InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining Article Swipe
Zhongjia Wu
,
Yufeng Wu
,
Jingyang Gao
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1186/s12864-020-6585-1
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1186/s12864-020-6585-1
Background Genomic inversion is one type of structural variations (SVs) and is known to play an important biological role. An established problem in sequence data analysis is calling inversions from high-throughput sequence data. It is more difficult to detect inversions because they are surrounded by duplication or other types of SVs in the inversion areas. Existing inversion detection tools are mainly based on three approaches: paired-end reads, split-mapped reads, and assembly. However, existing tools suffer from unsatisfying precision or sensitivity (eg: only 50~60% sensitivity) and it needs to be improved. Result In this paper, we present a new inversion calling method called InvBFM. InvBFM calls inversions based on feature mining. InvBFM first gathers the results of existing inversion detection tools as candidates for inversions. It then extracts features from the inversions. Finally, it calls the true inversions by a trained support vector machine (SVM) classifier. Conclusions Our results on real sequence data from the 1000 Genomes Project show that by combining feature mining and a machine learning model, InvBFM outperforms existing tools. InvBFM is written in Python and Shell and is available for download at https://github.com/wzj1234/InvBFM .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12864-020-6585-1
- https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-020-6585-1
- OA Status
- gold
- Cited By
- 4
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3009947873
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- OpenAlex ID
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https://openalex.org/W3009947873Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12864-020-6585-1Digital Object Identifier
- Title
-
InvBFM: finding genomic inversions from high-throughput sequence data based on feature miningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-01Full publication date if available
- Authors
-
Zhongjia Wu, Yufeng Wu, Jingyang GaoList of authors in order
- Landing page
-
https://doi.org/10.1186/s12864-020-6585-1Publisher landing page
- PDF URL
-
https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-020-6585-1Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-020-6585-1Direct OA link when available
- Concepts
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Python (programming language), Computer science, Support vector machine, Classifier (UML), Inversion (geology), Data mining, Artificial intelligence, Pattern recognition (psychology), Biology, Operating system, Structural basin, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2023: 2, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
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20Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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