publication . Preprint . Other literature type . Article . 2014

A comparison of peak callers used for DNase-Seq data.

Mikhail Spivakov; Hashem Koohy; Hubbard TJP;
Open Access English
  • Published: 08 May 2014
  • Publisher: Cold Spring Harbor Laboratory
  • Country: United Kingdom
Abstract
<jats:p>Genome-wide profiling of open chromatin regions using DNase I and high-throughput sequencing (DNase- seq) is an increasingly popular approach for finding and studying regulatory elements. A variety of algorithms have been developed to identify regions of open chromatin from raw sequence-tag data, which has motivated us to assess and compare their performance. In this study, four published, publicly available peak calling algorithms used for DNase-seq data analysis (F-seq, Hotspot, MACS and ZINBA) are assessed at a range of signal thresholds on two published DNase-seq datasets for three cell types. The results were benchmarked against an independent datas...
Subjects
Medical Subject Headings: genetic processes
free text keywords: Medicine, R, Science, Q, Research Article, Biology and Life Sciences, Computational Biology, Epigenomics, Genome Analysis, Genetics, Gene Expression, Gene Regulation, Genomics, Functional Genomics, Molecular Genetics, Computer and Information Sciences, Physical Sciences, Mathematics, Applied Mathematics, Algorithms, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine
Funded by
WT
Project
  • Funder: Wellcome Trust (WT)
,
WT| Wellcome Trust Sanger Institute - generic account for deposition of all core- funded research papers
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 098051
  • Funding stream: Cellular and Molecular Neuroscience
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29 references, page 1 of 2

1 Kim TH, Ren B (2006) Genome-wide analysis of protein-DNA interactions. Annual review of genomics and human genetics 7: 81–102.

2 Tong Y, Falk J (2009) Genome-wide analysis for protein-DNA interaction: ChIP-chip. Methods in molecular biology (Clifton, NJ) 590: 235–251.

3 ENCODE Project Consortium (2012) Bernstein BE, Birney E, Dunham I, Green ED, et al (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 488: 57–74.22832584 [PubMed]

4 Crawford GE, Holt IE, Whittle J, Webb BD, Tai D, et al (2006) Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome research 16: 123–131.16344561 [OpenAIRE] [PubMed]

5 Song L, Zhang Z, Grasfeder LL, Boyle AP, Giresi PG, et al (2011) Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome research 21: 1757–1767.21750106 [OpenAIRE] [PubMed]

6 Zeng W, Mortazavi A (2012) Technical considerations for functional sequencing assays. Nature Immunology 13: 802–807.22910383 [OpenAIRE] [PubMed]

7 John S, Sabo PJ, Thurman RE, Sung MH, Biddie SC, et al. (2011) Chromatin accessibility predetermines glucocorticoid receptor binding patterns. Nature genetics: 1–7.

8 Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, et al (2012) The accessible chromatin landscape of the human genome. Nature 489: 75–82.22955617 [OpenAIRE] [PubMed]

9 Pepke S, Wold B, Mortazavi A (2009) Computation for ChIP-seq and RNA-seq studies. Nature Methods 6: S22–32.19844228 [OpenAIRE] [PubMed]

10 Kim H, Kim J, Selby H, Gao D, Tong T, et al (2011) A short survey of computational analysis methods in analysing ChIP-seq data. Human genomics 5: 117–123.21296745 [OpenAIRE] [PubMed]

11 Szalkowski AM, Schmid CD (2011) Rapid innovation in ChIP-seq peak-callin g algorithms is out-distancing benchmarking efforts. Briefings in Bioinformatics 12: 626–633.21059603 [PubMed]

12 Rye MB, Sætrom P, Drabløs F (2011) A manually curated ChIP-seq benchmark demonstrates room for improvement in current peak-finder programs. Nucleic Acids Research 39: e25.21113027 [OpenAIRE] [PubMed]

13 Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biology 9: R137.18798982 [OpenAIRE] [PubMed]

14 Ramagopalan SV, Heger A, Berlanga AJ, Maugeri NJ, Lincoln MR, et al (2010) A ChIP-seq defined genome-wide map of vitamin D receptor binding: associations with disease and evolution. Genome research 20: 1352–1360.20736230 [OpenAIRE] [PubMed]

15 Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, et al (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome research 22: 1813–1831.22955991 [OpenAIRE] [PubMed]

