Genome biologyBioinformaticsPeer-reviewed journal article
Multi-context seeds enable fast and high-accuracy read mapping
Ivan TolstoganoviD, Marcel MartiniD, Nicolas Buchin, Kristoffer SahliniD
Abstract
A key step in sequence similarity search is to identify shared seeds between a query and a reference sequence. A well-known tradeoff is that longer seeds offer fast searches but reduce sensitivity in variable regions. We introduce multi-context seeds (MCS), which allow to store seeds with different lengths in the same index structure, thus retaining the advantages of both short and long seeds. We demonstrate the applicability of MCS by implementing them in strobealign. Strobealign with MCS substantially improves accuracy compared to the previous version with little cost in runtime and no memory overhead.
- Citations
- 2
- FWCI
- 4.10
- Field percentile
- top 8%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Efficient Seeding for Error-Prone Sequences with SubseqHash2
Xiang Li, Ke Chen, Mingfu Shao·BioinformaticsPeer-reviewed·Jul 23, 2025
- Flexible seed size enables ultra-fast and accurate read alignment
Kristoffer Sahlin·Genome biologyPeer-reviewed·Dec 15, 2022
- Context-Aware Seeds for Read Mapping
Hongyi Xin, Mingfu Shao, Carl Kingsford·Algorithms for Molecular BiologyPeer-reviewed·May 23, 2020
- Calibrating seed-based alignment heuristics with Sesame
Guillaume J. P. Filion, Ruggero Cortini, Eduard Valera Zorita·Frontiers in GeneticsPeer-reviewed·Jun 25, 2020
- BWA-MEME: BWA-MEM emulated with a machine learning approach
Youngmok Jung, Dongsu Han·BioinformaticsPeer-reviewed·Mar 3, 2022
- Entropy predicts sensitivity of pseudo-random seeds
Benjamin Dominik Maier, Kristoffer Sahlin·Genome ResearchPeer-reviewed·May 22, 2023