences, identifying characteristics associated with benchmark sequences, integrating these characteristics via statistical learning methods to generate a functional prediction model, and applying function prediction models to all transcribed regions, including ITRs. We utilized the random forest algorithm implemented in the Scikit-learn software⁶⁰ to perform machine learning runs aimed at distinguishing between functional and nonfunctional transcribed regions in rice. Benchmark functional transcribed regions were those that overlapped exons of genes with documented loss-of-function phenotypes (referred to as phenotype exons)^61,62. Benchmark nonfunctional sequences were represented by transcribed regions that overlapped pseudogene annotation (referred to as transcribed pseudogenes). We further filtered out pseudogenes that overlapped an exon from the MSU v.7 gene annotation. Benchmark non-coding sequences were transcribed regions that overlapped a set of high-confidence miRNA gene annotations from miRBase (referred to as transcribed miRNAs)⁶³. Prior to model building, missing data points among features were imputed by sequential regression imputation implemented in the mice package in R (m = 1, seed = 500)⁶⁴.

A machine learning-based prediction to distinguish between phenotype exons and transcribed pseudogenes was generated. We generated 100 balanced datasets that included equal proportions of phenotype exon and transcribed pseudogene sequences that were used for training and 10-fold cross-validation was implemented (i.e. 90% of a dataset was used for training and the held-out 10% used for testing). Parameter sweeps of maximum tree depth (3, 5, 10, and 50) and proportion of random features (10%, 25%, 50%, 75%, square root, and log₂) values were performed, with 10 and 25% providing the highest performance, respectively. For each of the 100 balanced datasets, a prediction score for each transcribed region was generated that was equal to the proportion of 500 random forest trees that predicted the region as a phenotype exon; a final prediction score was calculated as the median of the 100 scores from the balanced datasets. Two thresholds to predict transcribed regions as likely-functional were generated based on a false positive rate of 5% (T₁; score threshold = 0.60) and a false negative rate of 5% (T₂; score threshold = 0.29).

A three-class prediction model developed with random forest using phenotype exons, transcribed pseudogenes, and a set of 22 high-confidence miRNA annotations from miRBase (referred to as transcribed miRNAs)⁶³ as benchmark sequences. The machine learning procedure was the same as the two-class model described above, except that 20-fold cross-validation was used and 250 balanced training datasets were generated per cross-validation fold to adjust for the low number of transcribed miRNA sequences. When applied, the three-class model provided three scores between 0 and 1 to indicate similarity to phenotype exons, transcribed pseudogenes, and transcribed miRNAs, where score is proportional to the number of trees out of 500 that predicted as given class. Sequences were classified based on the highest score, with those classified as phenotype exon or transcribed miRNA predicted as functional and those as pseudogene predicted as non-functional. The Python script utilized to generate these predictions (ML_classification.py) is available on GitHub: https://github.com/ShiuLab/ML-Pipeline.

We explored the performance of prediction models from a simpler logistic regression algorithm and those built including fewer, less-correlated features. For this analysis, we selected a balanced test set that included 25 each of randomly selected phenotype exons and expressed pseudogenes (n_{testing instances} = 50). Models were developed and features selected using a balanced set of 225 each of randomly selected phenotype exons and expressed pseudogenes (n_{training instances} = 450) that were not included in the testing set. Generation of testing and training sets were repeated 100 times using a bootstrap approach. Feature values were quantile normalized prior to analysis. We found that models generated using logistic regression had lower AUC-ROC values (median AUC-ROC = 0.90; Fig. S7A) compared to those based on random forests (median AUC-ROC = 0.94; U test, p < 6 × 10⁻¹¹; Fig. S7B), indicating that a simpler classifier works, but does not perform as well as random forest. We next used feature selection by least absolute shrinkage and selection operator (LASSO), which tends to remove highly-correlated features, with multiple λ values to assess performance of random forest models using fewer features (Fig. S7C,D). We found that when 20–30 features out of 44 were selected (LASSO λ = 1 × 10⁻²), similar performance (median AUC-ROC = 0.93) was observed relative to performance with the full random forest model (median AUC-ROC = 0.94; U test, p = 0.44; Fig. S7B). However, models generated using 8–16 features (LASSO λ = 5 × 10⁻²) had significantly worse performance compared to the full model (U test, p < 3 × 10⁻⁴), although the effect size was small (median AUC-ROC = 0.92; Fig. S7B). In the absence of a highly simplified model (either by simpler algorithm or built on very few features) that performed similarly or better than the full random forest model, we used predictions based on the full random forest model for the downstream analysis of the functionality of rice sequences. This analysis was performed in R using the aod (logistic regression)⁶⁵, randomForest⁶⁶, and glmnet (LASSO)⁶⁷ packages.

