Demo Data from VLDB 2026 / reviewers in the wild / expert
Nikolay Cherkasov
dblp:425/2505
· DBLP profile ↗
1ranked-venue papers
0as 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 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 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genomics
genomic data analysis |
0.7 | 1 | 2023 | Pygenomics: manipulating genomic intervals and data files in Python · Bioinform. 2023 |
Bioinformatics and computational biology › genomics › genomic interval analysis
genomic interval operations |
0.7 | 1 | 2023 | Pygenomics: manipulating genomic intervals and data files in Python · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
property-based testing · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Pygenomics: manipulating genomic intervals and data files in PythonabstractSUMMARY: We present pygenomics, a Python package for working with genomic intervals and bioinformatic data files. The package implements interval operations, provides both API and CLI, and supports reading and writing data in widely used bioinformatic formats, including BAM, BED, GFF3, and VCF. The source code of pygenomics is provided with in-source documentation and type annotations and adheres to the functional programming paradigm. These features facilitate seamless integration of pygenomics routines into scripts and pipelines. The package is implemented in pure Python using its standard library only and contains the property-based testing framework. Comparison of pygenomics with other Python bioinformatic packages with relation to features and performance is presented. The performance comparison covers operations with genomic intervals, read alignments, and genomic variants and demonstrates that pygenomics is suitable for computationally effective analysis. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://gitlab.com/gtamazian/pygenomics. Gaik Tamazian, Nikolay Cherkasov, Alexander Kanapin, Anastassia Samsonova |
Bioinform. | 2 |