TranscriptClean: variant-aware correction of indels, mismatches and splice junctions in long-read transcripts

Wyman, Dana; Mortazavi, A

doi:10.1093/bioinformatics/bty483

Cited by 53 publications

(53 citation statements)

References 11 publications

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“…d) TALON can also be used to compare genes and transcripts across different samples such as mouse hippocampus and cortex. aligner such as Minimap2, reference-based error correction is performed using TranscriptClean to remove microindels, mismatches, and noncanonical splice junctions in a variant-aware manner as previously described 25 . Noncanonical splice junctions are permitted in the final output only if they are supported by the splice annotation.…”

Section: Tracking Transcript Novelty and Quantification Using Talonmentioning

confidence: 99%

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A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification

Wyman

Balderrama-Gutierrez

Reese

et al. 2019

Preprint

Self Cite

134

147

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Alternative splicing is widely acknowledged to be a crucial regulator of gene expression and is a key contributor to both normal developmental processes and disease states. While cost-effective and accurate for quantification, short-read RNA-seq lacks the ability to resolve full-length transcript isoforms despite increasingly sophisticated computational methods. Long-read sequencing platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) bypass the transcript reconstruction challenges of short reads. Here we introduce TALON, the ENCODE4 pipeline for platform-independent analysis of long-read transcriptomes. We apply TALON to the GM12878 cell line and show that while both PacBio and ONT technologies perform well at full-transcript discovery and quantification, each displayed distinct technical artifacts. We further apply TALON to mouse hippocampus and cortex transcriptomes and find that 422 genes found in these regions have more reads associated with novel isoforms than with annotated ones. We demonstrate that TALON is a capable of tracking both known and novel transcript models as well as their expression levels across datasets for both simple studies and in larger projects. These properties will enable TALON users to move beyond the limitations of short-read data to perform isoform discovery and quantification in a uniform manner on existing and future long-read platforms.

show abstract

Section: Tracking Transcript Novelty and Quantification Using Talonmentioning

confidence: 99%

“…The exact error rate depands largely on the number of passes that a molecule receives. Computational methods have also been developed to correct errors in long reads, including hybrid approaches that incorporate short reads, and other methods that make use of reference annotations [22][23][24][25] .…”

Section: Introductionmentioning

confidence: 99%

A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification

Wyman

Balderrama-Gutierrez

Reese

et al. 2019

Preprint

Self Cite

134

147

View full text Add to dashboard Cite

show abstract

“…This has the potential to greatly improve the sensitivity of analyses using long-read RNA-seq data, which in the past has relied primarily on reads that span transcripts end-to-end. The built-in consensus calling in StringTie2 should also lessen the need for a separate error correction step from tools such as TranscriptClean (Wyman and Mortazavi 2019). In addition to its fast runtime and small memory footprint, StringTie2 requires no dependencies and can be easily run as a single command, unlike tools such as FLAIR which consist of a series of scripts that can each fail independently.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome assembly from long-read RNA-seq alignments with StringTie2

Kovaka

Zimin

Pertea

et al. 2019

Preprint

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RNA sequencing using the latest single-molecule sequencing instruments produces reads that are thousands of nucleotides long. The ability to assemble these long reads can greatly improve the sensitivity of long-read analyses. Here we present StringTie2, a reference-guided transcriptome assembler that works with both short and long reads. StringTie2 includes new computational methods to handle the high error rate of long-read sequencing technology, which previous assemblers could not tolerate. It also offers the ability to work with full-length superreads assembled from short reads, which further improves the quality of assemblies. On 33 short-read datasets from humans and two plant species, StringTie2 is 47.3% more precise and 3.9% more sensitive than Scallop. On multiple long read datasets, StringTie2 on average correctly assembles 8.3 and 2.6 times as many transcripts as FLAIR and Traphlor, respectively, with substantially higher precision. StringTie2 is also faster and has a smaller memory footprint than all comparable tools.

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“…We used TranscriptClean 26 genes labeled at permissive threshold with more than 2 TPM expression of labeled reads, in the TUC-seq samples compared to 35 genes out of 10,584 genes detected in the controls (FDR = 0.33%). This number drops to 8,169 in the conservative category of labeled reads in the TUC-seq samples (Fig.…”

Section: Robust Detection Of Recently Synthesized Genes By Long-tuc-seqmentioning

confidence: 99%

Long-TUC-seq is a robust method for quantification of metabolically labeled full-length isoforms

Rahmanian

Balderrama-Gutierrez

Wyman

et al. 2020

Preprint

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The steady state expression of each gene is the result of a dynamic transcription and degradation of that gene. While regular RNA-seq methods only measure steady state expression levels, RNA-seq of metabolically labeled RNA identifies transcripts that were transcribed during the window of metabolic labeling. Whereas short-read RNA sequencing can identify metabolically labeled RNA at the gene level, long-read sequencing provides much better resolution of isoform-level transcription. Here we combine thiouridine-to-cytosine conversion (TUC) with PacBio long-read sequencing to study the dynamics of mRNA transcription in the GM12878 cell line. We show that using long-TUC-seq, we can detect metabolically labeled mRNA of distinct isoforms more reliably than using short reads. Long-TUC-seq holds the promise of capturing isoform dynamics robustly and without the need for enrichment.

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TranscriptClean: variant-aware correction of indels, mismatches and splice junctions in long-read transcripts

Abstract: None.

Cited by 53 publications

References 11 publications

A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification

A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification

Transcriptome assembly from long-read RNA-seq alignments with StringTie2

Long-TUC-seq is a robust method for quantification of metabolically labeled full-length isoforms

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