ArticleBMC genomics2020
InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Unravelling inversions: Technological advances, challenges, and potential impact on crop breeding.Plant biotechnology journal · 2024Review
- cnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network.BMC bioinformatics · 2023Article
- Detecting inversions in routine molecular diagnosis in MMR genes.Familial cancer · 2022Article
- Fine Breakpoint Mapping by Genome Sequencing Reveals the First Large X Inversion Disrupting theInternational journal of molecular sciences · 2021Article
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Authors and funding
3 authors.
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Abstract
backgroundGenomic 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.
resultIn 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.
conclusionsOur 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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