title: DeF-GPU: Efficient and effective deletions finding in hepatitis B viral genomic DNA using a GPU architecture.
abstract: Hepatitis B viral (HBV) infection is strongly associated with an increased risk of liver diseases like cirrhosis or hepatocellular carcinoma (HCC). Many lines of evidence suggest that deletions occurring in HBV genomic DNA are highly associated with the activity of HBV via the interplay between aberrant viral proteins release and human immune system. Deletions finding on the HBV whole genome sequences is thus a very important issue though there exist underlying the challenges in mining such big and complex biological data. Although some next generation sequencing (NGS) tools are recently designed for identifying structural variations such as insertions or deletions, their validity is generally committed to human sequences study. This design may not be suitable for viruses due to different species. We propose a graphics processing unit (GPU)-based data mining method called DeF-GPU to efficiently and precisely identify HBV deletions from large NGS data, which generally contain millions of reads. To fit the single instruction multiple data instructions, sequencing reads are referred to as multiple data and the deletion finding procedure is referred to as a single instruction. We use Compute Unified Device Architecture (CUDA) to parallelize the procedures, and further validate DeF-GPU on 5 synthetic and 1 real datasets. Our results suggest that DeF-GPU outperforms the existing commonly-used method Pindel and is able to exactly identify the deletions of our ground truth in few seconds. The source code and other related materials are available at https://sourceforge.net/projects/defgpu/.
authors.affiliation: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan.
authors.forename: Chun-Pei
authors.initials: CP
authors.lastname: Cheng
authors.affiliation: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan.
authors.forename: Kuo-Lun
authors.initials: KL
authors.lastname: Lan
authors.affiliation: Department of Internal Medicine, National Cheng Kung University Medical College and Hospital, Tainan 701, Taiwan.
authors.forename: Wen-Chun
authors.initials: WC
authors.lastname: Liu
authors.affiliation: Department of Internal Medicine, National Cheng Kung University Medical College and Hospital, Tainan 701, Taiwan.
authors.forename: Ting-Tsung
authors.initials: TT
authors.lastname: Chang
authors.affiliation: Department of Computer Science, National Chiao Tung University, Hsinchu 308, Taiwan. Electronic address: vtseng@cs.nctu.edu.tw.
authors.forename: Vincent S
authors.initials: VS
authors.lastname: Tseng
mesh_terms: D006528:Carcinoma, Hepatocellular / Q000235:genetics / Q000821:virology; D019295:Computational Biology / Q000379:methods*; D004279:DNA, Viral / Q000235:genetics; D016679:Genome, Viral / Q000235:genetics*; D006509:Hepatitis B / Q000235:genetics* / Q000821:virology; D006515:Hepatitis B virus / Q000235:genetics* / Q000472:pathogenicity; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D008113:Liver Neoplasms / Q000235:genetics / Q000821:virology; D017384:Sequence Deletion / Q000235:genetics; D012984:Software
chemical_list: D004279:DNA, Viral
keywords: Big data mining; Deletion finding; Genome; Graphics processing unit; Hepatitis B virus; Next-generation sequencing