Data Availability

All data are available in the manuscript or associated supplemental data.

References

Bertone, P. et al. Global Identification of Human Transcribed Sequences with Genome Tiling Arrays. Science (80-.). 306, 2242–2246 (2004).
Article ADS CAS Google Scholar
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Article ADS Google Scholar
Brown, J. B. et al. Diversity and dynamics of the Drosophila transcriptome. Nature 512, 393–399 (2014).
Article ADS CAS Google Scholar
Boeck, M. E. et al. The time-resolved transcriptome of C. elegans. Genome Res. 26, 1441–1450 (2016).
Article CAS Google Scholar
Nagalakshmi, U. et al. The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing. Science (80-.). 320, 1344–1349 (2008).
Article ADS CAS Google Scholar
Yamada, K. et al. Empirical analysis of transcriptional activity in the Arabidopsis genome. Science (80-.). 302, 842–846 (2003).
Article ADS CAS Google Scholar
Nobuta, K. et al. An expression atlas of rice mRNAs and small RNAs. Nat. Biotechnol. 25, 473–477 (2007).
Article CAS Google Scholar
Moghe, G. D. et al. Characteristics and significance of intergenic polyadenylated RNA transcription in Arabidopsis. Plant Physiol. 161, 210–24 (2013).
Article CAS Google Scholar
Krishnakumar, V. et al. Araport: the Arabidopsis information portal. Nucleic Acids Res. 43, D1003–9 (2015).
Article CAS Google Scholar
Liu, D., Mewalal, R., Hu, R., Tuskan, G. A. & Yang, X. New technologies accelerate the exploration of non-coding RNAs in horticultural plants. Hortic. Res. 4, 1–8 (2017).
Article Google Scholar
van Bakel, H., Nislow, C., Blencowe, B. J. & Hughes, T. R. Most ‘dark matter’ transcripts are associated with known genes. PLoS Biol. 8 (2010).
Tan, J. Y. et al. Extensive microRNA-mediated crosstalk between lncRNAs and mRNAs in mouse embryonic stem cells. Genome Res. 25, 655–666 (2015).
Article CAS Google Scholar
Guil, S. & Esteller, M. Cis-acting noncoding RNAs: friends and foes. Nat. Struct. Mol. Biol. 19, 1068–1075 (2012).
Article CAS Google Scholar
Hanada, K. et al. Small open reading frames associated with morphogenesis are hidden in plant genomes. Proc. Natl. Acad. Sci. USA 110, 2395–400 (2013).
Article ADS CAS Google Scholar
Struhl, K. Transcriptional noise and the fidelity of initiation by RNA polymerase II. Nat. Struct. Mol. Biol. 14, 103–105 (2007).
Article CAS Google Scholar
Carvunis, A.-R. et al. Proto-genes and de novo gene birth. Nature 487, 370–374 (2012).
Article ADS CAS Google Scholar
Amundson, R. & Lauder, G. V. Function without purpose. Biol. Philos. 9, 443–469 (1994).
Article Google Scholar
Graur, D. et al. On the immortality of television sets: ‘function’ in the human genome according to the evolution-free gospel of ENCODE. Genome Biol. Evol. 5, 578–90 (2013).
Article Google Scholar
Doolittle, W. F., Brunet, T. D. P., Linquist, S. & Gregory, T. R. Distinguishing between ‘function’ and ‘effect’ in genome biology. Genome Biol. Evol. 6, 1234–1237 (2014).
Article Google Scholar
Zou, C. et al. Evolutionary and expression signatures of pseudogenes in Arabidopsis and rice. Plant Physiol. 151, 3–15 (2009).
Article CAS Google Scholar
Pei, B. et al. The GENCODE pseudogene resource. Genome Biol. 13, R51 (2012).
Article CAS Google Scholar
Pang, K. C., Frith, M. C. & Mattick, J. S. Rapid evolution of noncoding RNAs: lack of conservation does not mean lack of function. Trends Genet. 22, 1–5 (2006).
Article CAS Google Scholar
Ponting, C. P. Biological function in the twilight zone of sequence conservation. BMC Biol. 15, 71 (2017).
Article Google Scholar
Kellis, M. et al. Defining functional DNA elements in the human genome. Proc. Natl. Acad. Sci. USA 111, 6131–8 (2014).
Article ADS CAS Google Scholar
Gulko, B., Gronau, I., Hubisz, M. J. & Siepel, A. Probabilities of Fitness Consequences for Point Mutations Across the Human Genome (2014).
Tsai, Z. T.-Y., Lloyd, J. P. & Shiu, S.-H. Defining Functional Genic Regions in the Human Genome through Integration of Biochemical, Evolutionary, and Genetic Evidence. Mol. Biol. Evol. (2017).
Lloyd, J. P., Tsai, Z. T.-Y., Sowers, R. P., Panchy, N. L. & Shiu, S.-H. A Model-Based Approach for Identifying Functional Intergenic Transcribed Regions and Noncoding RNAs. Mol. Biol. Evol. 35, 1422–1436 (2018).
Article CAS Google Scholar
Skendzic, E. M., Columbus, J. T. & Cerros-Tlatilpa, R. Phylogenetics of Chloridoideae (Gramineae): A preliminary study based on nuclear ribosomal internal transcribed spacer and chloroplast trnL-F sequences. Aliso A J. Syst. Evol. Bot. 23, 530–544 (2007).
Google Scholar
Liu, Q., Liu, H., Wen, J. & Peterson, P. M. Infrageneric phylogeny and temporal divergence of Sorghum (Andropogoneae, Poaceae) based on low-copy nuclear and plastid sequences. PLoS One 9 (2014).
Massa, A. N. et al. Gene space dynamics during the evolution of aegilops tauschii, brachypodium distachyon, Oryza sativa, and sorghum bicolor genomes. Mol. Biol. Evol. 28, 2537–2547 (2011).
Article CAS Google Scholar
Paterson, A. H., Bowers, J. E. & Chapman, B. A. Ancient polyploidization predating divergence of the cereals, and its consequences for comparative genomics. Proc. Natl. Acad. Sci. USA 101, 9903–8 (2004).
