Demo Data from EDBT 2026 / reviewers in the wild / expert
Alexander Kanapin
dblp:41/6265
· DBLP profile ↗
7ranked-venue papers
1as first author
2since 2021 · last 2024
0000-0001-9802-5297ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 7 · 1 first-author · 2 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
3 papers |
Bioinformatics and computational biology · 100% |
Topics — the 4 heaviest of 5, 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 |
Bioinformatics and computational biology
comparative genomics |
0.0 | 1 | 2002 | Interactive InterPro-based comparisons of proteins in whole genomes · Bioinform. 2002 |
Bioinformatics and computational biology › proteomics
proteome comparison |
0.0 | 1 | 2002 | Interactive InterPro-based comparisons of proteins in whole genomes · Bioinform. 2002 |
Methods — techniques the papers use, named apart from their topics
property-based testing · 0.7interpro analysis · 0.0regular expression · 0.0profile · 0.0hidden markov model · 0.0fingerprint · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | BSXplorer: analytical framework for exploratory analysis of BS-seq dataabstractBACKGROUND: Bisulfite sequencing detects and quantifies DNA methylation patterns, contributing to our understanding of gene expression regulation, genome stability maintenance, conservation of epigenetic mechanisms across divergent taxa, epigenetic inheritance and, eventually, phenotypic variation. Graphical representation of methylation data is crucial in exploring epigenetic regulation on a genome-wide scale in both plants and animals. This is especially relevant for non-model organisms with poorly annotated genomes and/or organisms where genome sequences are not yet assembled on chromosome level. Despite being a technology of choice to profile DNA methylation for many years now there are surprisingly few lightweight and robust standalone tools available for efficient graphical analysis of data in non-model systems. This significantly limits evolutionary studies and agrigenomics research. BSXplorer is a tool specifically developed to fill this gap and assist researchers in explorative data analysis and in visualising and interpreting bisulfite sequencing data more easily. RESULTS: BSXplorer provides in-depth graphical analysis of sequencing data encompassing (a) profiling of methylation levels in metagenes or in user-defined regions using line plots and heatmaps, generation of summary statistics charts, (b) enabling comparative analyses of methylation patterns across experimental samples, methylation contexts and species, and (c) identification of modules sharing similar methylation signatures at functional genomic elements. The tool processes methylation data quickly and offers API and CLI capabilities, along with the ability to create high-quality figures suitable for publication. CONCLUSIONS: BSXplorer facilitates efficient methylation data mining, contrasting and visualization, making it an easy-to-use package that is highly useful for epigenetic research. Konstantin Yuditskiy, Igor Bezdvornykh, Anastasiya Kazantseva, Alexander Kanapin, Anastassia Samsonova |
BMC Bioinform. | 4 |
| 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. | 3 |
| 2004 | Supervised Learning-Aided Optimization of Expert-Driven Functional Protein Sequence Annotation
Lev Soinov, Alexander Kanapin, Misha Kapushesky |
WABI | 2 |
| 2002 | Applications of InterPro in Protein Annotation and Genome AnalysisabstractThe applications of InterPro span a range of biologically important areas that includes automatic annotation of protein sequences and genome analysis. In automatic annotation of protein sequences InterPro has been utilised to provide reliable characterisation of sequences, identifying them as candidates for functional annotation. Rules based on the InterPro characterisation are stored and operated through a database called RuleBase. RuleBase is used as the main tool in the sequence database group at the EBI to apply automatic annotation to unknown sequences. The annotated sequences are stored and distributed in the TrEMBL protein sequence database. InterPro also provides a means to carry out statistical and comparative analyses of whole genomes. In the Proteome Analysis Database, InterPro analyses have been combined with other analyses based on CluSTr, the Gene Ontology (GO) and structural information on the proteins. Margaret Biswas, Joseph F. O'Rourke, Evelyn Camon, Gillian Fraser, Alexander Kanapin, Youla Karavidopoulou, Paul J. Kersey, Evgenia V. Kriventseva, Virginie Mittard, Nicola J. Mulder, Isabelle Phan, Florence Servant, Rolf Apweiler |
Briefings Bioinform. | 5 |
