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Article: Next generation sequencing has lower sequence coverage and poorer SNP-detection capability in the regulatory regions

TitleNext generation sequencing has lower sequence coverage and poorer SNP-detection capability in the regulatory regions
Authors
KeywordsComputational biology and bioinformatics
Gene regulation
Cancer genomics
Issue Date2011
PublisherNature Publishing Group. The Journal's web site is located at http://www.nature.com/srep/index.html
Citation
Scientific Reports, 2011, v. 1, article no. 55 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 SNP calls are two major challenges in taking full advantage of NGS. In this article, we reviewed the current software tools for mapping and SNP calling, and evaluated their performance on samples from The Cancer Genome Atlas (TCGA) project. We found that BWA and Bowtie are better than the other alignment tools in comprehensive performance for Illumina platform, while NovoalignCS showed the best overall performance for SOLiD. Furthermore, we showed that next-generation sequencing platform has significantly lower coverage and poorer SNP-calling performance in the CpG islands, promoter and 5'-UTR regions of the genome. NGS experiments targeting for these regions should have higher sequencing depth than the normal genomic region.
Persistent Identifierhttp://hdl.handle.net/10722/138955
ISSN
2015 Impact Factor: 5.228
2015 SCImago Journal Rankings: 2.073
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council781511M
778609M
N_HKU752/10
Food and Health Bureau of Hong Kong10091262
Funding Information:

Financial support was provided by Grants from the Research Grants Council (781511M, 778609M, N_HKU752/10) and Food and Health Bureau (10091262) of Hong Kong.

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorWang, Wen_HK
dc.contributor.authorZhi, Wen_HK
dc.contributor.authorLam, TWen_HK
dc.contributor.authorWang, JJen_HK
dc.date.accessioned2011-09-23T05:43:05Z-
dc.date.available2011-09-23T05:43:05Z-
dc.date.issued2011en_HK
dc.identifier.citationScientific Reports, 2011, v. 1, article no. 55en_HK
dc.identifier.issn2045-2322en_HK
dc.identifier.urihttp://hdl.handle.net/10722/138955-
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 SNP calls are two major challenges in taking full advantage of NGS. In this article, we reviewed the current software tools for mapping and SNP calling, and evaluated their performance on samples from The Cancer Genome Atlas (TCGA) project. We found that BWA and Bowtie are better than the other alignment tools in comprehensive performance for Illumina platform, while NovoalignCS showed the best overall performance for SOLiD. Furthermore, we showed that next-generation sequencing platform has significantly lower coverage and poorer SNP-calling performance in the CpG islands, promoter and 5'-UTR regions of the genome. NGS experiments targeting for these regions should have higher sequencing depth than the normal genomic region.en_HK
dc.languageengen_US
dc.publisherNature Publishing Group. The Journal's web site is located at http://www.nature.com/srep/index.html-
dc.relation.ispartofScientific Reportsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectComputational biology and bioinformatics-
dc.subjectGene regulation-
dc.subjectCancer genomics-
dc.titleNext generation sequencing has lower sequence coverage and poorer SNP-detection capability in the regulatory regionsen_HK
dc.typeArticleen_HK
dc.identifier.emailLam, TW: hresltk@hkucc.hku.hken_HK
dc.identifier.emailWang, JJ: junwen@hku.hken_HK
dc.identifier.authorityLam, TW=rp00135en_HK
dc.identifier.authorityWang, JJ=rp00280en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1038/srep00055en_HK
dc.identifier.pmid22355574-
dc.identifier.pmcidPMC3216542-
dc.identifier.scopuseid_2-s2.0-84857232194en_HK
dc.identifier.hkuros192078en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84857232194&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume1, article no. 55en_HK
dc.identifier.eissn2045-2322-
dc.identifier.isiWOS:000296050700001-
dc.publisher.placeUnited Kingdom-
dc.relation.projectA Novel Hidden Markov Model to Predict microRNAs and their Targets Simultaneously and its Application to the Epstein-Barr virus-
dc.identifier.scopusauthoridWang, J=8950599500en_HK
dc.identifier.scopusauthoridLam, TW=7202523165en_HK
dc.identifier.scopusauthoridWei, Z=53064846200en_HK
dc.identifier.scopusauthoridWang, W=55195156700en_HK
dc.identifier.citeulike9631504-

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