Paean: A parallel transcriptome quantification tool combining gene expression and alternative splicing events using GPU

RNA-seq is one of the most widely used methods to probe gene expression and alternative splicing events (ASE) at the transcriptome scale. It often generates large amounts of complex sequencing data, which expands rapidly in large scale studies containing many samples. Quantifying short RNA reads dis...

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Bibliographic Details
Published in2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 676 - 679
Main Authors Li, Jiefu, Guan, Jiawen, Qian, Jiaqiang, Feng, Yanghan, Yao, Ruijie, Fan, Rui, Wang, Zefeng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
ASE quantification
Bioinformatics
Genomics
GPU
Junctions
MISO communication
parallel computing
RNA
RNA-seq
Splicing
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Summary:RNA-seq is one of the most widely used methods to probe gene expression and alternative splicing events (ASE) at the transcriptome scale. It often generates large amounts of complex sequencing data, which expands rapidly in large scale studies containing many samples. Quantifying short RNA reads distribution on reference genomes is a key step for most analyses of RNA-seq. This process requires a large amount of computational resource, including high-end computing hardware. With the accumulation of high throughput sequencing results from modern big-data biology, fast and efficient processing of RNA-seq data has become an urgent requirement. In this paper, we observe that transcriptome quantification is a highly repetitive task suitable for parallel computation. We report on a GPU based transcriptome quantification tool called Parallel Transcriptome Quantification Analyzer (Paean), which achieves over 20X acceleration in processing RNA-seq data compared to existing CPU-based methods when analyzing splicing changes, while producing comparable accuracy to conventional methods. To our knowledge, this is the first reported use of a hybrid CPU-GPU approach for transcriptome quantification analysis with RNA-seq data.
DOI:10.1109/BIBM.2018.8621118