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LSCplus: a fast solution for improving long read accuracy by short read alignment.


ABSTRACT: The single molecule, real time (SMRT) sequencing technology of Pacific Biosciences enables the acquisition of transcripts from end to end due to its ability to produce extraordinarily long reads (>10 kb). This new method of transcriptome sequencing has been applied to several projects on humans and model organisms. However, the raw data from SMRT sequencing are of relatively low quality, with a random error rate of approximately 15 %, for which error correction using next-generation sequencing (NGS) short reads is typically necessary. Few tools have been designed that apply a hybrid sequencing approach that combines NGS and SMRT data, and the most popular existing tool for error correction, LSC, has computing resource requirements that are too intensive for most laboratory and research groups. These shortcomings severely limit the application of SMRT long reads for transcriptome analysis.Here, we report an improved tool (LSCplus) for error correction with the LSC program as a reference. LSCplus overcomes the disadvantage of LSC's time consumption and improves quality. Only 1/3-1/4 of the time and 1/20-1/25 of the error correction time is required using LSCplus compared with that required for using LSC.LSCplus is freely available at http://www.herbbol.org:8001/lscplus/ . Sample calculations are provided illustrating the precision and efficiency of this method regarding error correction and isoform detection.

SUBMITTER: Hu R 

PROVIDER: S-EPMC5103424 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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LSCplus: a fast solution for improving long read accuracy by short read alignment.

Hu Ruifeng R   Sun Guibo G   Sun Xiaobo X  

BMC bioinformatics 20161109 1


<h4>Background</h4>The single molecule, real time (SMRT) sequencing technology of Pacific Biosciences enables the acquisition of transcripts from end to end due to its ability to produce extraordinarily long reads (>10 kb). This new method of transcriptome sequencing has been applied to several projects on humans and model organisms. However, the raw data from SMRT sequencing are of relatively low quality, with a random error rate of approximately 15 %, for which error correction using next-gene  ...[more]

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