MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool
| Publication Date: |
2018-03-14
|
|---|---|
| Publisher: |
Oxford University Press
|
| Print ISSN: |
1367-4803
|
| Electronic ISSN: |
1460-2059
|
| Topics: |
Biology
Computer Science
Medicine
|
| Published by: |
| _version_ | 1836398844412166144 |
|---|---|
| autor | Lochovsky L, Zhang J, Gerstein M, et al. |
| beschreibung | Summary Identifying genomic regions with higher than expected mutation count is useful for cancer driver detection. Previous parametric approaches require numerous cell-type-matched covariates for accurate background mutation rate (BMR) estimation, which is not practical for many situations. Non-parametric, permutation-based approaches avoid this issue but usually suffer from considerable compute-time cost. Hence, we introduce Mutations Overburdening Annotations Tool (MOAT), a non-parametric scheme that makes no assumptions about mutation process except requiring that the BMR changes smoothly with genomic features. MOAT randomly permutes single-nucleotide variants, or target regions, on a relatively large scale to provide robust burden analysis. Furthermore, we show how we can do permutations in an efficient manner using graphics processing unit acceleration, speeding up the calculation by a factor of ∼250. Availability and implementation MOAT is available at moat.gersteinlab.org . Contact mark@gersteinlab.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online. |
| citation_standardnr | 6205877 |
| datenlieferant | ipn_articles |
| feed_id | 2184 |
| feed_publisher | Oxford University Press |
| feed_publisher_url | http://global.oup.com/ |
| insertion_date | 2018-03-14 |
| journaleissn | 1460-2059 |
| journalissn | 1367-4803 |
| publikationsjahr_anzeige | 2018 |
| publikationsjahr_facette | 2018 |
| publikationsjahr_intervall | 7984:2015-2019 |
| publikationsjahr_sort | 2018 |
| publisher | Oxford University Press |
| quelle | Bioinformatics |
| relation | https://academic.oup.com/bioinformatics/article/34/6/1031/4600182?rss=1 |
| search_space | articles |
| shingle_author_1 | Lochovsky L, Zhang J, Gerstein M, et al. |
| shingle_author_2 | Lochovsky L, Zhang J, Gerstein M, et al. |
| shingle_author_3 | Lochovsky L, Zhang J, Gerstein M, et al. |
| shingle_author_4 | Lochovsky L, Zhang J, Gerstein M, et al. |
| shingle_catch_all_1 | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool Summary Identifying genomic regions with higher than expected mutation count is useful for cancer driver detection. Previous parametric approaches require numerous cell-type-matched covariates for accurate background mutation rate (BMR) estimation, which is not practical for many situations. Non-parametric, permutation-based approaches avoid this issue but usually suffer from considerable compute-time cost. Hence, we introduce Mutations Overburdening Annotations Tool (MOAT), a non-parametric scheme that makes no assumptions about mutation process except requiring that the BMR changes smoothly with genomic features. MOAT randomly permutes single-nucleotide variants, or target regions, on a relatively large scale to provide robust burden analysis. Furthermore, we show how we can do permutations in an efficient manner using graphics processing unit acceleration, speeding up the calculation by a factor of ∼250. Availability and implementation MOAT is available at moat.gersteinlab.org . Contact mark@gersteinlab.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online. Lochovsky L, Zhang J, Gerstein M, et al. Oxford University Press 1367-4803 13674803 1460-2059 14602059 |
| shingle_catch_all_2 | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool Summary Identifying genomic regions with higher than expected mutation count is useful for cancer driver detection. Previous parametric approaches require numerous cell-type-matched covariates for accurate background mutation rate (BMR) estimation, which is not practical for many situations. Non-parametric, permutation-based approaches avoid this issue but usually suffer from considerable compute-time cost. Hence, we introduce Mutations Overburdening Annotations Tool (MOAT), a non-parametric scheme that makes no assumptions about mutation process except requiring that the BMR changes smoothly with genomic features. MOAT randomly permutes single-nucleotide variants, or target regions, on a relatively large scale to provide robust burden analysis. Furthermore, we show how we can do permutations in an efficient manner using graphics processing unit acceleration, speeding up the calculation by a factor of ∼250. Availability and implementation MOAT is available at moat.gersteinlab.org . Contact mark@gersteinlab.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online. Lochovsky L, Zhang J, Gerstein M, et al. Oxford University Press 1367-4803 13674803 1460-2059 14602059 |
| shingle_catch_all_3 | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool Summary Identifying genomic regions with higher than expected mutation count is useful for cancer driver detection. Previous parametric approaches require numerous cell-type-matched covariates for accurate background mutation rate (BMR) estimation, which is not practical for many situations. Non-parametric, permutation-based approaches avoid this issue but usually suffer from considerable compute-time cost. Hence, we introduce Mutations Overburdening Annotations Tool (MOAT), a non-parametric scheme that makes no assumptions about mutation process except requiring that the BMR changes smoothly with genomic features. MOAT randomly permutes single-nucleotide variants, or target regions, on a relatively large scale to provide robust burden analysis. Furthermore, we show how we can do permutations in an efficient manner using graphics processing unit acceleration, speeding up the calculation by a factor of ∼250. Availability and implementation MOAT is available at moat.gersteinlab.org . Contact mark@gersteinlab.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online. Lochovsky L, Zhang J, Gerstein M, et al. Oxford University Press 1367-4803 13674803 1460-2059 14602059 |
| shingle_catch_all_4 | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool Summary Identifying genomic regions with higher than expected mutation count is useful for cancer driver detection. Previous parametric approaches require numerous cell-type-matched covariates for accurate background mutation rate (BMR) estimation, which is not practical for many situations. Non-parametric, permutation-based approaches avoid this issue but usually suffer from considerable compute-time cost. Hence, we introduce Mutations Overburdening Annotations Tool (MOAT), a non-parametric scheme that makes no assumptions about mutation process except requiring that the BMR changes smoothly with genomic features. MOAT randomly permutes single-nucleotide variants, or target regions, on a relatively large scale to provide robust burden analysis. Furthermore, we show how we can do permutations in an efficient manner using graphics processing unit acceleration, speeding up the calculation by a factor of ∼250. Availability and implementation MOAT is available at moat.gersteinlab.org . Contact mark@gersteinlab.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online. Lochovsky L, Zhang J, Gerstein M, et al. Oxford University Press 1367-4803 13674803 1460-2059 14602059 |
| shingle_title_1 | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool |
| shingle_title_2 | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool |
| shingle_title_3 | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool |
| shingle_title_4 | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool |
| timestamp | 2025-06-30T23:33:32.318Z |
| titel | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool |
| titel_suche | MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool |
| topic | W SQ-SU WW-YZ |
| uid | ipn_articles_6205877 |