Dataset Information

DisV-HPV16, versatile and powerful software to detect HPV in RNA sequencing data.

ABSTRACT: BACKGROUND:The increasing availability of high-throughput sequencing data provides researchers with unprecedented opportunities to investigate viral genetic elements in host genomes that contribute to virus-linked cancers. Almost all of the available computational tools for secondary analysis of sequencing data detect viral infection or genome integration events. However, viral oncogenes expression is likely of importance in carcinoma. We therefore developed a new software, DisV-HPV16, for the evaluation of HPV16 oncogenes expression. RESULTS:HPV16 virus and viral oncogenes expression was detected more rapidly using DisV-HPV16 compared to other software. DisV-HPV16 was proved highly convenient for detecting candidate virus after modification of the reference file. The accuracy of DisV-HPV16 was empirically confirmed in laboratory experiments. DisV-HPV16 exhibited greater reliability than other software. CONCLUSIONS:DisV-HPV16 is a new, dependable software to detect virus and viral oncogenes expression through analysis of RNA sequencing data. Use of DisV-HPV16 can yield deeper, more comprehensive insights into virus infection status and viral and host cell gene expression.

SUBMITTER: Yan B

PROVIDER: S-EPMC6542142 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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DisV-HPV16, versatile and powerful software to detect HPV in RNA sequencing data.

Yan Bingqing B Liu Xinyi X Zhang Siwei S Yu Siyang S Tong Fangjia F Xie Huanhuan H Song Lianhao L Zhang Yan Y Wei Lanlan L

BMC infectious diseases 20190529 1

<h4>Background</h4>The increasing availability of high-throughput sequencing data provides researchers with unprecedented opportunities to investigate viral genetic elements in host genomes that contribute to virus-linked cancers. Almost all of the available computational tools for secondary analysis of sequencing data detect viral infection or genome integration events. However, viral oncogenes expression is likely of importance in carcinoma. We therefore developed a new software, DisV-HPV16, f ...[more]

PMID: 31142265

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Project description:BackgroundCircular RNA (circRNA), a class of RNA molecule with a loop structure, has recently attracted researchers due to its diverse biological functions and potential biomarkers of human diseases. Most of the current circRNA detection methods from RNA-sequencing (RNA-Seq) data utilize the mapping information of paired-end (PE) reads to eliminate false positives. However, much of the practical RNA-Seq data such as cross-linking immunoprecipitation sequencing (CLIP-Seq) data usually contain single-end (SE) reads. It is not clear how well these tools perform on SE RNA-Seq data.ResultsIn this study, we present a systematic evaluation of six advanced RNA-based methods and two CLIP-Seq based methods for detecting circRNAs from SE RNA-Seq data. The performances of the methods are rigorously assessed based on precision, sensitivity, F1 score, and true discovery rate. We investigate the impacts of read length, false positive ratio, sequencing depth and PE mapping information on the performances of the methods using simulated SE RNA-Seq simulated datasets. The real datasets used in this study consist of four experimental RNA-Seq datasets with ≥100bp read length and 124 CLIP-Seq samples from 45 studies that contain mostly short-read (≤50bp) RNA-Seq data. The simulation study shows that the sensitivities of most of the methods can be improved by increasing either read length or sequencing depth, and that the levels of false positive rates significantly affect the precision of all methods. Furthermore, the PE mapping information can improve the method's precision but can not always guarantee the increase of F1 score. Overall, no method is dominant for all SE RNA-Seq data. The RNA-based methods perform better for the long-read datasets but are worse for the short-read datasets. In contrast, the CLIP-Seq based methods outperform the RNA-Seq based methods for all the short-read samples. Combining the results of these methods can significantly improve precision in the CLIP-Seq data.ConclusionsThe results provide a systematic evaluation of circRNA detection methods on SE RNA-Seq data that would facilitate researchers' strategies in circRNA analysis.

Dataset Information

DisV-HPV16, versatile and powerful software to detect HPV in RNA sequencing data.

Publications

DisV-HPV16, versatile and powerful software to detect HPV in RNA sequencing data.

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