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- Publisher Website: 10.1093/bioinformatics/btu338
- Scopus: eid_2-s2.0-84907027434
- PMID: 24833803
- WOS: WOS:000342912400057
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Article: FaSD-somatic: A fast and accurate somatic SNV detection algorithm for cancer genome sequencing data
Title | FaSD-somatic: A fast and accurate somatic SNV detection algorithm for cancer genome sequencing data |
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Authors | |
Issue Date | 2014 |
Citation | Bioinformatics, 2014, v. 30, n. 17, p. 2498-2500 How to Cite? |
Abstract | Summary: Recent advances in high-throughput sequencing technologies have enabled us to sequence large number of cancer samples to reveal novel insights into oncogenetic mechanisms. However, the presence of intratumoral heterogeneity, normal cell contamination and insufficient sequencing depth, together pose a challenge for detecting somatic mutations. Here we propose a fast and an accurate somatic single-nucleotide variations (SNVs) detection program, FaSD-somatic. The performance of FaSD-somatic is extensively assessed on various types of cancer against several state-of-the-Art somatic SNV detection programs. Benchmarked by somatic SNVs from either existing databases or de novo higher-depth sequencing data, FaSD-somatic has the best overall performance. Furthermore, FaSD-somatic is efficient, it finishes somatic SNV calling within 14 h on 50X whole genome sequencing data in paired samples. © The Author 2014. Published by Oxford University Press. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/198460 |
ISSN | 2023 Impact Factor: 4.4 2023 SCImago Journal Rankings: 2.574 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | WANG, W | en_US |
dc.contributor.author | WANG, P | en_US |
dc.contributor.author | XU, F | en_US |
dc.contributor.author | LUO, R | en_US |
dc.contributor.author | Wong, MP | en_US |
dc.contributor.author | Lam, TW | en_US |
dc.contributor.author | Wang, JJ | en_US |
dc.date.accessioned | 2014-07-07T07:00:31Z | - |
dc.date.available | 2014-07-07T07:00:31Z | - |
dc.date.issued | 2014 | en_US |
dc.identifier.citation | Bioinformatics, 2014, v. 30, n. 17, p. 2498-2500 | en_US |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | http://hdl.handle.net/10722/198460 | - |
dc.description.abstract | Summary: Recent advances in high-throughput sequencing technologies have enabled us to sequence large number of cancer samples to reveal novel insights into oncogenetic mechanisms. However, the presence of intratumoral heterogeneity, normal cell contamination and insufficient sequencing depth, together pose a challenge for detecting somatic mutations. Here we propose a fast and an accurate somatic single-nucleotide variations (SNVs) detection program, FaSD-somatic. The performance of FaSD-somatic is extensively assessed on various types of cancer against several state-of-the-Art somatic SNV detection programs. Benchmarked by somatic SNVs from either existing databases or de novo higher-depth sequencing data, FaSD-somatic has the best overall performance. Furthermore, FaSD-somatic is efficient, it finishes somatic SNV calling within 14 h on 50X whole genome sequencing data in paired samples. © The Author 2014. Published by Oxford University Press. All rights reserved. | - |
dc.language | eng | en_US |
dc.relation.ispartof | Bioinformatics | en_US |
dc.title | FaSD-somatic: A fast and accurate somatic SNV detection algorithm for cancer genome sequencing data | en_US |
dc.type | Article | en_US |
dc.identifier.email | Wong, MP: mwpik@hku.hk | en_US |
dc.identifier.email | Lam, TW: hresltk@hkucc.hku.hk | en_US |
dc.identifier.email | Wang, JJ: junwen@hku.hk | en_US |
dc.identifier.authority | Wong, MP=rp00348 | en_US |
dc.identifier.authority | Lam, TW=rp00135 | en_US |
dc.identifier.authority | Wang, JJ=rp00280 | en_US |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1093/bioinformatics/btu338 | en_US |
dc.identifier.pmid | 24833803 | - |
dc.identifier.scopus | eid_2-s2.0-84907027434 | - |
dc.identifier.hkuros | 229945 | en_US |
dc.identifier.isi | WOS:000342912400057 | - |
dc.identifier.issnl | 1367-4803 | - |