2018
DOI: 10.1186/s12859-018-2174-6
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STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow

Abstract: BackgroundDe novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. Given the high number of sequences obtained from NGS approaches, a critical step in any analysis workflow is the assembly of reads to reconstruct transcripts thus reducing the complexity of the analysis. Despite many available tools show a good sensitivity, there is a high percentage of false pos… Show more

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Cited by 3 publications
(1 citation statement)
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“…The assembly of de novo transcriptomes, the puzzling together of short read RNA-Seq data to attempt to reconstruct the RNA present in a tissue or sample without the aid of a reference genome sequence, is a task with many approaches. A plethora of transcriptome assemblers are available (eg: Oases [1], TransABySS [2], IDBA-Tran [3], Trinity [4], STAble [5]), and decisions regarding the assembler algorithm, the impact of k-mer selection, and the use of read normalization can lead to different assemblies based on the same reads. Including methods that evaluate the quality of the assembly [6][7][8], or remove chimeras [9] and redundancy [10,11] adds extra variables that further complicates the process.…”
Section: Introductionmentioning
confidence: 99%
“…The assembly of de novo transcriptomes, the puzzling together of short read RNA-Seq data to attempt to reconstruct the RNA present in a tissue or sample without the aid of a reference genome sequence, is a task with many approaches. A plethora of transcriptome assemblers are available (eg: Oases [1], TransABySS [2], IDBA-Tran [3], Trinity [4], STAble [5]), and decisions regarding the assembler algorithm, the impact of k-mer selection, and the use of read normalization can lead to different assemblies based on the same reads. Including methods that evaluate the quality of the assembly [6][7][8], or remove chimeras [9] and redundancy [10,11] adds extra variables that further complicates the process.…”
Section: Introductionmentioning
confidence: 99%