Article ADS CAS Google Scholar
Tang, H., Bowers, J. E., Wang, X. & Paterson, A. H. Angiosperm genome comparisons reveal early polyploidy in the monocot lineage. Proc. Natl. Acad. Sci. USA 107, 472–477 (2010).
Article ADS CAS Google Scholar
Swigoňová, Z. et al. Close split of sorghum and maize genome progenitors. Genome Res. 14, 1916–1923 (2004).
Article Google Scholar
Davidson, R. M. et al. Utility of RNA Sequencing for Analysis of Maize Reproductive Transcriptomes. Plant Genome J. 4, 191 (2011).
Article CAS Google Scholar
Davidson, R. M. et al. Comparative transcriptomics of three Poaceae species reveals patterns of gene expression evolution. Plant J. 71, 492–502 (2012).
CAS PubMed Google Scholar
Cantarel, B. L. et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 18, 188–96 (2008).
Article CAS Google Scholar
Campbell, M. S., Holt, C., Moore, B. & Yandell, M. Genome Annotation and Curation Using MAKER and MAKER-P. Current Protocols in Bioinformatics 2014 (2014).
Campbell, M. S. et al. MAKER-P: A Tool Kit for the Rapid Creation, Management, and Quality Control of Plant Genome Annotations. Plant Physiol. 164, 513–524 (2014).
Article CAS Google Scholar
Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).
Article CAS Google Scholar
Law, M. et al. Automated Update, Revision, and Quality Control of the Maize Genome Annotations Using MAKER-P Improves the B73 RefGen_v3 Gene Models and Identifies New Genes. Plant Physiol. 167, 25–39 (2015).
Article CAS Google Scholar
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Article Google Scholar
Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
Article CAS Google Scholar
Finn, R. D. et al. The Pfam protein families database: Towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).
Article CAS Google Scholar
Goodstein, D. M. et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 40, D1178–86 (2012).
Article CAS Google Scholar
Nawrocki, E. P. et al. Rfam 12.0: Updates to the RNA families database. Nucleic Acids Res. 43, D130–D137 (2015).
Article CAS Google Scholar
Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).
Article CAS Google Scholar
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).
MathSciNet MATH Google Scholar
Hubisz, M. J., Pollard, K. S. & Siepel, A. PHAST and RPHAST: phylogenetic analysis with space/time models. Brief. Bioinform. 12, 41–51 (2011).
Article CAS Google Scholar
Wang, Y. et al. MCScanX: A toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, 1–14 (2012).
Article ADS Google Scholar
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–91 (2007).
Article CAS Google Scholar
Wang, X., Tang, H. & Paterson, A. H. Seventy million years of concerted evolution of a homoeologous chromosome pair, in parallel, in major Poaceae lineages. Plant Cell 23, 27–37 (2011).
Article Google Scholar
Blanchette, M. et al. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 14, 708–15 (2004).
Article CAS Google Scholar
Harris, R. S. Improved pairwise alignment of genomic DNA. (The Pennsylvania State University, 2007).
Hupalo, D. & Kern, A. D. Conservation and functional element discovery in 20 angiosperm plant genomes. Mol. Biol. Evol. 30, 1729–44 (2013).
Article CAS Google Scholar
Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–50 (2005).
Article CAS Google Scholar
Xu, S., Grullon, S., Ge, K. & Peng, W. Spatial clustering for identification of ChIP-enriched regions (SICER) to map regions of histone methylation patterns in embryonic stem cells. Methods Mol. Biol. 1150, 97–111 (2014).
Article CAS Google Scholar
Koehler, R., Issac, H., Cloonan, N. & Grimmond, S. M. The uniqueome: a mappability resource for short-tag sequencing. Bioinformatics 27, 272–274 (2011).
Article CAS Google Scholar
Wu, Y., Zhang, W. & Jiang, J. Genome-Wide Nucleosome Positioning Is Orchestrated by Genomic Regions Associated with DNase I Hypersensitivity in Rice. PLoS Genet. 10 (2014).
Liu, M.-J. et al. Determinants of nucleosome positioning and their influence on plant gene expression. Genome Res. 25, 1182–1195 (2015).
Article CAS Google Scholar
Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
MathSciNet MATH Google Scholar
Lloyd, J. P., Seddon, A. E., Moghe, G. D., Simenc, M. C. & Shiu, S.-H. Characteristics of Plant Essential Genes Allow for within- and between-Species Prediction of Lethal Mutant Phenotypes. Plant Cell 27, 2133–2147 (2015).
Article CAS Google Scholar
Oellrich, A. et al. An ontology approach to comparative phenomics in plants. Plant Methods 11, 10 (2015).
Article Google Scholar
Kozomara, A. & Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–73 (2014).
Article CAS Google Scholar
Buuren, S. van & Groothuis-Oudshoorn, K. mice: Multivariate Imputation by Chained Equations in R. J. Stat. Softw. 45 (2011).
Lesnoff, M. & Lancelot, R. aod: Analysis of Overdispersed Data (2012).
Liaw, A. & Wiener, M. Classification and Regression by randomForest. R News 2, 18–22 (2002).
Google Scholar
Friedman, J., Hastie, T. & Tibshirani, R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J. Stat. Softw. 33, 1–22 (2010).
Article Google Scholar

