Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads
Open accessAbstract
High-volume sequencing of DNA and RNA is now within reach of any research laboratory and is quickly becoming established as a key research tool. In many workflows, each of the short sequences (“reads”) resulting from a sequencing run are first “mapped” (aligned) to a reference sequence to infer the read from which the genomic location derived, a challenging task because of the high data volumes and often large genomes. Existing read mapping software excel in either speed (e.g., BWA, Bowtie, ELAND) or sensitivity (e.g., Novoalign), but not in both. In addition, performance often deteriorates in the presence of sequence variation, particularly so for short insertions and deletions (indels). Here, we present a read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation. This results in a higher useable sequence yield and improved accuracy compared to that of existing software.
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Stampy incorporates hashing to improve alignment accuracy in regions with high sequence variability, while primarily relying on the Burrows-Wheeler transform.
“While primarily relying on the Burrows-Wheeler transform for alignment, Stampy incorporates hashing to improve alignment accuracy in regions with high sequence variability [52].”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [52]
The algorithm description says Stampy uses a hash table to find candidate mapping locations, and the paper reports sensitivity when reads contain sequence variation. It does not say Stampy primarily relies on the Burrows–Wheeler transform: that data structure is attributed to BWA, which Stampy can use as a first stage in a recommended hybrid mode. (LLM, conf=0.88)
Experimental — accuracy not yet measuredStampy is exceptionally useful for aligning reads from organisms with significant genetic diversity or in studies focusing on evolutionary differences.
“This makes it exceptionally useful for aligning reads from organisms with significant genetic diversity or in studies focusing on evolutionary differences;”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [52]
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- Authors
- Lunter, G., & Goodson, M.
- Affiliations
- Centre for Human Genetics (GB)
- ORCID
- Lunter, Gerton
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2010-10-27
- Volume
- 21
- Issue
- 6
- Pages
- 936-939
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3106326
- OpenAlex
- W2187503591
- Cited by
- 1,016
- References
- 5
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 27 · 2023: 27 · 2022: 35 · 2021: 60 · 2020: 64 · 2019: 79 · 2018: 80 · 2017: 93 · 2016: 122 · 2015: 123 · 2014: 109 · 2013: 88 · 2012: 64
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Wellcome Trust (075491/Z/04/Z, 075491/Z/04,, 090532)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- sequence alignment, read mapping, genome mapping, alignment sensitivity
- MeSH terms
- Algorithms, Sensitivity and Specificity, Software, Models, Statistical, Sequence Alignment, Sequence Analysis, DNA
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