Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs
Antimicrobial resistance (AMR) is an escalating global health threat, often driven by the horizontal gene transfer (HGT) of resistance genes. Detecting AMR genes and understanding their genomic context within bacterial populations is crucial for mitigating the spread of resistance. In this study, we...
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MDPI AG
2024-10-01
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| author | Yusreen Shah Somayeh Kafaie |
| author_facet | Yusreen Shah Somayeh Kafaie |
| author_sort | Yusreen Shah |
| collection | DOAJ |
| description | Antimicrobial resistance (AMR) is an escalating global health threat, often driven by the horizontal gene transfer (HGT) of resistance genes. Detecting AMR genes and understanding their genomic context within bacterial populations is crucial for mitigating the spread of resistance. In this study, we evaluate the performance of three sequence alignment tools—Bandage, SPAligner, and GraphAligner—in identifying AMR gene sequences from assembly and de Bruijn graphs, which are commonly used in microbial genome assembly. Efficiently identifying these genes allows for the detection of neighboring genetic elements and possible HGT events, contributing to a deeper understanding of AMR dissemination. We compare the performance of the tools both qualitatively and quantitatively, analyzing the precision, computational efficiency, and accuracy in detecting AMR-related sequences. Our analysis reveals that Bandage offers the most precise and efficient identification of AMR gene sequences, followed by GraphAligner and SPAligner. The comparison includes evaluating the similarity of paths returned by each tool and measuring output accuracy using a modified edit distance metric. These results highlight Bandage’s potential for contributing to the accurate identification and study of AMR genes in bacterial populations, offering important insights into resistance mechanisms and potential targets for mitigating AMR spread. |
| format | Article |
| id | doaj-art-79efe96e56104a28ade913f213fe39b1 |
| institution | OA Journals |
| issn | 2076-2607 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Microorganisms |
| spelling | doaj-art-79efe96e56104a28ade913f213fe39b12025-08-20T01:53:57ZengMDPI AGMicroorganisms2076-26072024-10-011211216810.3390/microorganisms12112168Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly GraphsYusreen Shah0Somayeh Kafaie1Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, NS B3H 3C3, CanadaDepartment of Mathematics and Computing Science, Saint Mary’s University, Halifax, NS B3H 3C3, CanadaAntimicrobial resistance (AMR) is an escalating global health threat, often driven by the horizontal gene transfer (HGT) of resistance genes. Detecting AMR genes and understanding their genomic context within bacterial populations is crucial for mitigating the spread of resistance. In this study, we evaluate the performance of three sequence alignment tools—Bandage, SPAligner, and GraphAligner—in identifying AMR gene sequences from assembly and de Bruijn graphs, which are commonly used in microbial genome assembly. Efficiently identifying these genes allows for the detection of neighboring genetic elements and possible HGT events, contributing to a deeper understanding of AMR dissemination. We compare the performance of the tools both qualitatively and quantitatively, analyzing the precision, computational efficiency, and accuracy in detecting AMR-related sequences. Our analysis reveals that Bandage offers the most precise and efficient identification of AMR gene sequences, followed by GraphAligner and SPAligner. The comparison includes evaluating the similarity of paths returned by each tool and measuring output accuracy using a modified edit distance metric. These results highlight Bandage’s potential for contributing to the accurate identification and study of AMR genes in bacterial populations, offering important insights into resistance mechanisms and potential targets for mitigating AMR spread.https://www.mdpi.com/2076-2607/12/11/2168antimicrobial resistance (AMR)sequence alignmentassembly graphsBandageSPAlignerGraphAligner |
| spellingShingle | Yusreen Shah Somayeh Kafaie Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs Microorganisms antimicrobial resistance (AMR) sequence alignment assembly graphs Bandage SPAligner GraphAligner |
| title | Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs |
| title_full | Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs |
| title_fullStr | Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs |
| title_full_unstemmed | Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs |
| title_short | Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs |
| title_sort | evaluating sequence alignment tools for antimicrobial resistance gene detection in assembly graphs |
| topic | antimicrobial resistance (AMR) sequence alignment assembly graphs Bandage SPAligner GraphAligner |
| url | https://www.mdpi.com/2076-2607/12/11/2168 |
| work_keys_str_mv | AT yusreenshah evaluatingsequencealignmenttoolsforantimicrobialresistancegenedetectioninassemblygraphs AT somayehkafaie evaluatingsequencealignmenttoolsforantimicrobialresistancegenedetectioninassemblygraphs |