Accurate human genome analysis with element avidity sequencing
Abstract Background New sequencing technologies provide options for the scientific community to design studies and build clinical workflows. These options expand user choice, and can enable more accurate, scalable, or affordable workflows depending on the fit between scientist needs and platform cap...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06191-4 |
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| author | Andrew Carroll Alexey Kolesnikov Daniel E. Cook Lucas Brambrink Kelly N. Wiseman Sophie M. Billings Semyon Kruglyak Bryan R. Lajoie Junhua Zhao Shawn E. Levy Cory Y. McLean Kishwar Shafin Maria Nattestad Pi-Chuan Chang |
| author_facet | Andrew Carroll Alexey Kolesnikov Daniel E. Cook Lucas Brambrink Kelly N. Wiseman Sophie M. Billings Semyon Kruglyak Bryan R. Lajoie Junhua Zhao Shawn E. Levy Cory Y. McLean Kishwar Shafin Maria Nattestad Pi-Chuan Chang |
| author_sort | Andrew Carroll |
| collection | DOAJ |
| description | Abstract Background New sequencing technologies provide options for the scientific community to design studies and build clinical workflows. These options expand user choice, and can enable more accurate, scalable, or affordable workflows depending on the fit between scientist needs and platform capability. However, it is essential to understand the performance of these new technologies for different tasks, especially for capabilities that were not possible or tractable in prior technologies. We investigate the new sequencing technology avidity from Element Biosciences. to help the scientific community understand the performance of the options to generate sequencing data. Results We show that Element whole genome sequencing achieves higher mapping and variant calling accuracy compared to Illumina sequencing at the same coverage, with larger differences at lower coverages (20–30x). We quantify base error rates of Element reads, finding lower error rates, especially in homopolymer and tandem repeat regions. We use Element’s ability to generate paired end sequencing with longer insert sizes than typical short–read sequencing. We show that longer insert sizes result in even higher accuracy, with long insert Element sequencing giving more accurate genome analyses at all coverages. Conclusions New options for sequencing technologies can analyze genomes comparably or better than prior standard methods. |
| format | Article |
| id | doaj-art-87ef3a54d4034b79b7acca0640d7f450 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-87ef3a54d4034b79b7acca0640d7f4502025-08-20T03:46:15ZengBMCBMC Bioinformatics1471-21052025-07-0126111210.1186/s12859-025-06191-4Accurate human genome analysis with element avidity sequencingAndrew Carroll0Alexey Kolesnikov1Daniel E. Cook2Lucas Brambrink3Kelly N. Wiseman4Sophie M. Billings5Semyon Kruglyak6Bryan R. Lajoie7Junhua Zhao8Shawn E. Levy9Cory Y. McLean10Kishwar Shafin11Maria Nattestad12Pi-Chuan Chang13Google LLCGoogle LLCGoogle LLCGoogle LLCElement BiosciencesElement BiosciencesElement BiosciencesElement BiosciencesElement BiosciencesElement BiosciencesGoogle LLCGoogle LLCGoogle LLCGoogle LLCAbstract Background New sequencing technologies provide options for the scientific community to design studies and build clinical workflows. These options expand user choice, and can enable more accurate, scalable, or affordable workflows depending on the fit between scientist needs and platform capability. However, it is essential to understand the performance of these new technologies for different tasks, especially for capabilities that were not possible or tractable in prior technologies. We investigate the new sequencing technology avidity from Element Biosciences. to help the scientific community understand the performance of the options to generate sequencing data. Results We show that Element whole genome sequencing achieves higher mapping and variant calling accuracy compared to Illumina sequencing at the same coverage, with larger differences at lower coverages (20–30x). We quantify base error rates of Element reads, finding lower error rates, especially in homopolymer and tandem repeat regions. We use Element’s ability to generate paired end sequencing with longer insert sizes than typical short–read sequencing. We show that longer insert sizes result in even higher accuracy, with long insert Element sequencing giving more accurate genome analyses at all coverages. Conclusions New options for sequencing technologies can analyze genomes comparably or better than prior standard methods.https://doi.org/10.1186/s12859-025-06191-4GenomicsSequencingBioinformaticsWGSVariant calling |
| spellingShingle | Andrew Carroll Alexey Kolesnikov Daniel E. Cook Lucas Brambrink Kelly N. Wiseman Sophie M. Billings Semyon Kruglyak Bryan R. Lajoie Junhua Zhao Shawn E. Levy Cory Y. McLean Kishwar Shafin Maria Nattestad Pi-Chuan Chang Accurate human genome analysis with element avidity sequencing BMC Bioinformatics Genomics Sequencing Bioinformatics WGS Variant calling |
| title | Accurate human genome analysis with element avidity sequencing |
| title_full | Accurate human genome analysis with element avidity sequencing |
| title_fullStr | Accurate human genome analysis with element avidity sequencing |
| title_full_unstemmed | Accurate human genome analysis with element avidity sequencing |
| title_short | Accurate human genome analysis with element avidity sequencing |
| title_sort | accurate human genome analysis with element avidity sequencing |
| topic | Genomics Sequencing Bioinformatics WGS Variant calling |
| url | https://doi.org/10.1186/s12859-025-06191-4 |
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