Demo Data from EDBT 2026 / reviewers in the wild / expert
Miranda P. G. Zalusky
dblp:440/7238
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 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 · 94% Medical and health informatics · 6% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › population genetics › population parameter estimation
allele frequency estimation |
1.0 | 1 | 2026 | needLR: long-read structural variant annotation with population-scale frequency estimation · Bioinform. 2026 |
Bioinformatics and computational biology
population genetics |
1.0 | 1 | 2026 | needLR: long-read structural variant annotation with population-scale frequency estimation · Bioinform. 2026 |
Bioinformatics and computational biology › genomics › structural variation
structural variant analysis |
1.0 | 1 | 2026 | needLR: long-read structural variant annotation with population-scale frequency estimation · Bioinform. 2026 |
Bioinformatics and computational biology › sequence analysis › sequence variation analysis
structural variant annotation |
1.0 | 1 | 2026 | needLR: long-read structural variant annotation with population-scale frequency estimation · Bioinform. 2026 |
Medical and health informatics
clinical genomics |
0.3 | 1 | 2026 | needLR: long-read structural variant annotation with population-scale frequency estimation · Bioinform. 2026 |
Bioinformatics and computational biology › clinical bioinformatics
pathogenic variant prioritization |
0.3 | 1 | 2026 | needLR: long-read structural variant annotation with population-scale frequency estimation · Bioinform. 2026 |
Methods — techniques the papers use, named apart from their topics
long-read sequencing · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | needLR: long-read structural variant annotation with population-scale frequency estimationabstractSUMMARY: We present needLR, a structural variant (SV) annotation tool that can be used for filtering and prioritization of candidate pathogenic SVs from long-read sequencing data using population allele frequencies, annotations for genomic context, and gene-phenotype associations. When using population data from 500 presumably healthy individuals to evaluate nine test cases with known pathogenic SVs, needLR assigned allele frequencies to over 97.5% of all detected SVs and reduced the average number of novel genic SVs to 121 per case while retaining all known pathogenic variants. AVAILABILITY AND IMPLEMENTATION: needLR is implemented in bash with dependencies including Truvari v4.2.2, BEDTools v2.31.1, and BCFtools v1.19. Source code, documentation, and pre-computed population allele frequency data are freely available at https://github.com/jgust1/needLR under an MIT license and archived on Zenodo at https://zenodo.org/records/19463479. Jonas A Gustafson, Jiadong Lin, Miranda P. G. Zalusky, Evan E. Eichler, Danny E. Miller |
Bioinform. | 3 |