The core challenge in plant transcriptomics is that recovering a complete transcript sequence does not automatically reveal its biological function. A transcript may contain an open reading frame (ORF) that is too short to encode a functional protein, or it may lack homology to any known protein in existing databases, leaving its status unclear. Traditional annotation pipelines often force these ambiguous sequences into a binary coding or noncoding category, which can lead to the misclassification of novel genes or the dismissal of functional noncoding RNAs. The new source-resolved decomposition framework addresses this by integrating multiple lines of evidence, including coding potential predictions from tools like PINC and CPC2, protein homology searches via BLASTx against the Swiss-Prot database, ORF architecture analysis using TransDecoder, and repeat element evidence from RepeatMasker. This multi-evidence approach allows researchers to see exactly which factors are contributing to the uncertainty of a specific transcript.

Using this integrated framework, the researchers identified a substantial “grey zone” within the Arrhenatherum elatius transcriptome. Specifically, 20,178 transcripts, representing 31.71% of the total analyzed set, fell into this operational coding-noncoding grey zone. This high percentage underscores the difficulty of annotating non-model plant genomes, where a significant portion of the transcriptome remains functionally unresolved. The framework does not simply label these transcripts as unknown; instead, it decomposes the ambiguity into specific, mutually exclusive operational evidence states and overlapping ambiguity-source indicators. This level of detail is crucial for biologists who need to understand not just that a transcript is ambiguous, but why it is ambiguous, allowing for more targeted experimental validation strategies.

The analysis revealed that short-ORF-associated ambiguity was the largest single source of uncertainty, involving 7,383 transcripts. These are sequences that contain open reading frames, but they are often too short to be confidently classified as protein-coding genes. Following this, homology-unsupported long ORFs accounted for 6,977 transcripts, representing sequences that have the structural length to be coding but lack identifiable protein matches in current databases. Prediction conflicts, where different computational tools disagree on the coding potential of a sequence, affected 5,710 transcripts, while repeat-rich ambiguity, associated with transposable elements and other repetitive sequences, impacted 4,766 transcripts. This breakdown provides a clear map of the computational challenges facing plant genome annotators and highlights the specific types of evidence that are most often missing or conflicting in non-model species.

To test the robustness of their framework, the researchers conducted sensitivity analyses using various ORF-length thresholds, ranging from 50 to 150 amino acids. They found that 16,738 transcripts remained consistently within the grey-zone union across these different thresholds, indicating a stable core of ambiguous sequences. However, 4,170 transcripts showed threshold-dependent membership, meaning their classification changed depending on the minimum ORF length required. This finding is important for methodological transparency, as it demonstrates how sensitive transcript classification can be to arbitrary parameter choices. By explicitly reporting this variability, the study encourages other researchers to consider the impact of threshold selection on their own transcriptomic analyses, fostering a more nuanced and reproducible approach to genome annotation.

The study also examined the role of repetitive elements in transcript ambiguity. Under a specific refinement using a 0.5 coverage threshold, 4,405 transcripts met the definition of all-repeat ambiguity, while 2,359 met the definition of transposable element (TE)-only ambiguity. All 2,359 TE-supported ambiguity-source targets had valid TE-consensus coordinates, confirming the reliability of the repeat annotation. This is a critical finding because transposable elements can create chimeric transcripts or mimic coding sequences, leading to false positives in gene prediction. By isolating the contribution of repeats to the ambiguity, the framework helps distinguish between true biological novelty and artifacts of genome evolution, which is essential for accurate functional annotation.

As a comparative baseline, the researchers applied the six-state operational assignment logic to 39,936 transcripts from Daucus carota, the wild carrot. This limited implementation served to test whether the framework could be applied to a different species, even if it did not establish broad cross-species generalizability. The results from Daucus carota provided a useful context for understanding the specificity of the Arrhenatherum elatius findings. While the exact numbers and proportions of ambiguous transcripts differed between the two species, the overall structure of the evidence space remained consistent. This suggests that the types of ambiguity identified in the grass transcriptome are not unique to Arrhenatherum but may be common features of non-model plant genomes, highlighting the potential utility of the framework for a wide range of plant species.

