GASOLINE: detecting germline and somatic structural variants from long-reads data
Abstract Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identificati...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-48285-0 |
| _version_ | 1797388420383768576 |
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| author | Alberto Magi Gianluca Mattei Alessandra Mingrino Chiara Caprioli Chiara Ronchini Gianmaria Frigè Roberto Semeraro Marta Baragli Davide Bolognini Emanuela Colombo Luca Mazzarella Pier Giuseppe Pelicci |
| author_facet | Alberto Magi Gianluca Mattei Alessandra Mingrino Chiara Caprioli Chiara Ronchini Gianmaria Frigè Roberto Semeraro Marta Baragli Davide Bolognini Emanuela Colombo Luca Mazzarella Pier Giuseppe Pelicci |
| author_sort | Alberto Magi |
| collection | DOAJ |
| description | Abstract Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identification, however, requires complex computational methods, based on either read-depth or intra- and inter-alignment signatures approaches, which are limited by size or type of SVs. Moreover, most currently available tools only detect germline variants, thus requiring separate computation of sample pairs for comparative analyses. To overcome these limits, we developed a novel tool (Germline And SOmatic structuraL varIants detectioN and gEnotyping; GASOLINE) that groups SV signatures using a sophisticated clustering procedure based on a modified reciprocal overlap criterion, and is designed to identify germline SVs, from single samples, and somatic SVs from paired test and control samples. GASOLINE is a collection of Perl, R and Fortran codes, it analyzes aligned data in BAM format and produces VCF files with statistically significant somatic SVs. Germline or somatic analysis of 30 $$\times $$ × sequencing coverage experiments requires 4–5 h with 20 threads. GASOLINE outperformed currently available methods in the detection of both germline and somatic SVs in synthetic and real long-reads datasets. Notably, when applied on a pair of metastatic melanoma and matched-normal sample, GASOLINE identified five genuine somatic SVs that were missed using five different sequencing technologies and state-of-the art SV calling approaches. Thus, GASOLINE identifies germline and somatic SVs with unprecedented accuracy and resolution, outperforming currently available state-of-the-art WGS long-reads computational methods. |
| first_indexed | 2024-03-08T22:40:36Z |
| format | Article |
| id | doaj.art-9b134809fa3345dab06c684dc490b0e0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T22:40:36Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-9b134809fa3345dab06c684dc490b0e02023-12-17T12:13:05ZengNature PortfolioScientific Reports2045-23222023-11-0113111110.1038/s41598-023-48285-0GASOLINE: detecting germline and somatic structural variants from long-reads dataAlberto Magi0Gianluca Mattei1Alessandra Mingrino2Chiara Caprioli3Chiara Ronchini4Gianmaria Frigè5Roberto Semeraro6Marta Baragli7Davide Bolognini8Emanuela Colombo9Luca Mazzarella10Pier Giuseppe Pelicci11Department of Information Engineering, University of FlorenceDepartment of Information Engineering, University of FlorenceDepartment of Experimental and Clinical Medicine, University of FlorenceDepartment of Experimental Oncology, IEO European Institute of Oncology IRCCSDepartment of Experimental Oncology, IEO European Institute of Oncology IRCCSDepartment of Experimental Oncology, IEO European Institute of Oncology IRCCSDepartment of Experimental and Clinical Medicine, University of FlorenceDepartment of Information Engineering, University of FlorenceDepartment of Experimental and Clinical Medicine, University of FlorenceDepartment of Experimental Oncology, IEO European Institute of Oncology IRCCSDepartment of Experimental Oncology, IEO European Institute of Oncology IRCCSDepartment of Experimental Oncology, IEO European Institute of Oncology IRCCSAbstract Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identification, however, requires complex computational methods, based on either read-depth or intra- and inter-alignment signatures approaches, which are limited by size or type of SVs. Moreover, most currently available tools only detect germline variants, thus requiring separate computation of sample pairs for comparative analyses. To overcome these limits, we developed a novel tool (Germline And SOmatic structuraL varIants detectioN and gEnotyping; GASOLINE) that groups SV signatures using a sophisticated clustering procedure based on a modified reciprocal overlap criterion, and is designed to identify germline SVs, from single samples, and somatic SVs from paired test and control samples. GASOLINE is a collection of Perl, R and Fortran codes, it analyzes aligned data in BAM format and produces VCF files with statistically significant somatic SVs. Germline or somatic analysis of 30 $$\times $$ × sequencing coverage experiments requires 4–5 h with 20 threads. GASOLINE outperformed currently available methods in the detection of both germline and somatic SVs in synthetic and real long-reads datasets. Notably, when applied on a pair of metastatic melanoma and matched-normal sample, GASOLINE identified five genuine somatic SVs that were missed using five different sequencing technologies and state-of-the art SV calling approaches. Thus, GASOLINE identifies germline and somatic SVs with unprecedented accuracy and resolution, outperforming currently available state-of-the-art WGS long-reads computational methods.https://doi.org/10.1038/s41598-023-48285-0 |
| spellingShingle | Alberto Magi Gianluca Mattei Alessandra Mingrino Chiara Caprioli Chiara Ronchini Gianmaria Frigè Roberto Semeraro Marta Baragli Davide Bolognini Emanuela Colombo Luca Mazzarella Pier Giuseppe Pelicci GASOLINE: detecting germline and somatic structural variants from long-reads data Scientific Reports |
| title | GASOLINE: detecting germline and somatic structural variants from long-reads data |
| title_full | GASOLINE: detecting germline and somatic structural variants from long-reads data |
| title_fullStr | GASOLINE: detecting germline and somatic structural variants from long-reads data |
| title_full_unstemmed | GASOLINE: detecting germline and somatic structural variants from long-reads data |
| title_short | GASOLINE: detecting germline and somatic structural variants from long-reads data |
| title_sort | gasoline detecting germline and somatic structural variants from long reads data |
| url | https://doi.org/10.1038/s41598-023-48285-0 |
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