Recent advances in long-read sequencing solve inaccuracies in alternative transcript identification of full-length transcripts in short-read RNA-Seq data, which encourages the development of methods for isoform-centered functional analysis. Here, we present tappAS, the first framework to enable a comprehensive Functional Iso-Transcriptomics (FIT) analysis, which is effective at revealing the functional impact of context-specific post-transcriptional regulation. tappAS uses isoform-resolved annotation of coding and non-coding functional domains, motifs, and sites, in combination with novel analysis methods to interrogate different aspects of the functional readout of transcript variants and isoform regulation. tappAS software and documentation are available at https://app.tappas.org.

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in well-annotated mammalian species. The advances in sequencing technology have created a need for studies and tools that can characterize these novel variants. Here, we present SQANTI, an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline using 47 unique descriptors. We apply SQANTI to a neuronal mouse transcriptome using Pacific Biosciences (PacBio) long reads and illustrate how the tool is effective in characterizing and describing the composition of the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, resulting more frequently in novel ORFs than novel UTRs, and are enriched in both general metabolic and neural-specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read-based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases, we find that alternative isoforms are elusive to proteogenomics detection. SQANTI allows the user to maximize the analytical outcome of long-read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes.

Corrigendum: SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification Manuel Tardaguila, Lorena de la Fuente, CristinaMarti, Cécile Pereira, Francisco Jose Pardo-Palacios, Hector del Risco, Marc Ferrell, Maravillas Mellado, Marissa Macchietto, Kenneth Verheggen, Mariola Edelmann, Iakes Ezkurdia, Jesus Vazquez, Michael Tress, Ali Mortazavi, Lennart Martens, Susana Rodriguez-Navarro, Victoria Moreno-Manzano, and Ana Conesa

Based on the claimed role transcript variants in conferring functional meaning and the lack of methods to study the functional implications of alternative splicing (AS) and alternative polyadenylation (APA), we have developed a new methodology called FAIR. This methodology will let to address the functional profiling of transcript and protein isoforms at a genome-wide level by using long-reads technologies. Moreover, we have implemented it in a software called Transcript2GO. Therefore, using PacBio and Illumina data, FAIR can generate functional hypothesis about the role of alternative isoforms in our system. First, FAIR allows the functional annotation of each PacBio-resolved isoform which involves the ORF prediction and the annotation of several functional layers: miRNA binding sites, PFAM domains, post-translational modifications, UTR motifs, NMD prediction, repetitive elements, etc. Finally, it applies different statistical methods which combine both expression data and functional annotation over each PacBio-resolved isoform. Among the several included statistical methods, we can highlight the Feature Differential Splicing which is able to point out functional elements affected by AS/APA. Using our rich annotation pipeline over a neural differentiation system, we found that nearly all genes expressing several isoforms have them annotated with at least one differential functional label, suggesting that functional profiling at isoform resolution is meaningful. We identified several functions enriched in genes regulated by differential splicing, as well as specific features as miRNAs regulated by AS/APA across conditions. Other functional insights of the relationship between function and differential splicing are easily revealed by the tools implemented in Transcript2GO.