WaveSeq: A Novel Data-Driven Method of Detecting Histone Modification Enrichments Using Wavelets

PLoS ONE

Volumn 7, Issue 9, 2012, Pages

  Mitra, Apratim ^a   Song, Jiuzhou ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HISTONE;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; CHICKEN; CHROMATIN IMMUNOPRECIPITATION; DATA ANALYSIS; HISTONE MODIFICATION; INFORMATION PROCESSING; NONHUMAN; PROTEIN ANALYSIS; SENSITIVITY ANALYSIS; SIGNAL NOISE RATIO; STATISTICAL ANALYSIS; WAVESEQ;

ANIMALS; B-LYMPHOCYTES; BINDING SITES; CHICKENS; CHROMATIN IMMUNOPRECIPITATION; COMPUTATIONAL BIOLOGY; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HISTONES; LYMPHOCYTE ACTIVATION; PROTEIN BINDING; SENSITIVITY AND SPECIFICITY;

EID: 84867006345     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0045486     Document Type: Article

References (26)

1. Fejes AP, Robertson G, Bilenky M, Varhol R, Bainbridge M, et al. (2008) FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology. Bioinformatics 24: 1729-1730.
2. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, et al. (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9: R137.
3. Zang C, Schones DE, Zeng C, Cui K, Zhao K, et al. (2009) A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25: 1952-1958.
4. Rashid NU, Giresi PG, Ibrahim JG, Sun W, Lieb JD, (2011) ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions. Genome Biol 12: R67.
5. Mitra A, Liu G, Song J, (2009) A genome-wide analysis of array-based comparative genomic hybridization (CGH) data to detect intra-species variations and evolutionary relationships. PLoS One 4: e7978.
6. Torrence C, Compo G, (1998) A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society 79: 61-78.
7. Benjamini Y, Hochberg Y, (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57: 289-300.
8. Laajala TD, Raghav S, Tuomela S, Lahesmaa R, Aittokallio T, et al. (2009) A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments. BMC Genomics 10: 618.
9. Wilbanks EG, Facciotti MT, (2010) Evaluation of algorithm performance in ChIP-seq peak detection. PLoS One 5: e11471.
10. Jothi R, Cuddapah S, Barski A, Cui K, Zhao K, (2008) Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Res 36: 5221-5231.
11. Song Q, Smith AD, (2011) Identifying dispersed epigenomic domains from ChIP-Seq data. Bioinformatics 27: 870-871.
12. Valouev A, Johnson DS, Sundquist A, Medina C, Anton E, et al. (2008) Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods 5: 829-834.
13. Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, et al. (2007) Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448: 553-560.
14. Nix DA, Courdy SJ, Boucher KM, (2008) Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks. BMC Bioinformatics 9: 523.
15. Zhang ZD, Rozowsky J, Snyder M, Chang J, Gerstein M, (2008) Modeling ChIP sequencing in silico with applications. PLoS Comput Biol 4: e1000158.
16. Feng J, Liu T, Zhang Y (2011) Using MACS to Identify Peaks from ChIP-Seq Data. Current Protocols in Bioinformatics: John Wiley & Sons, Inc. 2.14.11-12.14.14.
17. Huang da W, Sherman BT, Lempicki RA, (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44-57.
18. Huang da W, Sherman BT, Lempicki RA, (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37: 1-13.
19. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, et al. (2003) PANTHER: a library of protein families and subfamilies indexed by function. Genome Res 13: 2129-2141.
20. Caldwell RB, Kierzek AM, Arakawa H, Bezzubov Y, Zaim J, et al. (2005) Full-length cDNAs from chicken bursal lymphocytes to facilitate gene function analysis. Genome Biol 6: R6.
21. Calnek BW, Schat KA, Ross LJ, Shek WR, Chen CL, (1984) Further characterization of Marek's disease virus-infected lymphocytes. I. In vivo infection. Int J Cancer 33: 389-398.
22. Langmead B, Trapnell C, Pop M, Salzberg SL, (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10: R25.
23. Team RDC (2008) R: A Language and Environment for Statistical Computing.
24. Fujita PA, Rhead B, Zweig AS, Hinrichs AS, Karolchik D, et al. (2010) The UCSC Genome Browser database: update 2011. Nucleic Acids Res 39: D876-882.
25. Kinsella RJ, Kahari A, Haider S, Zamora J, Proctor G, et al. (2011) Ensembl BioMarts: a hub for data retrieval across taxonomic space. Database (Oxford) 2011: bar030.
26. Flicek P, Amode MR, Barrell D, Beal K, Brent S, et al. (2011) Ensembl 2011. Nucleic Acids Res 39: D800-806.