Seqpare: a novel metric of similarity between genomic interval sets [version 2; peer review: 2 approved]
Searching genomic interval sets produced by sequencing methods has been widely and routinely performed; however, existing metrics for quantifying similarities among interval sets are inconsistent. Here we introduce Seqpare, a self-consistent and effective metric of similarity and tool for comparing...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2021-01-01
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| Series: | F1000Research |
| Online Access: | https://f1000research.com/articles/9-581/v2 |
| _version_ | 1828977003066294272 |
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| author | Selena C. Feng Nathan C. Sheffield Jianglin Feng |
| author_facet | Selena C. Feng Nathan C. Sheffield Jianglin Feng |
| author_sort | Selena C. Feng |
| collection | DOAJ |
| description | Searching genomic interval sets produced by sequencing methods has been widely and routinely performed; however, existing metrics for quantifying similarities among interval sets are inconsistent. Here we introduce Seqpare, a self-consistent and effective metric of similarity and tool for comparing sequences based on their interval sets. With this metric, the similarity of two interval sets is quantified by a single index, the ratio of their effective overlap over the union: an index of zero indicates unrelated interval sets, and an index of one means that the interval sets are identical. Analysis and tests confirm the effectiveness and self-consistency of the Seqpare metric. |
| first_indexed | 2024-12-14T14:50:02Z |
| format | Article |
| id | doaj.art-157aedf3a25a47b6ad70413f9a3a41bb |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-12-14T14:50:02Z |
| publishDate | 2021-01-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-157aedf3a25a47b6ad70413f9a3a41bb2022-12-21T22:57:09ZengF1000 Research LtdF1000Research2046-14022021-01-01910.12688/f1000research.23390.231408Seqpare: a novel metric of similarity between genomic interval sets [version 2; peer review: 2 approved]Selena C. Feng0Nathan C. Sheffield1Jianglin Feng2Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USACenter for Public Health Genomics, University of Virginia, Charlottesville, VA, USADeepstanding LLC, Crozet, VA, 22932, USASearching genomic interval sets produced by sequencing methods has been widely and routinely performed; however, existing metrics for quantifying similarities among interval sets are inconsistent. Here we introduce Seqpare, a self-consistent and effective metric of similarity and tool for comparing sequences based on their interval sets. With this metric, the similarity of two interval sets is quantified by a single index, the ratio of their effective overlap over the union: an index of zero indicates unrelated interval sets, and an index of one means that the interval sets are identical. Analysis and tests confirm the effectiveness and self-consistency of the Seqpare metric.https://f1000research.com/articles/9-581/v2 |
| spellingShingle | Selena C. Feng Nathan C. Sheffield Jianglin Feng Seqpare: a novel metric of similarity between genomic interval sets [version 2; peer review: 2 approved] F1000Research |
| title | Seqpare: a novel metric of similarity between genomic interval sets [version 2; peer review: 2 approved] |
| title_full | Seqpare: a novel metric of similarity between genomic interval sets [version 2; peer review: 2 approved] |
| title_fullStr | Seqpare: a novel metric of similarity between genomic interval sets [version 2; peer review: 2 approved] |
| title_full_unstemmed | Seqpare: a novel metric of similarity between genomic interval sets [version 2; peer review: 2 approved] |
| title_short | Seqpare: a novel metric of similarity between genomic interval sets [version 2; peer review: 2 approved] |
| title_sort | seqpare a novel metric of similarity between genomic interval sets version 2 peer review 2 approved |
| url | https://f1000research.com/articles/9-581/v2 |
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