Accurate assembly of transcripts through phase-preserving graph decomposition

Shao, Mingfu; Kingsford, Carl

doi:10.1038/nbt.4020

Cited by 189 publications

(224 citation statements)

References 23 publications

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“…In this work, as opposed to (author?) [5], we choose to not separately evaluate multi-exon transcripts and single-exon transcripts but rather maximize a combined AUC.…”

Section: Analyzing Parameter Behaviormentioning

confidence: 99%

“…Transcriptome assembly takes an RNA-Seq sample and a reference genome as input and reconstructs the set of transcripts that are present. Common tools for reference-based transcript assembly include Cufflinks [2], StringTie [3], TranscComb [4], and Scallop [5], Referencebased assemblers first align reads to the reference genome using a tool such as HISAT [6], STAR [7], TopHat [8], or SpliceMap [9]. Using the read splice locations (the positions where a read maps to non-neighboring locations on a genome), the assembler constructs the exons and splice-junctions of each transcript.…”

Section: Introductionmentioning

confidence: 99%

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Towards building an automated bioinformatician: more accurate transcript assembly via parameter advising

DeBlasio

Kim

Kingsford

2018

Preprint

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Motivation: The computational tools used for genomic analyses are becoming increasingly sophisticated and complex. While these applications provide more accurate results, a new problem is emerging in that these pieces of software have a large number of tunable parameters. The default parameter choices are designed to work well on average across all inputs, but the most interesting experiments are often not "average". Choosing the wrong parameter values for an application can lead to significant results being overlooked, or false results being reported. This problem is exacerbated when these applications are chained together in analysis pipelines where each step introduces errors due to parameter choices. Results: We take some first steps towards generating a truly automated genomic analysis pipeline by developing a method for automatically choosing input-specific parameter values for reference-based transcript assembly.We apply the parameter advising framework, first developed for multiple sequence alignment, to optimize parameter choices for the Scallop transcript assembler. In doing so, we provide the first method for finding advisor sets for applications with large numbers of tunable parameters. This procedure can be parallelized, meaning it does not add any additional wall time. By choosing parameter values for each input, the area under the curve is increased by 28.9% over using only the default parameter choices on 1595 RNA-Seq samples in the Sequence Read Archive. This approach is general, and when applied to StringTie it increases AUC by 13.1% on a set of 65 RNA-Seq experiments from ENCODE.

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“…In this work, as opposed to (author?) [5], we choose to not separately evaluate multi-exon transcripts and single-exon transcripts but rather maximize a combined AUC.…”

Section: Analyzing Parameter Behaviormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards building an automated bioinformatician: more accurate transcript assembly via parameter advising

DeBlasio

Kim

Kingsford

2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is complicated by the presence of paralogous genes and transcripts with many isoforms that largely overlap one another, and as a result this approach produces highly fragmented and error-prone transcriptomes. Reference-guided assemblers such as Cufflinks (Trapnell, Williams et al 2010), Bayesembler (Maretty, Sibbesen et al 2014), StringTie (Pertea, Pertea et al 2015), TransComb (Liu, Yu et al 2016), and Scallop (Shao and Kingsford 2017) take advantage of an existing genome to which the RNA-seq reads are first aligned using a spliced aligner such as HISAT (Kim, Langmead et al 2015) or STAR (Dobin, Davis et al 2013).…”

Section: Introductionmentioning

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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“…No published transcript assembler has been adapted and systematically tested on the challenges of long-read transcript assembly yet. Aiming to handle these challenges, we developed a longread transcript assembler called Scallop-LR, evolved from Scallop, an accurate short-read transcript assembler (Shao and Kingsford, 2017). Scallop-LR is designed for PacBio long reads.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Benefit Offered by Transcript Assembly on Single-Molecule Long Reads

Tung

Shao

Kingsford

2019

Preprint

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Third-generation sequencing technologies benefit transcriptome analysis by generating longer sequencing reads. However, not all single-molecule long reads represent full transcripts due to incomplete cDNA synthesis and the sequencing length limit of the platform. This drives a need for long read transcript assembly. We quantify the benefit that can be achieved by using a transcript assembler on long reads. Adding long-read-specific algorithms, we evolved Scallop to make Scallop-LR, a long-read transcript assembler, to handle the computational challenges arising from long read lengths and high error rates. Analyzing 26 SRA PacBio datasets using Scallop-LR, Iso-Seq Analysis, and StringTie, we quantified the amount by which assembly improved Iso-Seq results. Through combined evaluation methods, we found that Scallop-LR identifies 2100-4000 more (for 18 human datasets) or 1100-2200 more (for eight mouse datasets) known transcripts than Iso-Seq Analysis, which does not do assembly. Further, Scallop-LR finds 2.4-4.4 times more potentially novel isoforms than Iso-Seq Analysis for the human and mouse datasets. StringTie also identifies more transcripts than Iso-Seq Analysis. Adding long-read-specific optimizations in Scallop-LR increases the numbers of predicted known transcripts and potentially novel isoforms for the human transcriptome compared to several recent short-read assemblers (e.g. StringTie).Our findings indicate that transcript assembly by Scallop-LR can reveal a more complete human transcriptome.

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Accurate assembly of transcripts through phase-preserving graph decomposition

Cited by 189 publications

References 23 publications

Towards building an automated bioinformatician: more accurate transcript assembly via parameter advising

Towards building an automated bioinformatician: more accurate transcript assembly via parameter advising

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

Quantifying the Benefit Offered by Transcript Assembly on Single-Molecule Long Reads

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