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Oxford Nanopore (ONT) is a leading long-read technology which has been revolutionizing transcriptome analysis through its capacity to sequence the majority of transcripts from end-to-end. This has greatly increased our ability to study the diversity of transcription mechanisms such as transcription initiation, termination, and alternative splicing. However, ONT still suffers from high error rates which have thus far limited its scope to reference-based analyses. When a reference is not available or is not a viable option due to reference-bias, error correction is a crucial step towards the reconstruction of the sequenced transcripts and downstream sequence analysis of transcripts. In this paper, we present a novel computational method to error correct ONT cDNA sequencing data, called isONcorrect. IsONcorrect is able to jointly use all isoforms from a gene during error correction, thereby allowing it to correct reads at low sequencing depths. We are able to obtain a median accuracy of 98.9–99.6%, demonstrating the feasibility of applying cost-effective cDNA full transcript length sequencing for reference-free transcriptome analysis.

A significant portion of genes in vertebrate genomes belongs to multigene families, with each family containing several gene copies whose presence/absence, as well as isoform structure, can be highly variable across individuals. Existing de novo techniques for assaying the sequences of such highly-similar gene families fall short of reconstructing end-to-end transcripts with nucleotide-level precision or assigning alternatively spliced transcripts to their respective gene copies. We present IsoCon, a high-precision method using long PacBio Iso-Seq reads to tackle this challenge. We apply IsoCon to nine Y chromosome ampliconic gene families and show that it outperforms existing methods on both experimental and simulated data. IsoCon has allowed us to detect an unprecedented number of novel isoforms and has opened the door for unraveling the structure of many multigene families and gaining a deeper understanding of genome evolution and human diseases.