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Nikolay Cherkasov

dblp:425/2505 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics
genomic data analysis
0.712023
Pygenomics: manipulating genomic intervals and data files in Python · Bioinform. 2023
Bioinformatics and computational biology › genomics › genomic interval analysis
genomic interval operations
0.712023
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
YearPublicationVenuePosition
2023 Pygenomics: manipulating genomic intervals and data files in Python
abstract
SUMMARY: 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