Browsing by Author "Chang, Ting-Tsung"

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DeF-GPU: Efficient and effective deletions finding in hepatitis B viral genomic DNA using a GPU architecture
(2016-12-01)
Cheng, Chun-Pei
;
Lan, Kuo-Lun
;
Liu, Wen-Chun
;
Chang, Ting-Tsung
;
Tseng, Vincent S.
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/projectsidefgpui. (C) 2016 Elsevier Inc. All rights reserved.
Detecting exact breakpoints of deletions with diversity in hepatitis B viral genomic DNA from next-generation sequencing data
(2017-10-01)
Cheng, Ji-Hong
;
Liu, Wen-Chun
;
Chang, Ting-Tsung
;
Hsieh, Sun-Yuan
;
Tseng, Vincent S.
Many studies have suggested that deletions of Hepatitis B Viral (HBV) are associated with the development of progressive liver diseases, even ultimately resulting in hepatocellular carcinoma (HCC). Among the methods for detecting deletions from next-generation sequencing (NGS) data, few methods considered the characteristics of virus, such as high evolution rates and high divergence among the different HBV genomes. Sequencing high divergence HBV genome sequences using the NGS technology outputs millions of reads. Thus, detecting exact breakpoints of deletions from these big and complex data incurs very high computational cost. We proposed a novel analytical method named VirDelect (Virus Deletion Detect), which uses split read alignment base to detect exact breakpoint and diversity variable to consider high divergence in single-end reads data, such that the computational cost can be reduced without losing accuracy. We use four simulated reads datasets and two real pair-end reads datasets of HBV genome sequence to verify VirDelect accuracy by score functions. The experimental results show that VirDelect outperforms the state-of-the-art method Pindel in terms of accuracy score for all simulated datasets and VirDelect had only two base errors even in real datasets. VirDelect is also shown to deliver high accuracy in analyzing the single-end read data as well as pair-end data. VirDelect can serve as an effective and efficient bioinformatics tool for physiologists with high accuracy and efficient performance and applicable to further analysis with characteristics similar to HBV on genome length and high divergence. The software program of VirDelect can be downloaded at https://sourceforge.net/projects/virdelect/. (C) 2017 Published by Elsevier Inc.
Hepatocellular carcinoma-associated single-nucleotide variants and deletions identified by the use of genome-wide high-throughput analysis of hepatitis B virus
(2017-10-01)
Liu, Wen-Chun
;
Wu, I-Chin
;
Lee, Yen-Chien
;
Lin, Chih-Peng
;
Cheng, Ji-Hong
This study investigated hepatitis B virus (HBV) single-nucleotide variants (SNVs) and deletion mutations linked with hepatocellular carcinoma (HCC). Ninety-three HCC patients and 108 non-HCC patients were enrolled for HBV genome-wide next-generation sequencing (NGS) analysis. A systematic literature review and a meta-analysis were performed to validate NGS-defined HCC-associated SNVs and deletions. The experimental results identified 60 NGS-defined HCC-associated SNVs, including 41 novel SNVs, and their pathogenic frequencies. Each SNV was specific for either genotype B (n = 24) or genotype C (n = 34), except for nt53C, which was present in both genotypes. The pathogenic frequencies of these HCC-associated SNVs showed a distinct U-shaped distribution pattern. According to the meta-analysis and literature review, 167 HBV variants from 109 publications were categorized into four levels (A-D) of supporting evidence that they are associated with HCC. The proportion of NGS-defined HCC-associated SNVs among these HBV variants declined significantly from 75% of 12 HCC-associated variants by meta-analysis (Level A) to 0% of 10 HCC-unassociated variants by meta-analysis (Level D) (P < 0.0001). PreS deletions were significantly associated with HCC, in terms of deletion index, for both genotypes B (P = 0.030) and C (P = 0.049). For genotype C, preS deletions involving a specific fragment (nt2977-3013) were significantly associated with HCC (HCC versus non-HCC, 6/34 versus 0/32, P = 0.025). Meta-analysis of preS deletions showed significant association with HCC (summary odds ratio 3.0; 95% confidence interval 2.3-3.9). Transfection of Huh7 cells showed that all of the five novel NGS-defined HCC-associated SNVs in the small surface region influenced hepatocarcinogenesis pathways, including endoplasmic reticulum-stress and DNA repair systems, as shown by microarray, real-time polymerase chain reaction and western blot analysis. Their carcinogenic mechanisms are worthy of further research. Copyright (c) 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.