Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data
Abstract Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computationa...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-46614-z |
| _version_ | 1797247145656451072 |
|---|---|
| author | Yichen Henry Liu Can Luo Staunton G. Golding Jacob B. Ioffe Xin Maizie Zhou |
| author_facet | Yichen Henry Liu Can Luo Staunton G. Golding Jacob B. Ioffe Xin Maizie Zhou |
| author_sort | Yichen Henry Liu |
| collection | DOAJ |
| description | Abstract Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computational efficiency and lower coverage requirements, are prominent. Alternative approaches, relying solely on available reads for de novo genome assembly and employing assembly-based tools for SV detection via comparison to a reference genome, demand significantly more computational resources. However, the lack of comprehensive benchmarking constrains our comprehension and hampers further algorithm development. Here we systematically compare 14 read alignment-based SV calling methods (including 4 deep learning-based methods and 1 hybrid method), and 4 assembly-based SV calling methods, alongside 4 upstream aligners and 7 assemblers. Assembly-based tools excel in detecting large SVs, especially insertions, and exhibit robustness to evaluation parameter changes and coverage fluctuations. Conversely, alignment-based tools demonstrate superior genotyping accuracy at low sequencing coverage (5-10×) and excel in detecting complex SVs, like translocations, inversions, and duplications. Our evaluation provides performance insights, highlighting the absence of a universally superior tool. We furnish guidelines across 31 criteria combinations, aiding users in selecting the most suitable tools for diverse scenarios and offering directions for further method development. |
| first_indexed | 2024-04-24T19:54:03Z |
| format | Article |
| id | doaj.art-99142cf3d0274c468305226d7de086e6 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-24T19:54:03Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-99142cf3d0274c468305226d7de086e62024-03-24T12:26:15ZengNature PortfolioNature Communications2041-17232024-03-0115112210.1038/s41467-024-46614-zTradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing dataYichen Henry Liu0Can Luo1Staunton G. Golding2Jacob B. Ioffe3Xin Maizie Zhou4Department of Computer Science, Vanderbilt UniversityDepartment of Biomedical Engineering, Vanderbilt UniversityDepartment of Biomedical Engineering, Vanderbilt UniversityDepartment of Computer Science, Vanderbilt UniversityDepartment of Computer Science, Vanderbilt UniversityAbstract Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computational efficiency and lower coverage requirements, are prominent. Alternative approaches, relying solely on available reads for de novo genome assembly and employing assembly-based tools for SV detection via comparison to a reference genome, demand significantly more computational resources. However, the lack of comprehensive benchmarking constrains our comprehension and hampers further algorithm development. Here we systematically compare 14 read alignment-based SV calling methods (including 4 deep learning-based methods and 1 hybrid method), and 4 assembly-based SV calling methods, alongside 4 upstream aligners and 7 assemblers. Assembly-based tools excel in detecting large SVs, especially insertions, and exhibit robustness to evaluation parameter changes and coverage fluctuations. Conversely, alignment-based tools demonstrate superior genotyping accuracy at low sequencing coverage (5-10×) and excel in detecting complex SVs, like translocations, inversions, and duplications. Our evaluation provides performance insights, highlighting the absence of a universally superior tool. We furnish guidelines across 31 criteria combinations, aiding users in selecting the most suitable tools for diverse scenarios and offering directions for further method development.https://doi.org/10.1038/s41467-024-46614-z |
| spellingShingle | Yichen Henry Liu Can Luo Staunton G. Golding Jacob B. Ioffe Xin Maizie Zhou Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data Nature Communications |
| title | Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data |
| title_full | Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data |
| title_fullStr | Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data |
| title_full_unstemmed | Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data |
| title_short | Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data |
| title_sort | tradeoffs in alignment and assembly based methods for structural variant detection with long read sequencing data |
| url | https://doi.org/10.1038/s41467-024-46614-z |
| work_keys_str_mv | AT yichenhenryliu tradeoffsinalignmentandassemblybasedmethodsforstructuralvariantdetectionwithlongreadsequencingdata AT canluo tradeoffsinalignmentandassemblybasedmethodsforstructuralvariantdetectionwithlongreadsequencingdata AT stauntonggolding tradeoffsinalignmentandassemblybasedmethodsforstructuralvariantdetectionwithlongreadsequencingdata AT jacobbioffe tradeoffsinalignmentandassemblybasedmethodsforstructuralvariantdetectionwithlongreadsequencingdata AT xinmaiziezhou tradeoffsinalignmentandassemblybasedmethodsforstructuralvariantdetectionwithlongreadsequencingdata |