Demo Data from EDBT 2026 / reviewers in the wild / expert
Xuanlin Huang
dblp:197/8301
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genomics › structural variation
structural variation detection |
0.3 | 1 | 2017 | Seeksv: an accurate tool for somatic structural variation and virus integration detection · Bioinform. 2017 |
Bioinformatics and computational biology › genomics › viral genomics
viral integration detection |
0.3 | 1 | 2017 | Seeksv: an accurate tool for somatic structural variation and virus integration detection · Bioinform. 2017 |
Methods — techniques the papers use, named apart from their topics
split reads · 0.3read depth · 0.3discordant paired-end read · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Seeksv: an accurate tool for somatic structural variation and virus integration detectionabstractMOTIVATION: Many forms of variations exist in the human genome including single nucleotide polymorphism, small insert/deletion (DEL) (indel) and structural variation (SV). Somatically acquired SV may regulate the expression of tumor-related genes and result in cell proliferation and uncontrolled growth, eventually inducing tumor formation. Virus integration with host genome sequence is a type of SV that causes the related gene instability and normal cells to transform into tumor cells. Cancer SVs and viral integration sites must be discovered in a genome-wide scale for clarifying the mechanism of tumor occurrence and development. RESULTS: In this paper, we propose a new tool called seeksv to detect somatic SVs and viral integration events. Seeksv simultaneously uses split read signal, discordant paired-end read signal, read depth signal and the fragment with two ends unmapped. Seeksv can detect DEL, insertion, inversion and inter-chromosome transfer at single-nucleotide resolution. Different types of sequencing data, such as single-end sequencing data or paired-end sequencing data can accommodate to detect SV. Seeksv develops a rescue model for SV with breakpoints located in sequence homology regions. Results on simulated and real data from the 1000 Genomes Project and esophageal squamous cell carcinoma samples show that seeksv has higher efficiency and precision compared with other similar software in detecting SVs. For the discovery of hepatitis B virus integration sites from probe capture data, the verified experiments show that more than 90% viral integration sequences detected by seeksv are true. AVAILABILITY AND IMPLEMENTATION: seeksv is implemented in C ++ and can be downloaded from https://github.com/qkl871118/seeksv CONTACT: : [email protected] information: Supplementary data are available at Bioinformatics online. Kunlong Qiu, Bo Liao 0002, Wen Zhu, Xuanlin Huang, Xiangtao Chen, Keqin Li 0001 |
Bioinform. | 5 |