The poisson margin test for normalization-free significance analysis of NGS data

Journal of Computational Biology

Volumn 18, Issue 3, 2011, Pages 391-400

  Kowalczyk, Adam ^a   Bedo, Justin ^a   Conway, Thomas ^a   Beresford Smith, Bryan ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Algorithms; computational molecular biology

Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; CONFERENCE PAPER; DNA SEQUENCE; GENOMICS; METHODOLOGY; MOLECULAR GENETICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; GENOMICS; MOLECULAR SEQUENCE ANNOTATION; SEQUENCE ANALYSIS, DNA;

EID: 79952404725     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2010.0272     Document Type: Conference Paper

References (16)

1. Abeel, T., Van de Peer, Y., and Saeys, Y. 2009. Toward a gold standard for promoter prediction evaluation. Bioinformatics 25, i313-i320.
2. Baggerly, K.A., Deng, L., Morris, J.S., et al. 2003. Differential expression in SAGE: Accounting for normal betweenlibrary variation. Bioinformatics 19, 1477-1483. (Pubitemid 37038863)
3. Bedo, J., MacIntyre, G., Haviv, I., et al. 2009. Simple SVM based whole-genome segmentation. Available at: Http:// dx.doi.org/10.1038/npre.2009. 3811.1. Accessed December 1, 2010.
4. Bloushtain-Qimron, N., Yao, J., and Snyder, E. 2008. Cell type-specific DNA methylation patterns in the human breast. Proc. Natl. Acad. Sci. USA 105, 14076-14081.
5. Ji, H., Jiang, H., Ma, W., et al. 2008. An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat. Biotechnol. 26, 1293-1300.
6. Keeping, E. 1995. Introduction to Statistical Infernce. [Reprint of 1962 edition by D. Van Nostrand Co.]. Dover, New York.
7. Kowalczyk, A. 2009. Some formal results for significance of short read concentrations. Available at: Http://www .genomics.csse.unimelb.edu.au/ shortreadtheory. Accessed December 1, 2010.
8. Kowalczyk, A., Bedo, J., Conway, T., et al. 2009. Poisson margin test for normalisation free significance analysis of NGS data [Supplementary Materials]. Available at: Http://www.genomics.csse.unimelb.edu.au/peakfiltsup. Accessed December 1, 2010.
9. Nix, D., Courdy, S., and Boucher, K. 2008. Empirical methods for controlling false positives and estimating confidence in ChIP-seq peaks. BMC Bioinformatics 9, 523.
10. Pepke, S., Wold, B., and Mortazavi, A. 2009. Computation for ChIP-seq and RNA-seq studies. Nat. Methods 6, S22-S32.
11. Robertson, G., Hirst, M., Bainbridge, M., et al. 2007. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651-657. (Pubitemid 47202477)
12. Robinson, M., and Smyth, G. 2007. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887. (Pubitemid 350162894)
13. Rozowsky, J., Euskirchen, G., Auerbach, R., et al. 2009. Peakseq enables systematic scoring of chip-seq experiments relative to controls. Nat. Biotechnol. 27, 66-75.
14. Sonnenburg, S., Zien, A., and Ratsch, G. 2006. Arts: Accurate recognition of transcription starts in human. Bioinformatics 22, e423-e480.
15. Zang, C., Schones, D.E., Zeng, C., et al. 2009. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25, 1952-1958.
16. Zhang, Y., Liu, T., Meyer, C., et al. 2008. Model-based analysis of ChIP-seq (macs). Genome Biol. 9, R137+.