Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distribution
ABSTRACT Next-generation sequencing technologies have enabled many advances across diverse areas of biology, with many benefiting from increased sample size. Although the cost of running next-generation sequencing instruments has dropped substantially over time, the cost of sample preparation method...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Society for Microbiology
2023-08-01
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| Series: | mSystems |
| Subjects: | |
| Online Access: | https://journals.asm.org/doi/10.1128/msystems.00006-23 |
| _version_ | 1797730336200720384 |
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| author | Caitriona Brennan Rodolfo A. Salido Pedro Belda-Ferre MacKenzie Bryant Charles Cowart Maria D. Tiu Antonio González Daniel McDonald Caitlin Tribelhorn Amir Zarrinpar Rob Knight |
| author_facet | Caitriona Brennan Rodolfo A. Salido Pedro Belda-Ferre MacKenzie Bryant Charles Cowart Maria D. Tiu Antonio González Daniel McDonald Caitlin Tribelhorn Amir Zarrinpar Rob Knight |
| author_sort | Caitriona Brennan |
| collection | DOAJ |
| description | ABSTRACT Next-generation sequencing technologies have enabled many advances across diverse areas of biology, with many benefiting from increased sample size. Although the cost of running next-generation sequencing instruments has dropped substantially over time, the cost of sample preparation methods has lagged behind. To counter this, researchers have adapted library miniaturization protocols and large sample pools to maximize the number of samples that can be prepared by a certain amount of reagents and sequenced in a single run. However, due to high variability of sample quality, over and underrepresentation of samples in a sequencing run has become a major issue in high-throughput sequencing. This leads to misinterpretation of results due to increased noise, and additional time and cost rerunning underrepresented samples. To overcome this problem, we present a normalization method that uses shallow iSeq sequencing to accurately inform pooling volumes based on read distribution. This method is superior to the widely used fluorometry methods, which cannot specifically target adapter-ligated molecules that contribute to sequencing output. Our normalization method not only quantifies adapter-ligated molecules but also allows normalization of feature space; for example, we can normalize to reads of interest such as non-ribosomal reads. As a result, this normalization method improves the efficiency of high-throughput next-generation sequencing by reducing noise and producing higher average reads per sample with more even sequencing depth. IMPORTANCE High-throughput next generation sequencing (NGS) has significantly contributed to the field of genomics; however, further improvements can maximize the potential of this important tool. Uneven sequencing of samples in a multiplexed run is a common issue that leads to unexpected extra costs or low-quality data. To mitigate this problem, we introduce a normalization method based on read counts rather than library concentration. This method allows for an even distribution of features of interest across samples, improving the statistical power of data sets and preventing the financial loss associated with resequencing libraries. This method optimizes NGS, which already has huge importance across many areas of biology. |
| first_indexed | 2024-03-12T11:42:49Z |
| format | Article |
| id | doaj.art-ad7c5f3d3cf14b89bb7c978b9cc196e7 |
| institution | Directory Open Access Journal |
| issn | 2379-5077 |
| language | English |
| last_indexed | 2024-03-12T11:42:49Z |
| publishDate | 2023-08-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mSystems |
| spelling | doaj.art-ad7c5f3d3cf14b89bb7c978b9cc196e72023-08-31T13:00:43ZengAmerican Society for MicrobiologymSystems2379-50772023-08-018410.1128/msystems.00006-23Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distributionCaitriona Brennan0Rodolfo A. Salido1Pedro Belda-Ferre2MacKenzie Bryant3Charles Cowart4Maria D. Tiu5Antonio González6Daniel McDonald7Caitlin Tribelhorn8Amir Zarrinpar9Rob Knight10Department of Pediatrics, University of California San Diego , La Jolla, California, USADepartment of Bioengineering, University of California San Diego , La Jolla, California, USADepartment of Pediatrics, University of California San Diego , La Jolla, California, USADepartment of Pediatrics, University of California San Diego , La Jolla, California, USADepartment of Pediatrics, University of California San Diego , La Jolla, California, USADivision of Gastroenterology, University of California San Diego , La Jolla, California, USADepartment of Pediatrics, University of California San Diego , La Jolla, California, USADepartment of Pediatrics, University of California San Diego , La Jolla, California, USADepartment of Pediatrics, University of California San Diego , La Jolla, California, USADivision of Gastroenterology, University of California San Diego , La Jolla, California, USADepartment of Pediatrics, University of California San Diego , La Jolla, California, USAABSTRACT Next-generation sequencing technologies have enabled many advances across diverse areas of biology, with many benefiting from increased sample size. Although the cost of running next-generation sequencing instruments has dropped substantially over time, the cost of sample preparation methods has lagged behind. To counter this, researchers have adapted library miniaturization protocols and large sample pools to maximize the number of samples that can be prepared by a certain amount of reagents and sequenced in a single run. However, due to high variability of sample quality, over and underrepresentation of samples in a sequencing run has become a major issue in high-throughput sequencing. This leads to misinterpretation of results due to increased noise, and additional time and cost rerunning underrepresented samples. To overcome this problem, we present a normalization method that uses shallow iSeq sequencing to accurately inform pooling volumes based on read distribution. This method is superior to the widely used fluorometry methods, which cannot specifically target adapter-ligated molecules that contribute to sequencing output. Our normalization method not only quantifies adapter-ligated molecules but also allows normalization of feature space; for example, we can normalize to reads of interest such as non-ribosomal reads. As a result, this normalization method improves the efficiency of high-throughput next-generation sequencing by reducing noise and producing higher average reads per sample with more even sequencing depth. IMPORTANCE High-throughput next generation sequencing (NGS) has significantly contributed to the field of genomics; however, further improvements can maximize the potential of this important tool. Uneven sequencing of samples in a multiplexed run is a common issue that leads to unexpected extra costs or low-quality data. To mitigate this problem, we introduce a normalization method based on read counts rather than library concentration. This method allows for an even distribution of features of interest across samples, improving the statistical power of data sets and preventing the financial loss associated with resequencing libraries. This method optimizes NGS, which already has huge importance across many areas of biology.https://journals.asm.org/doi/10.1128/msystems.00006-23metagenomicslarge-scale studiesNGS normalizationautomationmultiplexingquantification |
| spellingShingle | Caitriona Brennan Rodolfo A. Salido Pedro Belda-Ferre MacKenzie Bryant Charles Cowart Maria D. Tiu Antonio González Daniel McDonald Caitlin Tribelhorn Amir Zarrinpar Rob Knight Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distribution mSystems metagenomics large-scale studies NGS normalization automation multiplexing quantification |
| title | Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distribution |
| title_full | Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distribution |
| title_fullStr | Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distribution |
| title_full_unstemmed | Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distribution |
| title_short | Maximizing the potential of high-throughput next-generation sequencing through precise normalization based on read count distribution |
| title_sort | maximizing the potential of high throughput next generation sequencing through precise normalization based on read count distribution |
| topic | metagenomics large-scale studies NGS normalization automation multiplexing quantification |
| url | https://journals.asm.org/doi/10.1128/msystems.00006-23 |
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