InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining
Abstract 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 su...
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| Format: | Article |
| Language: | English |
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BMC
2020-03-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12864-020-6585-1 |
| _version_ | 1818266657272889344 |
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| author | Zhongjia Wu Yufeng Wu Jingyang Gao |
| author_facet | Zhongjia Wu Yufeng Wu Jingyang Gao |
| author_sort | Zhongjia Wu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-12T20:10:11Z |
| format | Article |
| id | doaj.art-06ce62c8738d4aa88c14aee69d2d03a5 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-12T20:10:11Z |
| publishDate | 2020-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-06ce62c8738d4aa88c14aee69d2d03a52022-12-22T00:13:31ZengBMCBMC Genomics1471-21642020-03-0121S111010.1186/s12864-020-6585-1InvBFM: finding genomic inversions from high-throughput sequence data based on feature miningZhongjia Wu0Yufeng Wu1Jingyang Gao2College of Information Science and Technology, Beijing University of Chemical TechnologyDepartment of Computer Science and Engineering, University of ConnecticutCollege of Information Science and Technology, Beijing University of Chemical TechnologyAbstract 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.http://link.springer.com/article/10.1186/s12864-020-6585-1GenomicsHigh-throughput sequencingStructural variationInversionSupport vector machine |
| spellingShingle | Zhongjia Wu Yufeng Wu Jingyang Gao InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining BMC Genomics Genomics High-throughput sequencing Structural variation Inversion Support vector machine |
| title | InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining |
| title_full | InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining |
| title_fullStr | InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining |
| title_full_unstemmed | InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining |
| title_short | InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining |
| title_sort | invbfm finding genomic inversions from high throughput sequence data based on feature mining |
| topic | Genomics High-throughput sequencing Structural variation Inversion Support vector machine |
| url | http://link.springer.com/article/10.1186/s12864-020-6585-1 |
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