MOTIVATION: Advances in high-throughput sequencing have illuminated the complexity of transcriptome landscape in eukaryotes. An inherent part of this complexity is the presence of multiple isoforms generated by the alternative splicing and the use of alternative transcription start and polyadenylation sites. However, currently available tools have limited capacity to infer full-length isoforms. RESULTS: We developed a new pipeline, FLIC (full-length isoform constructor). FLIC is based on the long-read transcriptome data and integrates several key features: (1) utilizing biological replicate concordance to filter out noise and artifacts; (2) employing peak calling to precisely identify transcription start and polyadenylation sites; (3) enabling robust isoform reconstruction with minimal reliance on existing annotations. We evaluated FLIC using a dedicated set of real and simulated data of Arabidopsis thaliana cDNA sequencing. Results demonstrate that FLIC accurately reconstructs known and novel isoforms, outperforming existing tools, especially in the absence of reference annotations. A direct comparison with CAGE, currently regarded as the gold standard for transcription start site identification, shows that FLIC is equally accurate, while being much less time-consuming. Thus, FLIC provides a valuable tool for comprehensive transcript characterization, particularly for non-model organisms or when dealing with incomplete or inaccurate annotations. AVAILABILITY AND IMPLEMENTATION: FLIC is available at https://github.com/albidgy/FLIC.

Interspecific gene comparisons are the keystones for many areas of biological research and are especially important for the translation of knowledge from model organisms to economically important species. Currently they are hampered by the low resolution of methods based on sequence analysis and by the complex evolutionary history of eukaryotic genes. This is especially critical for plants, whose genomes are shaped by multiple whole genome duplications and subsequent gene loss. This requires the development of new methods for comparing the functions of genes in different species. Here, we report ISEEML (Interspecific Similarity of Expression Evaluated using Machine Learning)-a novel machine learning-based algorithm for interspecific gene classification. In contrast to previous studies focused on sequence similarity, our algorithm focuses on functional similarity inferred from the comparison of gene expression profiles. We propose novel metrics for expression pattern similarity-expression score (ES)-that is suitable for species with differing morphologies. As a proof of concept, we compare detailed transcriptome maps of Arabidopsis thaliana, the model species, Zea mays (maize) and Fagopyrum esculentum (common buckwheat), which are species that represent distant clades within flowering plants. The classifier resulted in an AUC of 0.91; under the ES threshold of 0.5, the specificity was 94%, and sensitivity was 72%.