2021
DOI: 10.1186/s12859-021-04198-1
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Comparative evaluation of full-length isoform quantification from RNA-Seq

Abstract: Background Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short. Results Here we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systemat… Show more

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Cited by 32 publications
(46 citation statements)
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References 43 publications
(119 reference statements)
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“…Standard parameters (-b 100) were employed for PE mapping. As recently reported in an exhaustive benchmark evaluation [ 44 ], Kallisto is effective for large datasets, such as the one reported herein. In addition, since this software is accurate and fast during the mapping and quantification procedure, it will be useful for keeping BEB up-to-date as more RNA-Seq datasets become available in the future.…”
Section: Methodsmentioning
confidence: 71%
See 1 more Smart Citation
“…Standard parameters (-b 100) were employed for PE mapping. As recently reported in an exhaustive benchmark evaluation [ 44 ], Kallisto is effective for large datasets, such as the one reported herein. In addition, since this software is accurate and fast during the mapping and quantification procedure, it will be useful for keeping BEB up-to-date as more RNA-Seq datasets become available in the future.…”
Section: Methodsmentioning
confidence: 71%
“…As more RNA-Seq datasets become available, this table (and associated metadata file) will be expanded in , as detailed herein and in the online information. We will rely on Kallisto software for accurate mapping and rapid quantification of RNA-Seq data, as described previously [ 44 ].…”
Section: Resultsmentioning
confidence: 99%
“…Salmon quantifies expression at the transcript level using an RNA-Seq pseudo-alignment approach. Our previous benchmarking study found that Salmon was a top transcript-level quantifier 20 , which is the motivation to evaluate its performance further.…”
Section: Resultsmentioning
confidence: 99%
“…More specifically, we considered several available RNA‐Seq data from wheat plants that were either infected with major fungal pathogens, artificially treated with chitin and flagellin 22, or stressed with cold temperatures (Table S2). Because of the high accuracy of expression estimates for gene duplicates and isoforms (Sarantopoulou et al, 2021; Soneson et al, 2015), Salmon (Patro et al, 2017) was used for estimating the expression of wheat coding sequences (IWGSC RefSeq v1.0). It was run in mapping‐based mode with standard parameters and the estimated number of mapped reads (NumReads) were used for differential expression analysis as previously described by Praz et al (2018).…”
Section: Methodsmentioning
confidence: 99%