29 references, page 1 of 2
Abstract
<jats:p>Genome-wide profiling of open chromatin regions using DNase I and high-throughput sequencing (DNase- seq) is an increasingly popular approach for finding and studying regulatory elements. A variety of algorithms have been developed to identify regions of open chromatin from raw sequence-tag data, which has motivated us to assess and compare their performance. In this study, four published, publicly available peak calling algorithms used for DNase-seq data analysis (F-seq, Hotspot, MACS and ZINBA) are assessed at a range of signal thresholds on two published DNase-seq datasets for three cell types. The results were benchmarked against an independent datas...
Subjects
Medical Subject Headings: genetic processes
free text keywords: Medicine, R, Science, Q, Research Article, Biology and Life Sciences, Computational Biology, Epigenomics, Genome Analysis, Genetics, Gene Expression, Gene Regulation, Genomics, Functional Genomics, Molecular Genetics, Computer and Information Sciences, Physical Sciences, Mathematics, Applied Mathematics, Algorithms, General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine
Funded by
WT
Project
  • Funder: Wellcome Trust (WT)
,
WT| Wellcome Trust Sanger Institute - generic account for deposition of all core- funded research papers
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 098051
  • Funding stream: Cellular and Molecular Neuroscience
Download fromView all 8 versions
bioRxiv
Preprint . 2014
Provider: bioRxiv
PLoS ONE
Article . 2014
PLoS ONE
Article . 2014
Provider: Crossref
PLoS ONE
Article
Provider: UnpayWall
29 references, page 1 of 2

1 Kim TH, Ren B (2006) Genome-wide analysis of protein-DNA interactions. Annual review of genomics and human genetics 7: 81–102.

2 Tong Y, Falk J (2009) Genome-wide analysis for protein-DNA interaction: ChIP-chip. Methods in molecular biology (Clifton, NJ) 590: 235–251.

3 ENCODE Project Consortium (2012) Bernstein BE, Birney E, Dunham I, Green ED, et al (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 488: 57–74.22832584 [PubMed]

4 Crawford GE, Holt IE, Whittle J, Webb BD, Tai D, et al (2006) Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome research 16: 123–131.16344561 [OpenAIRE] [PubMed]

5 Song L, Zhang Z, Grasfeder LL, Boyle AP, Giresi PG, et al (2011) Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome research 21: 1757–1767.21750106 [OpenAIRE] [PubMed]

6 Zeng W, Mortazavi A (2012) Technical considerations for functional sequencing assays. Nature Immunology 13: 802–807.22910383 [OpenAIRE] [PubMed]

7 John S, Sabo PJ, Thurman RE, Sung MH, Biddie SC, et al. (2011) Chromatin accessibility predetermines glucocorticoid receptor binding patterns. Nature genetics: 1–7.

8 Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, et al (2012) The accessible chromatin landscape of the human genome. Nature 489: 75–82.22955617 [OpenAIRE] [PubMed]

9 Pepke S, Wold B, Mortazavi A (2009) Computation for ChIP-seq and RNA-seq studies. Nature Methods 6: S22–32.19844228 [OpenAIRE] [PubMed]

10 Kim H, Kim J, Selby H, Gao D, Tong T, et al (2011) A short survey of computational analysis methods in analysing ChIP-seq data. Human genomics 5: 117–123.21296745 [OpenAIRE] [PubMed]

11 Szalkowski AM, Schmid CD (2011) Rapid innovation in ChIP-seq peak-callin g algorithms is out-distancing benchmarking efforts. Briefings in Bioinformatics 12: 626–633.21059603 [PubMed]

12 Rye MB, Sætrom P, Drabløs F (2011) A manually curated ChIP-seq benchmark demonstrates room for improvement in current peak-finder programs. Nucleic Acids Research 39: e25.21113027 [OpenAIRE] [PubMed]

13 Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biology 9: R137.18798982 [OpenAIRE] [PubMed]

14 Ramagopalan SV, Heger A, Berlanga AJ, Maugeri NJ, Lincoln MR, et al (2010) A ChIP-seq defined genome-wide map of vitamin D receptor binding: associations with disease and evolution. Genome research 20: 1352–1360.20736230 [OpenAIRE] [PubMed]

15 Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, et al (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome research 22: 1813–1831.22955991 [OpenAIRE] [PubMed]

29 references, page 1 of 2
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