Interpreting alignment-free sequence comparison: what makes a score a good score? Article Swipe
Martin Swain
,
Martin Vickers
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/nargab/lqac062
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/nargab/lqac062
Alignment-free methods are alternatives to alignment-based methods when searching sequence data sets. The output from an alignment-free sequence comparison is a similarity score, the interpretation of which is not straightforward. We propose objective functions to interpret and calibrate outputs from alignment-free searches, noting that different objective functions are necessary for different biological contexts. This leads to advantages: visualising and comparing score distributions, including those from true positives, may be a relatively simple method to gain insight into the performance of different metrics. Using an empirical approach with both DNA and protein sequences, we characterise different similarity score distributions generated under different parameters. In particular, we demonstrate how sequence length can affect the scores. We show that scores of true positive sequence pairs may correlate significantly with their mean length; and even if the correlation is weak, the relative difference in length of the sequence pair may significantly reduce the effectiveness of alignment-free metrics. Importantly, we show how objective functions can be used with test data to accurately estimate the probability of true positives. This can significantly increase the utility of alignment-free approaches. Finally, we have developed a general-purpose software tool called KAST for use in high-throughput workflows on Linux clusters.
Related Topics
Concepts
Sequence (biology)
Similarity (geometry)
False positive paradox
Multiple sequence alignment
Computer science
Correlation
Score
Sequence alignment
Pattern recognition (psychology)
Data mining
Artificial intelligence
Algorithm
Statistics
Mathematics
Machine learning
Biology
Image (mathematics)
Genetics
Biochemistry
Gene
Peptide sequence
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/nargab/lqac062
- https://academic.oup.com/nargab/article-pdf/4/3/lqac062/45690656/lqac062.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4294591310
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4294591310Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/nargab/lqac062Digital Object Identifier
- Title
-
Interpreting alignment-free sequence comparison: what makes a score a good score?Work title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-07-09Full publication date if available
- Authors
-
Martin Swain, Martin VickersList of authors in order
- Landing page
-
https://doi.org/10.1093/nargab/lqac062Publisher landing page
- PDF URL
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https://academic.oup.com/nargab/article-pdf/4/3/lqac062/45690656/lqac062.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/nargab/article-pdf/4/3/lqac062/45690656/lqac062.pdfDirect OA link when available
- Concepts
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Sequence (biology), Similarity (geometry), False positive paradox, Multiple sequence alignment, Computer science, Correlation, Score, Sequence alignment, Pattern recognition (psychology), Data mining, Artificial intelligence, Algorithm, Statistics, Mathematics, Machine learning, Biology, Image (mathematics), Genetics, Biochemistry, Gene, Peptide sequence, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
72Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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