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Binary Interval Search: a scalable algorithm for counting interval intersections
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | Bioinformatics, 29, 2013, 1, S. 1-7 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
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Layer, Ryan M. Skadron, Kevin Robins, Gabriel Hall, Ira M. Quinlan, Aaron R. Layer, Ryan M. Skadron, Kevin Robins, Gabriel Hall, Ira M. Quinlan, Aaron R. |
|---|---|
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Layer, Ryan M. Skadron, Kevin Robins, Gabriel Hall, Ira M. Quinlan, Aaron R. |
| spellingShingle |
Layer, Ryan M. Skadron, Kevin Robins, Gabriel Hall, Ira M. Quinlan, Aaron R. Bioinformatics Binary Interval Search: a scalable algorithm for counting interval intersections Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
layer, ryan m. |
| spelling |
Layer, Ryan M. Skadron, Kevin Robins, Gabriel Hall, Ira M. Quinlan, Aaron R. 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bts652 <jats:title>Abstract</jats:title> <jats:p>Motivation: The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery.</jats:p> <jats:p>Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals.</jats:p> <jats:p>Availability: https://github.com/arq5x/bits.</jats:p> <jats:p>Contact: arq5x@virginia.edu</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> Binary Interval Search: a scalable algorithm for counting interval intersections Bioinformatics |
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| title |
Binary Interval Search: a scalable algorithm for counting interval intersections |
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Binary Interval Search: a scalable algorithm for counting interval intersections |
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Binary Interval Search: a scalable algorithm for counting interval intersections |
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Binary Interval Search: a scalable algorithm for counting interval intersections |
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Binary Interval Search: a scalable algorithm for counting interval intersections |
| title_short |
Binary Interval Search: a scalable algorithm for counting interval intersections |
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binary interval search: a scalable algorithm for counting interval intersections |
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Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/bts652 |
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2013 |
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1-7 |
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<jats:title>Abstract</jats:title>
<jats:p>Motivation: The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery.</jats:p>
<jats:p>Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals.</jats:p>
<jats:p>Availability: https://github.com/arq5x/bits.</jats:p>
<jats:p>Contact: arq5x@virginia.edu</jats:p>
<jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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| author | Layer, Ryan M., Skadron, Kevin, Robins, Gabriel, Hall, Ira M., Quinlan, Aaron R. |
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| description | <jats:title>Abstract</jats:title> <jats:p>Motivation: The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery.</jats:p> <jats:p>Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals.</jats:p> <jats:p>Availability: https://github.com/arq5x/bits.</jats:p> <jats:p>Contact: arq5x@virginia.edu</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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| spelling | Layer, Ryan M. Skadron, Kevin Robins, Gabriel Hall, Ira M. Quinlan, Aaron R. 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bts652 <jats:title>Abstract</jats:title> <jats:p>Motivation: The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery.</jats:p> <jats:p>Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals.</jats:p> <jats:p>Availability: https://github.com/arq5x/bits.</jats:p> <jats:p>Contact: arq5x@virginia.edu</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> Binary Interval Search: a scalable algorithm for counting interval intersections Bioinformatics |
| spellingShingle | Layer, Ryan M., Skadron, Kevin, Robins, Gabriel, Hall, Ira M., Quinlan, Aaron R., Bioinformatics, Binary Interval Search: a scalable algorithm for counting interval intersections, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Binary Interval Search: a scalable algorithm for counting interval intersections |
| title_full | Binary Interval Search: a scalable algorithm for counting interval intersections |
| title_fullStr | Binary Interval Search: a scalable algorithm for counting interval intersections |
| title_full_unstemmed | Binary Interval Search: a scalable algorithm for counting interval intersections |
| title_short | Binary Interval Search: a scalable algorithm for counting interval intersections |
| title_sort | binary interval search: a scalable algorithm for counting interval intersections |
| title_unstemmed | Binary Interval Search: a scalable algorithm for counting interval intersections |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/bts652 |