Publications
Click a title for authors, abstract and links. † joint first authors. Full list on Google Scholar.
First-author papers5
2026Flashback: a reversible bilateral run-peeling decomposition of stringsarXivPreprint
We introduce Flashback, a reversible string decomposition that repeatedly peels the maximal leading and trailing character runs from a sentinel-wrapped input, recording each pair as one bilateral token. Decomposition and reconstruction both run in linear time and space. The central result is a run-pairing theorem: Flashback pairs the first run of the string with the last, the second with the second-to-last, and so on, giving an exact token count that matches a lower bound for any admissible bilateral run-peeling scheme. Structural properties follow as corollaries, including a two-symbol irreducible kernel, a characterization of palindromes, a finite-state description of the image, and edit locality.
Cite
@misc{konstantinovsky2026flashback,
title = {{Flashback: a reversible bilateral run-peeling decomposition of strings}},
author = {Thomas Konstantinovsky and Gur Yaari},
year = {2026},
eprint = {2604.26190},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2604.26190}
}
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2025Enhancing sequence alignment of adaptive immune receptors through multi-task deep learningNucleic Acids Research
Sequence alignment of immunoglobulin (Ig) sequences is central to the computational analysis of adaptive immune receptor repertoire sequencing (AIRR-seq) data, impacting adaptive immunity research and antibody engineering. Traditional Ig sequence aligners often struggle with the complexities of V(D)J recombination and somatic hypermutation (SHM), resulting in suboptimal allele assignment accuracy and sequence segmentation. We introduce AlignAIR, a deep learning-based aligner that leverages simulation and a multi-task learning framework. AlignAIR improves allele assignment accuracy, productivity assessment, sequence segmentation and speed, and its latent space captures SHM characteristics. It integrates with existing AIRR-seq pipelines and ships with a web interface and a container image for local processing of millions of sequences.
Cite
@article{konstantinovsky2025enhancing,
title = {{Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning}},
author = {Thomas Konstantinovsky and Ayelet Peres and Ran Eisenberg and Pazit Polak and Ofir Lindenbaum and Gur Yaari},
journal = {Nucleic Acids Research},
volume = {53},
number = {13},
pages = {gkaf651},
year = {2025},
doi = {10.1093/nar/gkaf651},
url = {https://doi.org/10.1093/nar/gkaf651}
}
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2024An unbiased comparison of immunoglobulin sequence alignersBriefings in Bioinformatics
Reliable analysis of AIRR-seq data depends on accurate alignment of rearranged immunoglobulin (Ig) sequences, yet there is no unified benchmarking standard that represents the complexities of real data. We introduce GenAIRR, a modular simulation framework that generates Ig sequences alongside their ground truth, realistically simulating V(D)J recombination, somatic hypermutation and a range of sequence corruptions. Using GenAIRR we assess prominent Ig sequence aligners across a range of metrics and reveal distinct performance characteristics for each. The datasets and evaluation criteria provide a basis for unbiased benchmarking of immunogenetics tools.
Cite
@article{konstantinovsky2024unbiased,
title = {{An unbiased comparison of immunoglobulin sequence aligners}},
author = {Thomas Konstantinovsky and Ayelet Peres and Pazit Polak and Gur Yaari},
journal = {Briefings in Bioinformatics},
volume = {25},
number = {6},
pages = {bbae556},
year = {2024},
doi = {10.1093/bib/bbae556},
url = {https://doi.org/10.1093/bib/bbae556}
}
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2023A novel approach to T-cell receptor beta chain (TCRB) repertoire encoding using lossless string compressionBioinformatics
T-cell receptor beta chain (TCRB) repertoires are crucial for understanding immune responses, but their diversity makes them hard to represent and analyze. We introduce an encoding of TCRB repertoires based on the Lempel-Ziv 76 algorithm that yields a graph-like model of a repertoire. The representation supports generation probability inference, feature-vector derivation, sequence generation, a new diversity estimate and a new sequence centrality measure, and requires no alignment or reference genotype. Applied to four large public TCRB datasets, it demonstrates a scalable approach for large sequencing data. Implemented in the LZGraphs Python package.
Cite
@article{konstantinovsky2023novel,
title = {{A novel approach to T-cell receptor beta chain (TCRB) repertoire encoding using lossless string compression}},
author = {Thomas Konstantinovsky and Gur Yaari},
journal = {Bioinformatics},
volume = {39},
number = {7},
pages = {btad426},
year = {2023},
doi = {10.1093/bioinformatics/btad426},
url = {https://doi.org/10.1093/bioinformatics/btad426}
}
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2022Deconvolution of the epigenetic age discloses distinct inter-personal variability in epigenetic aging patternsEpigenetics & Chromatin
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@article{shahal2022deconvolution,
title = {{Deconvolution of the epigenetic age discloses distinct inter-personal variability in epigenetic aging patterns}},
author = {Tamar Shahal and Elad Segev and Thomas Konstantinovsky and Yonit Marcus and Gabi Shefer and Metsada Pasmanik-Chor and Assaf Buch and Yuval Ebenstein and Paul Zimmet and Naftali Stern},
journal = {Epigenetics \& Chromatin},
volume = {15},
number = {1},
year = {2022},
doi = {10.1186/s13072-022-00441-y},
url = {https://doi.org/10.1186/s13072-022-00441-y}
}
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Co-authored papers2
2026The current landscape of adaptive immune receptor genomic and repertoire data: OGRDB and VDJbaseNucleic Acids Research
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@article{lees2026current,
title = {{The current landscape of adaptive immune receptor genomic and repertoire data: OGRDB and VDJbase}},
author = {William D. Lees and Ayelet Peres and Vered Klein and Naama Amos and Uddalok Jana and Eric Engelbrecht and Zachary Vanwinkle and Yaniv Malach and Thomas Konstantinovsky and Pazit Polak and Corey T. Watson and Gur Yaari},
journal = {Nucleic Acids Research},
volume = {54},
number = {D1},
pages = {D932-D937},
year = {2026},
doi = {10.1093/nar/gkaf1094},
url = {https://doi.org/10.1093/nar/gkaf1094}
}
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2022Epigenetic aging waves: artificial intelligence detects clustering of switch points in DNA methylation rate in defined sex-dependent age periodsbioRxivPreprint
Cite
@misc{segev2022epigenetic,
title = {{Epigenetic aging waves: artificial intelligence detects clustering of switch points in DNA methylation rate in defined sex-dependent age periods}},
author = {Elad Segev and Tamar Shahal and Thomas Konstantinovsky and Yonit Marcus and Gabi Shefer and Yuval Ebenstein and Metsada Pasmanik-Chor and Naftali Stern},
howpublished = {bioRxiv},
year = {2022},
doi = {10.1101/2022.10.02.510495},
url = {https://doi.org/10.1101/2022.10.02.510495}
}
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Earlier work2
2021A new approach to text rating classification using sentiment analysisarXivPreprint
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@misc{konstantinovsky2021new,
title = {{A new approach to text rating classification using sentiment analysis}},
author = {Thomas Konstantinovsky},
year = {2021},
eprint = {2103.12368},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2103.12368}
}
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2021On bi-gram graph attributesComputer and Information Science
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@article{konstantinovsky2021bigram,
title = {{On bi-gram graph attributes}},
author = {Thomas Konstantinovsky and Matan Mizrachi},
journal = {Computer and Information Science},
volume = {14},
number = {3},
pages = {78},
year = {2021},
doi = {10.5539/cis.v14n3p78},
url = {https://doi.org/10.5539/cis.v14n3p78}
}
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