| 2002 | InterPro: An Integrated Documentation Resource for Protein Families, Domains and Functional SitesabstractThe exponential increase in the submission of nucleotide sequences to the nucleotide sequence database by genome sequencing centres has resulted in a need for rapid, automatic methods for classification of the resulting protein sequences. There are several signature and sequence cluster-based methods for protein classification, each resource having distinct areas of optimum application owing to the differences in the underlying analysis methods. In recognition of this, InterPro was developed as an integrated documentation resource for protein families, domains and functional sites, to rationalise the complementary efforts of the individual protein signature database projects. The member databases - PRINTS, PROSITE, Pfam, ProDom, SMART and TIGRFAMs - form the InterPro core. Related signatures from each member database are unified into single InterPro entries. Each InterPro entry includes a unique accession number, functional descriptions and literature references, and links are made back to the relevant member database(s). Release 4.0 of InterPro (November 2001) contains 4,691 entries, representing 3,532 families, 1,068 domains, 74 repeats and 15 sites of post-translational modification (PTMs) encoded by different regular expressions, profiles, fingerprints and hidden Markov models (HMMs). Each InterPro entry lists all the matches against SWISS-PROT and TrEMBL (2,141,621 InterPro hits from 586,124 SWISS-PROT and TrEMBL protein sequences). The database is freely accessible for text- and sequence-based searches. Nicola J. Mulder, Rolf Apweiler, Terri K. Attwood, Amos Bairoch, Alex Bateman, David Binns, Margaret Biswas, Paul Bradley, Peer Bork, Philipp Bucher, Richard R. Copley, Emmanuel Courcelle, Richard Durbin, Laurent Falquet, Wolfgang Fleischmann, Jérôme Gouzy, Sam Griffiths-Jones, Daniel H. Haft, Henning Hermjakob, Nicolas Hulo, Daniel Kahn, Alexander Kanapin, Maria Krestyaninova, Rodrigo Lopez, Ivica Letunic, Sue Orchard, Marco Pagni, David Peyruc, Chris P. Ponting, Florence Servant, Christian J. A. Sigrist |
Briefings Bioinform. | 22 |
| 2002 | Interactive InterPro-based comparisons of proteins in whole genomesabstractAbstract Motivation: The SWISS-PROT group at the EBI has developed the Proteome Analysis Database utilizing existing resources and providing comprehensive and integrated comparative analysis of the predicted protein coding sequencesof the complete genomes of bacteria, archaea and eukaryotes. The Proteome Analysis Database is accompanied by a program that has been designed to carry out interactive InterPro proteome comparisons for any one proteome against any other one or more of the proteomes in the database. Availability: http://www.ebi.ac.uk/proteome/comparisons.html Contact: [email protected]; [email protected] * To whom all correspondence should be addressed. Alexander Kanapin, Rolf Apweiler, Margaret Biswas, Wolfgang Fleischmann, Youla Karavidopoulou, Paul J. Kersey, Evgenia V. Kriventseva, Virginie Mittard, Nicola J. Mulder, Thomas M. Oinn, Isabelle Phan, Florence Servant, Evgeny M. Zdobnov |
Bioinform. | 1 |
| 2000 | InterPro-an integrated documentation resource for protein families, domains and functional sitesabstractMOTIVATION: InterPro is a new integrated documentation resource for protein families, domains and functional sites, developed initially as a means of rationalising the complementary efforts of the PROSITE, PRINTS, Pfam and ProDom database projects. RESULTS: Merged annotations from PRINTS, PROSITE and Pfam form the InterPro core. Each combined InterPro entry includes functional descriptions and literature references, and links are made back to the relevant parent database(s), allowing users to see at a glance whether a particular family or domain has associated patterns, profiles, fingerprints, etc. Merged and individual entries (i.e. those that have no counterpart in the companion resources) are assigned unique accession numbers. Release 1.2 of InterPro (June 2000) contains over 3000 entries, representing families, domains, repeats and sites of post-translational modification (PTMs) encoded by 6581 different regular expressions, profiles, fingerprints and Hidden Markov Models (HMMs). Each InterPro entry lists all the matches against SWISS-PROT and TrEMBL (more than 1000000 hits from 264333 different proteins out of 384572 in SWISS-PROT and TrEMBL). Rolf Apweiler, Terri K. Attwood, Amos Bairoch, Alex Bateman, Ewan Birney, Margaret Biswas, Philipp Bucher, Lorenzo Cerutti, Florence Corpet, Michael D. R. Croning, Richard Durbin, Laurent Falquet, Wolfgang Fleischmann, Jérôme Gouzy, Henning Hermjakob, Nicolas Hulo, Inge Jonassen, Daniel Kahn, Alexander Kanapin, Youla Karavidopoulou, Rodrigo Lopez, Beate Marx, Nicola J. Mulder, Thomas M. Oinn, Marco Pagni, Florence Servant, Christian J. A. Sigrist, Evgeny M. Zdobnov |
Bioinform. | 19 |