Academic Journal
BINSEQ: A family of high-performance binary formats for nucleotide sequences.
| Τίτλος: | BINSEQ: A family of high-performance binary formats for nucleotide sequences. |
|---|---|
| Συγγραφείς: | Teyssier, Noam, Dobin, Alexander |
| Πηγή: | PLoS Computational Biology; 5/28/2026, Vol. 22 Issue 5, p1-19, 19p |
| Θεματικοί όροι: | Parallel processing, Data compression, Storage, Nucleotide sequence, Sequence alignment, Real-time computing, Bioinformatics software |
| Περίληψη: | Modern genomics produces billions of sequencing records per run, which are typically stored as gzip-compressed FASTQ files. While this format is widely used, it is not optimal for high-throughput processing due to its reliance on single-threaded decompression and sequential parsing of irregularly sized records. This limitation is particularly problematic for applications that would benefit from parallel processing, such as read mapping, variant calling, and de novo assembly. Here, we present BINSEQ, a family of simple binary formats that enable high-throughput parallel processing of sequencing data. The BINSEQ family consists of two complementary implementations: BQ, optimized for fixed-length reads using a two-bit or four-bit encoding scheme with true random record access capability, and VBQ, designed for variable-length sequences with optional quality scores and block-based compression. We demonstrate that BINSEQ files are up to 90x faster than compressed FASTQ for parallel processing and can reduce analysis time from hours to minutes for large-scale genome and transcriptome analyses, particularly for resource-intensive applications like alignment, mapping, and de novo assembly. To facilitate adoption we provide high-performance libraries for reading and writing BINSEQ formats, native parallelization strategies with convenient APIs, and a command-line tool for conversion to and from traditional formats. Author summary: Modern sequencing technologies routinely generate billions of reads per experiment, yet the methods for storing and accessing these data have not kept pace. Sequencing reads remain predominantly stored in FASTQ, a text-based format designed for far smaller datasets. FASTQ's sequential parsing requirements and practical need for compression create a fundamental mismatch with modern multi-core architectures, where data access rather than computation has become the primary bottleneck. We address this problem with BINSEQ, a family of binary formats engineered for random access and native parallelization. Systematic benchmarking across applications of varying computational complexity demonstrates that BINSEQ achieves 90-fold improvements in data access and maintains substantial advantages in compute-intensive tasks such as genome alignment, reducing runtimes from hours to minutes. We present two complementary implementations: BQ, optimized for simplicity and maximal throughput, and VBQ, designed for flexibility while maintaining high performance. By reconsidering the relationship between storage architecture and parallel processing capabilities, BINSEQ provides a practical solution to a critical infrastructure challenge in high-throughput genomics. [ABSTRACT FROM AUTHOR] |
| Copyright of PLoS Computational Biology is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: BINSEQ: A family of high-performance binary formats for nucleotide sequences. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Teyssier%2C+Noam%22">Teyssier, Noam</searchLink><br /><searchLink fieldCode="AR" term="%22Dobin%2C+Alexander%22">Dobin, Alexander</searchLink> – Name: TitleSource Label: Source Group: Src Data: PLoS Computational Biology; 5/28/2026, Vol. 22 Issue 5, p1-19, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+compression%22">Data compression</searchLink><br /><searchLink fieldCode="DE" term="%22Storage%22">Storage</searchLink><br /><searchLink fieldCode="DE" term="%22Nucleotide+sequence%22">Nucleotide sequence</searchLink><br /><searchLink fieldCode="DE" term="%22Sequence+alignment%22">Sequence alignment</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics+software%22">Bioinformatics software</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Modern genomics produces billions of sequencing records per run, which are typically stored as gzip-compressed FASTQ files. While this format is widely used, it is not optimal for high-throughput processing due to its reliance on single-threaded decompression and sequential parsing of irregularly sized records. This limitation is particularly problematic for applications that would benefit from parallel processing, such as read mapping, variant calling, and de novo assembly. Here, we present BINSEQ, a family of simple binary formats that enable high-throughput parallel processing of sequencing data. The BINSEQ family consists of two complementary implementations: BQ, optimized for fixed-length reads using a two-bit or four-bit encoding scheme with true random record access capability, and VBQ, designed for variable-length sequences with optional quality scores and block-based compression. We demonstrate that BINSEQ files are up to 90x faster than compressed FASTQ for parallel processing and can reduce analysis time from hours to minutes for large-scale genome and transcriptome analyses, particularly for resource-intensive applications like alignment, mapping, and de novo assembly. To facilitate adoption we provide high-performance libraries for reading and writing BINSEQ formats, native parallelization strategies with convenient APIs, and a command-line tool for conversion to and from traditional formats. Author summary: Modern sequencing technologies routinely generate billions of reads per experiment, yet the methods for storing and accessing these data have not kept pace. Sequencing reads remain predominantly stored in FASTQ, a text-based format designed for far smaller datasets. FASTQ's sequential parsing requirements and practical need for compression create a fundamental mismatch with modern multi-core architectures, where data access rather than computation has become the primary bottleneck. We address this problem with BINSEQ, a family of binary formats engineered for random access and native parallelization. Systematic benchmarking across applications of varying computational complexity demonstrates that BINSEQ achieves 90-fold improvements in data access and maintains substantial advantages in compute-intensive tasks such as genome alignment, reducing runtimes from hours to minutes. We present two complementary implementations: BQ, optimized for simplicity and maximal throughput, and VBQ, designed for flexibility while maintaining high performance. By reconsidering the relationship between storage architecture and parallel processing capabilities, BINSEQ provides a practical solution to a critical infrastructure challenge in high-throughput genomics. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of PLoS Computational Biology is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1371/journal.pcbi.1014181 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Parallel processing Type: general – SubjectFull: Data compression Type: general – SubjectFull: Storage Type: general – SubjectFull: Nucleotide sequence Type: general – SubjectFull: Sequence alignment Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Bioinformatics software Type: general Titles: – TitleFull: BINSEQ: A family of high-performance binary formats for nucleotide sequences. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Teyssier, Noam – PersonEntity: Name: NameFull: Dobin, Alexander IsPartOfRelationships: – BibEntity: Dates: – D: 28 M: 05 Text: 5/28/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1553734X Numbering: – Type: volume Value: 22 – Type: issue Value: 5 Titles: – TitleFull: PLoS Computational Biology Type: main |
| ResultId | 1 |