Haplotype-aware diplotyping from noisy long reads
| Main Authors: | Ebler,Jana, Haukness, Marina, Pesout, Trevor, Paten, Benedict, Marschall, Tobias |
|---|---|
| Format: | info publication-preprint Journal |
| Terbitan: |
, 2018
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/2616973 |
| ctrlnum |
2616973 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Ebler,Jana</creator><creator>Haukness, Marina</creator><creator>Pesout, Trevor</creator><creator>Paten, Benedict</creator><creator>Marschall, Tobias</creator><date>2018-12-18</date><description>SNP calls for individual NA12878 produced by MarginPhase and WhatsHap on PacBio and Oxford Nanopore data as well as the version of source code used to generate this dataset.
Paper Abstract: Current genotyping approaches for single nucleotide variations rely on short, accurate reads from second generation sequencing devices. Presently, third generation sequencing platforms are rapidly becoming more widespread, yet approaches for leveraging their long but error-prone reads for genotyping are lacking. Here, we introduce a novel statistical framework for the joint inference of haplotypes and genotypes from noisy long reads, which we term diplotyping. Our technique takes full advantage of linkage information provided by long reads. We validate hundreds of thousands of candidate variants that have not yet been included in the high-confidence reference set of the Genome-in-a-Bottle effort.
Source Code:
WhatsHap: bitbucket.org/whatshap/whatshap
MarginPhase: github.com/benedictpaten/marginPhase</description><identifier>https://zenodo.org/record/2616973</identifier><identifier>10.5281/zenodo.2616973</identifier><identifier>oai:zenodo.org:2616973</identifier><relation>doi:10.5281/zenodo.1319975</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>Computational genomics, long-reads, genotyping, phasing, diplotypes</subject><title>Haplotype-aware diplotyping from noisy long reads</title><type>Other:info:eu-repo/semantics/preprint</type><type>Other:publication-preprint</type><recordID>2616973</recordID></dc>
|
| format |
Other:info:eu-repo/semantics/preprint Other Other:publication-preprint Journal:Journal Journal |
| author |
Ebler,Jana Haukness, Marina Pesout, Trevor Paten, Benedict Marschall, Tobias |
| title |
Haplotype-aware diplotyping from noisy long reads |
| publishDate |
2018 |
| topic |
Computational genomics long-reads genotyping phasing diplotypes |
| url |
https://zenodo.org/record/2616973 |
| contents |
SNP calls for individual NA12878 produced by MarginPhase and WhatsHap on PacBio and Oxford Nanopore data as well as the version of source code used to generate this dataset.
Paper Abstract: Current genotyping approaches for single nucleotide variations rely on short, accurate reads from second generation sequencing devices. Presently, third generation sequencing platforms are rapidly becoming more widespread, yet approaches for leveraging their long but error-prone reads for genotyping are lacking. Here, we introduce a novel statistical framework for the joint inference of haplotypes and genotypes from noisy long reads, which we term diplotyping. Our technique takes full advantage of linkage information provided by long reads. We validate hundreds of thousands of candidate variants that have not yet been included in the high-confidence reference set of the Genome-in-a-Bottle effort.
Source Code:
WhatsHap: bitbucket.org/whatshap/whatshap
MarginPhase: github.com/benedictpaten/marginPhase |
| id |
IOS16997.2616973 |
| institution |
ZAIN Publications |
| institution_id |
7213 |
| institution_type |
library:special library |
| library |
Cognizance Journal of Multidisciplinary Studies |
| library_id |
5267 |
| collection |
Cognizance Journal of Multidisciplinary Studies |
| repository_id |
16997 |
| subject_area |
Multidisciplinary |
| city |
Stockholm |
| province |
INTERNASIONAL |
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1 |
| repoId |
IOS16997 |
| first_indexed |
2022-06-06T04:45:52Z |
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2022-06-06T04:45:52Z |
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dc |
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1734903148134268928 |
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17.610363 |