Download references

Acknowledgements

The authors wish to thank Ning Jiang for providing a rice repeat library, Nicholas Panchy for identifying pseudogenes, Robin Buell for helpful discussions, and Jack R.S. DeCatters for inspiration and support. This work was supported by the National Science Foundation (NSF) [IOS-1546617 and DEB-1655386 to S.-H.S.]; the Department of Energy Great Lakes Bioenergy Research Center [BER DE-SC0018409 to S.-H.S]; NSF Graduate Research Fellowship to C.B.A.; and the Michigan State University Dissertation Completion Fellowship to J.P.L. Funding bodies had no role in design of the study or collection, analysis, and interpretation of data or in writing the manuscript.

Author information

John P. Lloyd
Present address: Departments of Human Genetics and Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA

Authors and Affiliations

Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
John P. Lloyd, Megan J. Bowman, Christina B. Azodi, Kevin L. Childs & Shin-Han Shiu
Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA, 16801, USA
Rosalie P. Sowers
Section of Plant Biology, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, 14853, USA
Gaurav D. Moghe
Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI, 48824, USA
Shin-Han Shiu

Authors

John P. Lloyd
View author publications
You can also search for this author in PubMed Google Scholar
Megan J. Bowman
View author publications
You can also search for this author in PubMed Google Scholar
Christina B. Azodi
View author publications
You can also search for this author in PubMed Google Scholar
Rosalie P. Sowers
View author publications
You can also search for this author in PubMed Google Scholar
Gaurav D. Moghe
View author publications
You can also search for this author in PubMed Google Scholar
Kevin L. Childs
View author publications
You can also search for this author in PubMed Google Scholar
Shin-Han Shiu
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

J.P.L., G.D.M. and S.-H.S. designed the research. J.P.L., M.J.B., C.A.B., R.P.S. and K.L.C. performed the research. J.P.L., G.D.M., M.J.B., K.L.C., C.A.B. and S.-H.S. wrote the article.

Corresponding author

Correspondence to Shin-Han Shiu.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplemental Information

Supplementary Dataset 1

Supplementary Dataset 2

Supplementary Dataset 3

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Lloyd, J.P., Bowman, M.J., Azodi, C.B. et al. Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae. Sci Rep 9, 12122 (2019). https://doi.org/10.1038/s41598-019-47797-y

Download citation

Received: 08 January 2019
Accepted: 19 July 2019
Published: 20 August 2019
DOI: https://doi.org/10.1038/s41598-019-47797-y

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.