Further analysis of the sequence composition of the ambiguous transcripts revealed statistically detectable but modest state-associated structure in 3-mer and 4-mer representations. This indicates that while the sequences in the grey zone are not random, they do not possess the strong compositional biases typically seen in clearly coding or noncoding sequences. Additionally, a Gene Ontology (GO) sensitivity test was performed, testing 137 terms and identifying 68 enriched terms after excluding shared best-hit accessions. This functional enrichment analysis provides clues about the potential biological roles of the ambiguous transcripts, suggesting that they are not merely noise but may encode functional products or regulatory RNAs with specific biological activities. These findings add a layer of biological context to the computational decomposition, linking the abstract evidence states to tangible functional categories.

The authors emphasize that the coding-noncoding ambiguity in the Arrhenatherum elatius full-length transcriptome forms a structured computational evidence space rather than a set of validated long noncoding RNAs or confirmed novel protein-coding genes. This distinction is vital for interpreting the results; the framework does not claim to have definitively classified every transcript, but rather to have provided a transparent and detailed map of the uncertainty. By preserving the evidence responsible for uncertainty, the study allows for future re-analysis as new data and tools become available. The source-resolved framework thus serves as a dynamic resource for the scientific community, enabling continuous refinement of transcript annotations as our understanding of plant biology deepens.

In conclusion, this study provides a robust and transparent method for handling the complex ambiguity in plant transcriptomes. By decomposing the grey zone into specific evidence sources, the researchers have offered a powerful tool for prioritizing transcripts for further study. The application to Arrhenatherum elatius and the comparative baseline with Daucus carota demonstrate the framework’s applicability and robustness. As plant genomics continues to expand beyond model species, such detailed computational frameworks will be essential for unlocking the functional potential of non-model plant genomes, ultimately contributing to our understanding of plant diversity, ecology, and evolution.

Subject of Research: Computational decomposition of coding and noncoding transcript ambiguity in non-model plant genomes

Article Title: Source-resolved decomposition of coding–noncoding ambiguity in a non-model plant full-length transcriptome

Article References: Hou, M., Hu, Q., Si, Q., Cai, J., Yin, Q., & Li, Y. (2026). Source-resolved decomposition of coding–noncoding ambiguity in a non-model plant full-length transcriptome. Plant Methods. https://doi.org/10.1186/s13007-026-01608-4

Image Credits: AI Generated

DOI: 10.1186/s13007-026-01608-4

Keywords: transcriptomics, plant genomics, noncoding RNA, gene annotation, Arrhenatherum elatius, computational biology, open reading frames, transposable elements, sequence analysis, bioinformatics, Source-resolved, decomposition

A team led by Megan D. Schertzer and Stella H. Park of the New York Genome Center and Columbia University, together with Jiayu Su, Fairlie Reese, Gloria M. Sheynkman and senior author David A. Knowles, believes the field has been answering that question in the wrong way. In a study published in Genome Biology, the researchers propose replacing the crude filtering steps that dominate current pipelines with a single, principled number borrowed from information theory: perplexity. Their central claim is provocative. Instead of discarding rare isoforms as noise, every transcript a gene produces, no matter how scarce, should contribute proportionally to that gene’s measured diversity.

To understand why this matters, it helps to look at how isoform analysis currently works. Long-read sequencing experiments inevitably generate some technical artifacts, spurious transcript models that do not correspond to real RNA molecules. Pipelines such as SQANTI3, which the new study uses to classify transcript structures into quality categories, help researchers separate genuine isoforms from these errors. But after artifact removal, most workflows then apply an expression threshold, keeping only isoforms that exceed some minimum abundance, typically measured in transcripts per million. Those thresholds are, as the authors point out, arbitrary. An isoform sitting just below the cutoff is thrown away, even though it may be a bona fide transcript with a real biological function. The result is a systematic undercounting of diversity that varies from study to study depending on where each team happens to set its cutoff, undermining reproducibility across labs and datasets.

Perplexity offers a way out of this arbitrariness. The concept originates in Shannon entropy, the foundational measure of uncertainty in information theory. If you treat the isoforms of a gene as the possible outcomes of a random draw, weighted by their relative abundances, the Shannon entropy quantifies how uncertain you are about which isoform you will draw next. Exponentiating that entropy yields perplexity, which has an elegant and intuitive interpretation: it is the effective number of isoforms a gene produces. A gene that expresses one dominant transcript with only trace alternatives has a perplexity close to one. A gene that spreads its expression evenly across ten isoforms has a perplexity approaching ten. The metric is closely related to the Hill numbers that ecologists have long used to measure biodiversity, a theoretical connection the authors develop in their supplementary materials, drawing on the framework formalized by Tom Leinster.

