Demo Data from EDBT 2026 / reviewers in the wild / expert
Rasmus Amund Henriksen
dblp:370/4932
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
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
sequencing |
0.7 | 1 | 2023 | NGSNGS: next-generation simulator for next-generation sequencing data · Bioinform. 2023 |
Bioinformatics and computational biology
sequencing simulation |
0.7 | 1 | 2023 | NGSNGS: next-generation simulator for next-generation sequencing data · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
multithreading · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | NGSNGS: next-generation simulator for next-generation sequencing dataabstractSUMMARY: With the rapid expansion of the capabilities of the DNA sequencers throughout the different sequencing generations, the quantity of generated data has likewise increased. This evolution has also led to new bioinformatical methods, for which in silico data have become crucial when verifying the accuracy of a model or the robustness of a genomic analysis pipeline. Here, we present a multithreaded next-generation simulator for next-generation sequencing data (NGSNGS), which simulates reads faster than currently available methods and programs. NGSNGS can simulate reads with platform-specific characteristics based on nucleotide quality score profiles as well as including a post-mortem damage model which is relevant for simulating ancient DNA. The simulated sequences are sampled (with replacement) from a reference DNA genome, which can represent a haploid genome, polyploid assemblies or even population haplotypes and allows the user to simulate known variable sites directly. The program is implemented in a multithreading framework and is factors faster than currently available tools while extending their feature set and possible output formats. AVAILABILITY AND IMPLEMENTATION: The method and associated programs are released as open-source software, code and user manual are available at https://github.com/RAHenriksen/NGSNGS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Rasmus Amund Henriksen, Thorfinn Korneliussen |
Bioinform. | 1 |