BioinformaticsBioinformaticsPeer-reviewed journal article
TALC: Transcript-level Aware Long Read Correction
Lucile BroséusiD, Aubin Thomas, Andrew OldfieldiD, Dany Séverac, Emeric DuboisiD, William RitchieiD
Abstract
MotivationLong-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous "hybrid correction" algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data. ResultsWe have created a novel reference-free algorithm called TALC (Transcription Aware Long Read Correction) which models changes in RNA expression and isoform representation in a weighted De-Bruijn graph to correct long reads from transcriptome studies. We show that transcription aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology. Availability and ImplementationTALC is implemented in C++ and available at https://gitlab.igh.cnrs.fr/lbroseus/TALC. Contactwilliam.ritchie@igh.cnrs.fr
- assay: long-read sequencing
- assay: bulk RNA-seq
- research focus: variant calling
- research focus: dimensionality reduction
- approach: statistical model
- Citations
- 20
- FWCI
- 1.05
- Field percentile
- top 26%
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