The beauty of this formulation is that no isoform is ever discarded. A transcript representing one percent of a gene’s output contributes to the perplexity calculation in proportion to its abundance, exactly as its share of the expression distribution dictates. Rare isoforms nudge the effective count upward; abundant ones dominate it. The measurement is continuous, interpretable and independent of any researcher-chosen threshold. In effect, perplexity converts the messy, long-tailed distribution of isoform abundances that long-read sequencers reveal into a single number that means the same thing in every dataset, every tissue and every laboratory.

To demonstrate that the metric delivers on this promise, the team applied it to a substantial trove of public data: 124 PacBio long-read RNA sequencing datasets from the ENCODE4 project, spanning 55 human cell types. This resource, generated by the Encyclopedia of DNA Elements consortium, captures transcriptomes across a remarkable range of biological contexts, from immune cells to neuronal lineages. After processing the data through their custom pipeline, which includes artifact filtering with SQANTI3 and annotation of open reading frames, the authors computed perplexity for every gene in every sample and asked whether the resulting diversity measurements were interpretable and reproducible.

The answer, according to the study, is yes. Perplexity proved to be a stable and meaningful quantity across genes, regulatory levels and tissues. Genes whose isoform repertoires are governed by well-characterized regulatory mechanisms showed diversity profiles consistent with known biology, while the metric distinguished cell types in ways that reflect their underlying splicing and transcription programs. Because the calculation uses all detected isoforms rather than a thresholded subset, repeated measurements of the same biological condition converge far more reliably than threshold-based counts, addressing one of the most persistent complaints about long-read transcriptomics: that different pipelines, and even slightly different parameters within a single pipeline, can yield noticeably different catalogs of isoforms.

The work also extends beyond simply counting transcripts. The authors analyzed their detected isoforms at the level of open reading frames, the stretches of RNA that are actually translated into protein, and their supplementary analyses include quadrant classifications of open reading frames and tissue-specific assignments. This reflects a growing recognition in the field that isoform diversity matters not merely as a cataloging exercise but because different isoforms can carry different protein-coding potential, potentially producing distinct protein products from the same gene. A diversity metric that faithfully captures the full isoform landscape therefore has direct implications for proteomics and for interpreting the functional consequences of genetic variation.

The implications reach into medicine as well. Alternative splicing is increasingly implicated in cancer, neurological disease and genetic disorders, and dysregulated isoform production is a hallmark of many tumors. Gloria Sheynkman, whose laboratory at the University of Virginia focuses on connecting transcriptomics to proteomics, and David Knowles, whose group at Columbia and the New York Genome Center develops computational methods for genome interpretation, have both founded or advised companies in this space, underscoring the translational interest in reliable isoform quantification. A reproducible, threshold-free measure of isoform diversity could improve the statistical power of studies that compare diseased and healthy tissue, where subtle shifts in splicing regimes may be drowned out by the noise introduced by arbitrary filtering.

There is also a satisfying intellectual symmetry in the approach. Ecologists counting species in a rainforest, linguists measuring the richness of a vocabulary and now genomicists quantifying the diversity of a transcriptome are all grappling with the same mathematical structure: a distribution over many categories, where rare categories are real but hard to handle. Shannon entropy and its exponential, perplexity, provide a common language for all of these problems. By importing that language into transcriptomics, Schertzer, Park and their colleagues have turned a long-standing methodological weakness into an opportunity, giving the field a way to embrace the full, messy richness of the human transcriptome rather than trimming it to fit a threshold. As long-read sequencing continues its march toward routine clinical and research use, metrics like perplexity may well become as standard a vocabulary for RNA biology as expression levels are today.

Subject of Research: An information-theoretic metric for quantifying isoform diversity in the human transcriptome

Article Title: Perplexity as a metric for isoform diversity in the human transcriptome

Article References: Schertzer, M. D., Park, S. H., Su, J., Reese, F., Sheynkman, G. M., & Knowles, D. A. (2026). Perplexity as a metric for isoform diversity in the human transcriptome. Genome Biology. https://doi.org/10.1186/s13059-026-04260-2

Image Credits: AI Generated

DOI: 10.1186/s13059-026-04260-2

Keywords: perplexity, isoform diversity, long-read RNA sequencing, Shannon entropy, Hill numbers, transcriptome, ENCODE4, PacBio, alternative splicing, SQANTI3, open reading frames, Genome Biology