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Book Chapter: Assessment of Mapping and SNP-Detection Algorithms for Next-Generation Sequencing Data in Cancer Genomics

TitleAssessment of Mapping and SNP-Detection Algorithms for Next-Generation Sequencing Data in Cancer Genomics
Authors
Issue Date2013
PublisherSpringer
Citation
Assessment of Mapping and SNP-Detection Algorithms for Next-Generation Sequencing Data in Cancer Genomics. In Wu, W & Choudhry, H (Eds.), Next generation sequencing in cancer research. Volume 1, Decoding the cancer genome, p. 301-317. New York: Springer, 2013 How to Cite?
AbstractThe rapid development of next-generation sequencing (NGS) technology provides a new chance to extend the scale and resolution of genomic research. How to efficiently map millions of short reads to the reference genome and how to make accurate variant call are two major challenges in NGS analysis. In this chapter, we review current software for aligning short reads and detecting single-nucleotide polymorphisms (SNPs) and extensively evaluate their performance on normal and cancer samples from the Cancer Genome Atlas project and trio’s data from the 1000 Genomes Project. We find that Burrows–Wheeler transform-based aligners are proven to be the most suitable for Illumina platform, and NovoalignCS shows the best overall performance for SOLiD data. We also demonstrate FaSD as the most reliable SNP caller compared with several state-of-the-art programs. Furthermore, NGS shows significantly lower coverage and poorer SNP-calling performance in the CpG island, promoter, and 5′UTR regions of the human genome. We show that both high GC-content and low repetitive elements are the causes of lower coverage in the promoter regions.
Persistent Identifierhttp://hdl.handle.net/10722/187486
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWang, Wen_US
dc.contributor.authorXu, Fen_US
dc.contributor.authorWang, JJen_US
dc.date.accessioned2013-08-20T12:52:20Z-
dc.date.available2013-08-20T12:52:20Z-
dc.date.issued2013en_US
dc.identifier.citationAssessment of Mapping and SNP-Detection Algorithms for Next-Generation Sequencing Data in Cancer Genomics. In Wu, W & Choudhry, H (Eds.), Next generation sequencing in cancer research. Volume 1, Decoding the cancer genome, p. 301-317. New York: Springer, 2013en_US
dc.identifier.isbn9781461476443en_US
dc.identifier.urihttp://hdl.handle.net/10722/187486-
dc.description.abstractThe rapid development of next-generation sequencing (NGS) technology provides a new chance to extend the scale and resolution of genomic research. How to efficiently map millions of short reads to the reference genome and how to make accurate variant call are two major challenges in NGS analysis. In this chapter, we review current software for aligning short reads and detecting single-nucleotide polymorphisms (SNPs) and extensively evaluate their performance on normal and cancer samples from the Cancer Genome Atlas project and trio’s data from the 1000 Genomes Project. We find that Burrows–Wheeler transform-based aligners are proven to be the most suitable for Illumina platform, and NovoalignCS shows the best overall performance for SOLiD data. We also demonstrate FaSD as the most reliable SNP caller compared with several state-of-the-art programs. Furthermore, NGS shows significantly lower coverage and poorer SNP-calling performance in the CpG island, promoter, and 5′UTR regions of the human genome. We show that both high GC-content and low repetitive elements are the causes of lower coverage in the promoter regions.-
dc.languageengen_US
dc.publisherSpringeren_US
dc.relation.ispartofNext generation sequencing in cancer research. Volume 1, Decoding the cancer genomeen_US
dc.titleAssessment of Mapping and SNP-Detection Algorithms for Next-Generation Sequencing Data in Cancer Genomicsen_US
dc.typeBook_Chapteren_US
dc.identifier.emailWang, JJ: junwen@hku.hken_US
dc.identifier.authorityWang, JJ=rp00280en_US
dc.identifier.doi10.1007/978-1-4614-7645-0_15-
dc.identifier.hkuros220851en_US
dc.identifier.spage301en_US
dc.identifier.epage317en_US
dc.publisher.placeNew Yorken_US

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